Beyond the Qubit

Transduction is “everybody’s problem and nobody’s problem.”That line from Vijoy Pandey, SVP/GM of Outshift by Cisco, stayed with me after Part 2 of my Beyond the Qubit interview because it points to one of the most important value layers in quantum.In this episode, I explore why connecting quantum computers may become just as important as building them. In classical networking, information can be inspected, routed, and forwarded. Quantum information is different. If you inspect the quantum state, you risk destroying the information you are trying to preserve.This episode is for investors, founders, and anyone trying to understand the infrastructure that quantum computing will need to scale. The challenge is not only building better qubits. It is creating the interfaces, switches, and protocols that allow different quantum systems to communicate.That is where transduction becomes strategically important. It is the translation layer between quantum computers and quantum networks. Without it, the industry risks creating isolated compute islands where every system speaks its own language.Cisco’s approach is interesting because it is not built around betting on one winning quantum modality. Instead, the focus is on building a network layer that can connect different architectures. If quantum becomes a distributed computing platform, the companies controlling the interfaces and connectivity layers could become important control points.💡 In this episode, we cover:Why quantum networks cannot copy classical internet architectureWhy transduction is the interface problem between quantum computers and networksHow quantum switches differ from classical switchesWhy different quantum modalities create a connectivity challengeHow Cisco’s universal quantum switch approaches translation between encodingsWhy ecosystem collaboration matters for quantum networkingWhy the network layer could become a valuable control pointWhat investors should watch as quantum infrastructure developsChapters00:00 Why transduction matters in quantum networking01:35 Why quantum needs a network layer02:39 The NIC card analogy for quantum03:24 Why transduction is harder than classical networking04:51 Why quantum networks cannot inspect information07:05 Building a universal quantum switch08:45 Translating different quantum encodings10:48 Why ecosystem collaboration matters12:47 Keeping the network simple and complexity at the edge14:48 Why transduction could become a strategic control pointShare this episode with someone investing in or building in quantum, and subscribe or follow Beyond the Qubit for more conversations on quantum technology, markets, and investing.📌 Disclaimer:This is not investment advice.This post is shared on a personal basis and I do not represent any company.

The internet was built by reading packet headers.Quantum networks cannot do that.In this episode, I explore my biggest takeaways from Part 1 of my Beyond the Qubit interview with Vijoy Pandey, head of Outshift at Cisco. The key difference is simple but profound: classical networks scale by inspecting and forwarding information, while quantum networks have to preserve information they cannot directly inspect.This episode is for investors, founders, and anyone trying to understand what infrastructure quantum computing will need beyond the QPU itself. If quantum systems are going to scale beyond isolated machines, they will need a network layer that can connect processors, sensors, and resources while preserving fragile quantum states.Cisco’s approach is interesting because the company is not trying to build the quantum computer itself. It is exploring the networking layer around quantum systems, using photonics, telecom frequencies, room-temperature components, and existing fiber infrastructure. The goal is not to recreate the classical internet, but to build the architecture needed for quantum scale.The strategic question is not only who builds the best qubits.It is also:Who builds the network that connects them?💡 In this episode, we cover:Why quantum networks cannot simply copy classical internet architectureWhy quantum information cannot be inspected like classical packetsHow quantum switches and entanglement sources could enable scalingWhy networking between QPUs may become a major bottleneckWhy Cisco is focusing on the infrastructure layer around quantum computersHow photonics and existing fiber infrastructure could improve deployabilityWhy quantum networks may also connect sensors and shared resourcesWhat investors should watch in the quantum networking stackChapters00:00 Why Cisco is exploring quantum networking00:50 Why quantum needs a network layer03:22 Quantum clusters and the need for connectivity04:47 What quantum networking actually means05:23 The quantum switch challenge08:01 Why classical networking cannot be copied10:03 Why quantum networks cannot inspect information12:29 Building networks without destroying quantum states14:42 Why Cisco focuses on photonics and fiber17:39 Connecting quantum computers and sensors27:14 The current state of quantum networkingShare this episode with someone investing in or building in quantum, and subscribe or follow Beyond the Qubit for more conversations on quantum technology, markets, and investing.📌 Disclaimer: This is not investment advice. This post is shared on a personal basis and I do not represent any company.

Quantum will not scale by optimizing hardware, software, and testing independently.The winners will have to make the entire system work.In this episode, I continue my conversation with Adriaan van Rol from OrangeQS to explore why quantum testing may become one of the most important infrastructure layers in the industry. OrangeQS looks like a hardware company at first. Its OrangeQS MAX platform combines cryogenics, electronics, cabling, control systems, and data acquisition to test quantum chips.But one of the more interesting parts of the system is the software layer underneath: OrangeQS Juice. What started as the software environment around OrangeQS MAX evolved into a broader platform for multi-user workflows, instrument control, structured data, and scalable test operations.This episode is for investors, founders, and anyone trying to understand what it takes for quantum to move from research toward manufacturing. At scale, testing is not just about measuring a chip. It is about turning measurements into diagnosis, validated insights, and better manufacturing decisions.That is where the strategic opportunity becomes interesting. The potential moat is not only the hardware or the software individually. It is the integrated system: hardware + software + data + domain knowledge. Because in quantum manufacturing, the ability to create a faster feedback loop may become just as important as the ability to build the device itself.💡 In this episode, we cover:Why quantum testing needs to evolve beyond laboratory workflowsHow OrangeQS MAX combines hardware infrastructure for quantum chip testingWhy OrangeQS built Juice instead of relying on simple lab scriptsHow software enables repeatable, traceable, multi-user testingWhy testing becomes inseparable from manufacturing at scaleHow measurement data can become manufacturing insightWhy integrated systems may create stronger moats than individual productsThe importance of data governance in quantum manufacturingChapters00:00 Why quantum testing needs a complete system01:50 What OrangeQS is building for quantum chip testing08:10 Why testing and manufacturing need feedback loops14:40 Turning test data into manufacturing learning29:33 Why OrangeQS Juice became a hidden gem30:54 Moving from lab scripts to scalable workflows35:00 Hardware, software, data and domain knowledge together42:15 The future of quantum manufacturing infrastructureShare this episode with someone investing in or building in quantum, and subscribe or follow Beyond the Qubit for more conversations on quantum technology, markets, and investing.📌 Disclaimer:This is not investment advice.This post is shared on a personal basis and I do not represent any company.

Why is building a great deep-tech product only half the battle?In this episode, I explore one of my biggest takeaways from my conversation with Adriaan Rol from OrangeQS. OrangeQS is building test solutions for quantum chips, but the challenge is not only technical. When a product costs millions, the company also has to solve a different problem: how customers actually buy.This episode is for investors, founders, and anyone interested in deep-tech commercialization. Adriaan shares an honest lesson from OrangeQS’s journey: they underestimated not the physics or engineering, but the complexity of corporate decision-making, CapEx budgets, procurement processes, and internal risk.A strong technical story is not enough. A great product is not enough. At some point, deep-tech companies need to make the buying decision easier. That means understanding the customer’s internal process, creating the right milestones, reducing risk step by step, and turning technical interest into a purchase order.The lesson is bigger than quantum. In deep tech, product-market fit is only part of the equation. Companies also need procurement-market fit.💡 In this episode, we cover:Why deep-tech companies need more than product-market fitWhy expensive hardware requires a different sales approachHow OrangeQS learned to navigate CapEx decisions and procurementWhy technical validation does not automatically lead to purchasesHow partnership programs can reduce customer riskWhy customers need a buying process they can defend internallyHow deep-tech founders can turn interest into purchase ordersWhy adoption is often as difficult as the technology itselfChapters00:00 Why deep tech needs procurement-market fit08:34 Why million-euro hardware changes the buying process17:32 Building a partnership program to reduce risk22:19 The hidden challenge of CapEx decisions40:29 Why procurement can make or break adoptionShare this episode with someone building or investing in deep tech, and subscribe or follow Beyond the Qubit for more conversations on quantum technology, markets, and investing.📌 Disclaimer:This is not investment advice.This post is shared on a personal basis and I do not represent any company.

When quantum computers scale out, the algorithm has to know where the weak links are.In this episode, I continue my conversation with Enrique Solano from Kipu Quantum to explore why modular quantum computing is not only a hardware challenge. Most people think modularity means more chips, more qubits, and more scale. But Enrique highlights a deeper issue: once quantum computers become distributed systems, the algorithm has to understand the physical reality of the machine.This episode is for investors, founders, and anyone trying to understand where quantum software value may emerge. When computation moves across multiple chips, not every connection is equal. Some gates may have very high fidelity inside a chip, while connections between chips can introduce weaker operations. Small differences in fidelity can determine whether an algorithm survives long enough to become useful.That is where Kipu’s hardware-aware approach becomes interesting. The software cannot treat every quantum backend as identical. It needs to understand connectivity, topology, fidelities, and the weak points of the architecture. In quantum, scaling hardware creates a new software problem. The winning software layer may be the one that understands the machine well enough to extract value from its imperfections.💡 In this episode, we cover:Why modular quantum computing changes the software challengeWhy more qubits do not automatically mean more useful computationHow inter-chip connections can create new fidelity bottlenecksWhy algorithms need to understand quantum hardware topologyWhy hardware-aware software may matter more than hardware-agnostic approaches todayHow Kipu thinks about extracting value from imperfect quantum systemsWhy scaling quantum hardware creates new algorithmic challengesWhat investors should watch in quantum software companiesChapters00:00 Why Kipu Quantum focuses on hardware-aware software01:29 Why algorithms must adapt as hardware changes03:26 Why architecture matters in quantum systems06:46 Fidelity limits inside quantum architectures08:03 Why modularity changes the algorithm09:01 Building quantum systems beyond single chips15:04 Why inter-chip connections create new challenges15:21 Why small fidelity differences matter16:30 Why algorithms must know the machineShare this episode with someone investing in or building in quantum, and subscribe or follow Beyond the Qubit for more conversations on quantum technology, markets, and investing.📌 Disclaimer:This is not investment advice.This post is shared on a personal basis and I do not represent any company.

Much of quantum software is built around one assumption: better hardware will eventually solve the problem.Kipu Quantum takes a different view.In this episode, I explore my biggest takeaways from Part 1 of my Beyond the Qubit interview with Enrique Solano, CEO of Kipu Quantum. The traditional quantum narrative is familiar: today’s machines are too noisy, qubits are not good enough, and real value will only arrive with fault-tolerant quantum computers. Enrique challenges that mindset.The point is not that today’s hardware is perfect. It clearly is not. The point is different: accept the reality conditions of the machines that exist today and build software that works within those constraints. Instead of waiting for perfect hardware, Kipu studies the architecture, connectivity, fidelities, and limitations of current quantum systems, then designs algorithms that can extract useful performance from them.This episode is for investors, founders, and anyone trying to understand where quantum value may emerge. Kipu’s bet is that useful quantum computing may arrive before fault tolerance, by finding the corners where today’s imperfect machines can already create value.The question is not only who builds the best quantum computer.It is also:Who can make today’s quantum computers useful?💡 In this episode, we cover:Why Kipu Quantum challenges the idea that hardware must come firstWhy current quantum limitations should become design constraintsHow hardware-aware algorithms can extract more value from existing machinesWhy Kipu focuses on industrial problems and customer needsHow algorithm compression can reduce the requirements for quantum hardwareWhy hybrid quantum-classical workflows may matter before fault toleranceWhy quantum software companies may create value earlier than expectedWhat investors should watch in quantum software businessesChapters00:00 Why Kipu Quantum matters for investors01:10 Building value with today’s quantum hardware03:33 Why Kipu does not wait for fault tolerance04:15 Starting from hardware, not use cases05:59 Finding industrial problems quantum can solve06:59 Customer applications and commercial validation08:56 Why current hardware is still worth using28:57 Why Kipu rejects blaming the hardware31:27 Accepting reality conditions in quantum38:05 Hardware-aware algorithms and compression50:53 Why software must adapt as hardware improvesShare this episode with someone investing in or building in quantum, and subscribe or follow Beyond the Qubit for more conversations on quantum technology, markets, and investing.📌 Disclaimers:This is not investment advice.This post is shared on a personal basis and I do not represent any company.

In photonic quantum computing, the cheapest error is the one you reduce before it compounds.In this episode, I continue my deep dive with Jelmer Renema from QuiX Quantum to explore one of the most important challenges in photonic quantum computing: improving photon quality before errors become more expensive later.At first, I thought QuiX’s photon distillation work was mainly about reducing loss. Jelmer corrected that. The deeper challenge is indistinguishability. Photons need to be identical enough that the system cannot tell them apart: the same timing, colour, polarization, and quantum state. That turns a physics challenge into an economic one.This episode is for investors, founders, and anyone trying to understand what it takes to make photonic quantum computing commercially viable. Better photons early can mean more useful resources later: more computing power from the same input photons, less correction overhead, less hardware complexity, and potentially lower costs.That is why QuiX is focusing on the difficult problems early: photon quality, error reduction, feed-forward, and system integration. The key investor question is not only whether photonic quantum computers can work. It is whether they can scale with an economic model that makes sense.💡 In this episode, we cover:Why photon quality matters more than just photon quantityWhy indistinguishability is the key challenge behind photon errorsHow photon distillation can reduce overhead before errors compoundWhy better physics can translate into better economicsHow error reduction affects hardware requirements and scaling costsWhy QuiX is focused on difficult engineering problems earlyWhat feed-forward and system integration mean for photonic quantum systemsThe investor question: can photonic quantum computing scale economically?Chapters00:00 QuiX’s latest progress in photonic quantum computing09:40 Why photon quality matters for scaling10:09 Loss versus indistinguishability explained14:51 How photon distillation reduces overhead17:30 Why better photons improve quantum economics20:30 The challenge of building scalable photonic systems22:00 Delivering photonic quantum computers to customers24:00 The future of photonic quantum computingShare this episode with someone investing in or building in quantum, and subscribe or follow Beyond the Qubit for more conversations on quantum technology, markets, and investing.📌 Disclaimer:This post is shared on a personal basis and I do not represent any company.

ASML taught investors a powerful lesson: the real moat can be systems integration.Quantum may follow the same path.In this episode, I explore my biggest takeaways from my Beyond the Qubit interview with Jelmer Renema, CEO of QuiX Quantum. QuiX started by building photonic quantum processors, but the company made a strategic decision to move from components toward complete photonic quantum computer systems.That shift matters because quantum computing may not be won by the company with one perfect component. It may be won by the company that can make many difficult technologies work together. Photon sources, photonic chips, detectors, feed-forward electronics, software, packaging, and loss management all have to operate as one reliable machine.This episode is for investors, founders, and anyone trying to understand where value may build across the quantum stack. QuiX’s opportunity is not only photonics. It is the operational learning curve of turning photonic technology into a deployable quantum system.Because a quantum computer is not a component. It is a system of systems.💡 In this episode, we cover:Why QuiX moved from photonic components to complete quantum systemsWhy systems integration could become a major quantum moatHow photonic quantum computing differs from other approachesWhy low-loss photonic chips are strategically importantWhy packaging, detectors, software, and control systems matter togetherHow early customers help deep tech companies mature fasterWhy investors should look beyond individual componentsChapters00:00 Introduction to QuiX Quantum and photonic computing01:56 Why silicon nitride matters for photonic quantum computing04:18 From photonic processors to quantum systems10:00 Why integration is the real challenge in quantum11:51 Building the full photonic quantum stack15:38 Why early customers accelerate deep tech learning17:17 Why vertical integration could become a quantum advantageShare this episode with someone investing in or building in quantum, and subscribe or follow Beyond the Qubit for more conversations on quantum technology, markets, and investing.📌 Disclaimer: This post is shared on a personal basis and I do not represent any company.

Quantum has many technical claims. But it does not yet have enough comparable evidence.In this episode, I explore one of my biggest takeaways from my Beyond the Qubit interview with Roytman Piccoli, CEO of SoftQuantus. The question that stood out to me was not only “what does a quantum provider claim?” but “what can an enterprise actually verify?”Quantum hardware is different from classical cloud infrastructure. Performance changes over time. Calibration changes. Noise changes. Queue conditions change. The best backend yesterday may not be the best backend today. That creates a need for independent measurement, benchmarking, and trust.This episode is for investors, founders, and anyone trying to understand what enterprise adoption of quantum may require. SoftQuantus is building around a control and orchestration layer designed to bring execution, telemetry, reliability scoring, backend selection, and traceability into a fragmented quantum ecosystem.That matters because serious markets need trusted measurement. Semiconductors needed metrology. Cloud needed observability. Cybersecurity needed certifications. Quantum will need its own trust layer. The investor question is not only who builds the best quantum hardware, but who helps enterprises know which systems are reliable, compatible, and ready for real workloads.💡 In this episode, we cover:Why quantum needs independent benchmarking and verificationWhy provider claims are not enough for enterprise adoptionHow SoftQuantus approaches quantum orchestration and controlWhat QCOS is designed to solve across fragmented quantum platformsWhy telemetry, traceability, and reliability scoring matterHow quantum backends differ across hardware modalitiesWhy certification and compatibility layers could become strategically importantWhy trust infrastructure may become a valuable layer in the quantum stackChapters00:00 Why quantum needs a trust layer00:48 SoftQuantus and the quantum control layer02:00 Why quantum needs orchestration and traceability04:20 Building a unified quantum operating system08:30 Connecting quantum systems with enterprise workflows11:24 Why benchmarking quantum hardware is difficult17:19 Creating trust through measurement and scoring22:00 Why enterprises need vendor comparison27:30 The future of quantum interoperabilityShare this episode with someone investing in or building in quantum, and subscribe or follow Beyond the Qubit for more conversations on quantum technology, markets, and investing.📌 Disclaimer:This post is shared on a personal basis and I do not represent any company.

I used to think cryogenic cooling was mainly a support layer in quantum. After my interview with Alexander Regnat, I now think it may be one of the strategic bottlenecks.In this episode, Henny Crauwels asked me what actually changed in my thinking after the Kiutra interview. My honest answer is that I underestimated the cooling layer. For superconducting and spin qubits, cryogenic cooling is not optional. It is what makes the quantum effects usable in the first place. But the more important insight is that cooling is both an enabler and a bottleneck. It enables the qubit, but it can also limit how fast the industry learns, scales, and deploys.This episode is for investors, founders, and anyone trying to understand what really constrains the quantum stack. Three things changed my view. First, testing speed. If cooling, loading, testing, and reloading takes many hours or even a day, the learning cycle slows down. Second, the heat budget. As qubit counts scale, the control lines, wiring, amplifiers, and electronics all compete for tiny millikelvin cooling budgets measured in microwatts. Third, helium-3. Traditional dilution refrigerators depend on a scarce isotope with concentrated supply, which turns cooling into not only an engineering issue but also a supply chain and sovereignty issue.That is why Kiutra became more interesting to me during the interview. Not just as a cryogenic cooling company, but as a company attacking hidden bottlenecks in the quantum stack: testing speed, heat budget, helium-3 dependency, and modular cooling architecture. The broader investor lesson is simple. Quantum is not only a qubit race. It is also an infrastructure race. And the qubit roadmap only matters if the infrastructure roadmap can keep up.💡 In this episode, we cover:Why cryogenic cooling is more strategic than I first thoughtWhy testing speed can become a major bottleneck in quantum hardwareWhy faster cooling and reloading can accelerate the learning cycleWhy the heat budget becomes critical as systems scaleWhy microwatts matter more than most people realizeWhy helium-3 creates a supply chain and sovereignty questionWhy cooling also matters for other modalities beyond superconducting and spin qubitsWhy modular cooling architecture could matter as systems become larger and more deployableChapters00:00 What changed in my thinking after Kiutra00:35 Why cryogenic cooling is essential00:59 Why testing speed is a real bottleneck01:52 Why the heat budget matters so much03:10 Why cooling also matters beyond superconducting qubits04:17 Helium-3 scarcity and sovereignty risk05:17 How Kiutra’s magnetic cooling works06:43 Why faster testing changes the learning cycle07:43 Heat budget explained in simple terms10:43 Where helium-free cooling really startsShare this episode with someone investing in or building in quantum, and subscribe or follow Beyond the Qubit for more conversations on quantum technology, markets, and investing.📌 Disclaimer:This post is shared on a personal basis and I do not represent any company.

What if quantum does not scale by building bigger fridges, but by redesigning the cooling architecture itself?In this episode, I unpack one of my biggest takeaways from Part 2 of my Beyond the Qubit interview with Alexander Regnat, co-founder and CEO of kiutra. Most people still picture quantum computing as a chip inside a giant cryogenic chandelier. The default assumption is simple: if the quantum computer gets bigger, the fridge gets bigger. But that may be the wrong mental model.This episode is for investors, founders, and anyone trying to understand what it will take to move quantum from lab systems to deployable infrastructure. Today, many quantum setups are still highly integrated lab machines, where the cooling system, wiring, electronics, and quantum payload are built into one large cryogenic setup. That works in the lab. But as systems grow, it becomes harder to ship, install, upgrade, and scale. At some point, just building a bigger fridge may stop being the right answer.That is why kiutra’s roadmap caught my attention. L-Type Rapid addresses today’s testing and qualification bottleneck. But the bigger architectural bet is X-Type. The idea is to separate the cooling infrastructure from the quantum payload, move beyond one monolithic cryostat, and make scaling more modular. Add cooling modules instead of replacing the whole system. Less like bespoke lab equipment. More like infrastructure. That is the bigger investor lesson. The question is not only who can build more qubits. It is also who can build the infrastructure layer that turns qubit roadmaps into deployable systems.💡 In this episode, we cover:Why bigger fridges may be the wrong scaling model for quantumWhy cooling architecture matters as systems become larger and more complexHow kiutra’s X-Type changes the mental model of cryogenic infrastructureWhy separating cooling from the quantum payload could improve deployment and upgradesWhy future systems will need cooling at multiple temperature stages, not only one extreme cold pointHow modular cooling could scale from microwatts to hundreds of microwatts and beyondWhy shipping, installation, and integration may become real bottlenecksWhy deployable infrastructure may become just as important as the qubit roadmap itselfChapters00:00 Magnetocaloric cooling basics20:43 Why X-Type is a different architecture21:20 Why integrated cryostats do not scale well22:48 Separating cooling from the quantum payload24:14 Why modular cooling matters25:20 Zero helium-3 and 20 millikelvin26:00 Scaling cooling power with modules42:46 From 1 microwatt to 20 microwatts and beyondShare this episode with someone investing in or building in quantum, and subscribe or follow Beyond the Qubit for more conversations on quantum technology, markets, and investing.📌 Disclaimers:This is not investment advice.This post is shared on a personal basis and I do not represent any company.

What if the next bottleneck in quantum is not qubit count, but the speed of learning around the hardware?In this episode, I unpack one of my biggest takeaways from Part 1 of my Beyond the Qubit interview with Alexander Regnat, co-founder and CEO of kiutra. Most quantum discussions still focus on the visible roadmap: more qubits, higher fidelity, better error correction, logical qubits, and fault tolerance.All of that matters. But scaling quantum hardware also requires something less glamorous and just as important: the ability to test, learn, and iterate quickly.This episode is for investors, founders, and anyone trying to understand what it really takes to move quantum hardware from promising science to scalable engineering. For superconducting and spin-based systems, the cryogenic stack is part of the bottleneck. Chips, resonators, amplifiers, wiring, and materials all need to be tested, qualified, and improved under cryogenic conditions. If every iteration takes a full warm-up, reassembly, pump-down, cool-down, and then a day later you discover a failed wire bond, the learning cycle becomes painfully slow.That is what makes kiutra interesting. Not just because it cools things down, but because it may compress the quantum learning cycle. For certain R&D, testing, and qualification workflows, kiutra’s magnetocaloric cooling approach can reduce manual interaction to minutes, cool-down to hours, and improve throughput by roughly 3 to 10x depending on the measurement. In deep tech, the fastest learner often wins. The question is not only who has the most impressive qubit roadmap. It is also who can build the fastest learning system around that roadmap.💡 In this episode, we cover:Why testing may become a major quantum bottleneckWhy cryogenic cooling is part of the scaling problemHow helium-3 dependence creates a supply chain riskWhat magnetocaloric cooling is and why kiutra uses itWhy faster testing can compress the quantum learning cycleHow throughput and feedback speed affect iteration and yield learningWhy failed wire bonds and slow cool-downs are more costly than they lookWhy the fastest learner may gain the biggest advantage in quantumChapters00:00 Why investors should care about kiutra00:58 The helium-3 problem in quantum cooling03:05 Magnetocaloric cooling explained03:38 Alexander Regnat’s background and kiutra’s origin35:47 Why testing and qualification matter so much36:41 Why traditional dilution fridges slow the learning cycle38:41 How kiutra cuts interaction time to minutes39:42 Why faster feedback changes quantum R&D45:51 The 3 to 10x throughput advantage46:25 Why the fastest learning system may winShare this episode with someone investing in or building in quantum, and subscribe or follow Beyond the Qubit for more conversations on quantum technology, markets, and investing.📌 Disclaimer:This post is shared on a personal basis and I do not represent any company.

In neutral atoms, scale matters less if you cannot control it.What matters more in neutral atoms: the size of the array, or the ability to control and read it out as the system scales?In this episode, I unpack the key learnings from Part 3 of my Beyond the Qubit interview with Matt Kinsella, CEO of Infleqtion. I went into the conversation looking mostly at qubit scale. I came out paying much closer attention to control, readout, and sensing. Neutral atoms still have a beautiful scaling story. The qubits are encoded in atoms, the atoms are naturally identical, and they can be trapped in large arrays. But large arrays are no longer the only question.This episode is for investors, founders, and anyone trying to understand what really matters in the neutral atom race. The first wave of attention was about physical scale. The next wave may be about control. If you cannot control the atoms accurately, read them out reliably, and connect that to error correction, then large arrays remain impressive science rather than useful logical systems.That is why photonics, lasers, vacuum systems, and readout are not side details. They are central to the investment case. And the second thing I underestimated was quantum sensing. After speaking with Matt, I see it less as an adjacent market and more as a real commercial signal. Infleqtion sits at the intersection of both: neutral atom computing and quantum sensing. That makes the company interesting not only from a technical standpoint, but also from a commercialization standpoint.💡 In this episode, we cover:Why control and readout may matter more than array sizeWhy neutral atoms still have a strong scaling advantageWhy photonics, lasers, and vacuum systems are central to the roadmapHow readout connects physical arrays to logical qubit qualityWhy quantum sensing deserves more investor attentionHow clocks, RF systems, and inertial sensing could become earlier marketsWhy GPS resilience makes sensing more than a niche science storyThe two investor questions that matter most for InfleqtionChapters00:00 The two biggest investor takeaways01:00 Why sensing could become a revenue bridge02:23 Infleqtion’s ambitious logical qubit roadmap03:03 Neutral atoms explained simply05:24 Why control and readout matter more now05:37 Why sensing changes the business model06:48 GPS jamming, spoofing, and why sensing matters10:12 Why photonics and lasers matter for logical qubits12:43 Integrated photonics and scaling the control stack16:50 The two questions investors should watchShare this episode with someone investing in or building in quantum, and subscribe or follow Beyond the Qubit for more conversations on quantum technology, markets, and investing.📌 Disclaimer: This post is shared on a personal basis and I do not represent any company.

Can neutral atoms turn physical scale into logical qubit quality?What matters more in neutral atoms: how many atoms you can trap, or whether you can turn them into high-quality logical qubits at acceptable overhead?In this episode, I continue my deep dive with Matt Kinsella, CEO of Infleqtion, to unpack what I think is the real neutral atom question. Infleqtion has already shown a 1,600 physical qubit array, which is impressive. But physical qubits are not the final scoreboard. Logical qubits are. That is why the real investor question is not just whether neutral atoms can produce large arrays, but whether those arrays can be converted into useful logical qubits efficiently.This episode is for investors, founders, and anyone trying to understand how neutral atom systems may actually scale. We get into why the bottleneck is not adding more atoms, but scaling the optics, lasers, readout, control software, and error correction around them. That is why photonics, spatial light modulators, photonic integrated circuits, dual-species atoms, and qubit movement are not side details. They are part of the scaling architecture.That is what makes this conversation so important. Neutral atoms may have a real advantage because the qubits are naturally identical, highly packable, and movable. But that advantage only matters if the platform can cross the line from physical qubit headlines to logical qubit quality.💡 In this episode, we cover:Why logical qubits matter more than physical qubit headlinesInfleqtion’s roadmap from 12 logical qubits to 1,000Why neutral atoms have a natural scaling advantageWhy optics, lasers, and readout become the real bottlenecksWhy spatial light modulators matter for scalable controlHow photonic integrated circuits could improve stability and scaleWhy cesium and rubidium together could support a dual-species approachWhy movable qubits may matter for error correction and overheadChapters00:00 Why logical qubits are the real metric 03:56 Infleqtion’s logical qubit roadmap 06:51 Why neutral atoms scale differently 10:15 Cesium, rubidium, and the dual-species approach 12:13 Why spatial light modulators matter 15:35 Photonic integrated circuits and better lasers 16:50 Why quality can improve with quantity 17:20 Why movable qubits matter for error correction 23:06 The biggest technical bottlenecks ahead 26:48 What investors should really watchShare this episode with someone investing in or building in quantum, and subscribe or follow Beyond the Qubit for more conversations on quantum technology, markets, and investing.

What if one of the most important questions in quantum is not how many qubits a company can build, but whether it can finance the journey?In this episode, I go deep with Matt Kinsella, CEO of Infleqtion, to explore why the company’s neutral atom strategy may look very different from the standard quantum computing playbook. Most companies are still judged on the long-term roadmap: more qubits, better gates, lower error rates, logical qubits, fault tolerance. All of that matters. But Infleqtion is trying to build more than a quantum computer. It is trying to build a quantum technology company.This episode is for investors, founders, and anyone trying to understand how quantum companies may actually survive long enough to reach useful quantum computing. Neutral atoms are not only useful as qubits. They can also be used for clocks and sensors, which opens up nearer-term markets in precision timing, GPS resilience, RF sensing, and inertial sensing. That creates a possible commercial bridge before useful quantum computing fully arrives.That is what makes this conversation so interesting. The question is not only whether Infleqtion can build a useful quantum computer. It is also whether clocks, sensors, and timing systems can create the revenue bridge that helps fund the much longer computing roadmap.💡 In this episode, we cover:Why Infleqtion is building a quantum technology company, not only a quantum computing companyWhy neutral atoms can be used for clocks, sensors, and computingWhy precision timing is becoming a resilience problem, not just a science problemHow GPS jamming and spoofing create demand for better local timingWhy nearer-term sensing revenue could help fund the longer quantum computing roadmapWhy Matt compares the business logic to Nvidia’s path into larger marketsWhat investors should watch when judging commercial traction versus scientific promiseWhy financing the road may matter as much as the roadmap itselfChapters 00:00 Why investors should care about Infleqtion 01:04 Why neutral atoms matter beyond quantum computing 03:54 The Nvidia analogy and the revenue bridge 04:46 Matt Kinsella’s path from investor to CEO 12:49 Why capital is one of the biggest questions in quantum 19:07 What quantum technology can do better than classical systems 19:58 Why GPS timing is fragile 24:28 How Infleqtion’s quantum clocks work 36:03 How clocks, sensors, and computing connect 42:01 How photonics and scale could drive cost downShare this episode with someone investing in or building in quantum, and subscribe or follow Beyond the Qubit for more conversations on quantum technology, markets, and investing.

What if one of the most important companies in quantum is not the one building the qubits, but the one helping the industry see what is going wrong around them?In this episode, I break down my key learnings from the QuantaMap interview and why I think diagnostics could become one of the most strategic layers in quantum computing. One of the biggest bottlenecks may not be qubit count itself. It may be the invisible defects around it: tiny magnetic fluctuations, microscopic heat leakage, material imperfections, and unwanted current paths that disturb the quantum system.This episode is for investors, founders, and anyone trying to understand how value could build around the quantum stack. In semiconductors, companies like KLA became essential because advanced chips could not scale without metrology and inspection. Quantum may face the same reality, possibly even more strongly, because these systems are so sensitive and because invasive measurements can disturb the system itself.That is what makes this discussion so interesting. Scaling quantum is no longer just about building a hero experiment in a lab. It is about repeatability, yield, reliability, and eventually semiconductor-style manufacturing. If that shift happens, the diagnostic layer around quantum could become far more important than many people expect.💡 In this episode, we cover:Why diagnostics may become a strategic layer in quantum computingWhy invisible defects around qubits matter so muchHow QuantaMap’s SQUID-based approach helps reveal hidden physical disturbancesWhy quantum inspection is harder than classical chip inspectionWhy repeatability, yield, and reliability will matter more as quantum scalesWhy QuantaMap could become part of a KLA-like infrastructure layer for quantumThe biggest risks, including technology exposure and customer adoptionWhy measurement may become more valuable as computing gets more complexChapters 00:00 Why quantum diagnostics matters 01:27 Why quantum is more complex than classical chips 02:50 What QuantaMap actually does 04:01 What a SQUID is 04:42 Why traditional tools miss the real problem 06:55 Why this matters for investors 08:17 Could QuantaMap become the KLA of quantum? 09:34 The biggest risks for QuantaMap 11:25 What would increase conviction 13:23 The one line investors should rememberShare this episode with someone investing in or building in quantum, and subscribe or follow Beyond the Qubit for more conversations on quantum technology, markets, and investing.📌 Disclaimers: This is not investment advice. I do this under my personal name and do not represent any company.

What if the real bottleneck in quantum is not building the chip, but learning why it fails?In this episode, I unpack the key learnings from Part 2 of my deep dive with Johannes Jobst, CEO of QuantaMap. One of my biggest takeaways is that quantum may need its own process control and diagnostics layer before the industry can truly scale. Building a few quantum chips in a lab is one thing. Building thousands of high quality chips with repeatable performance and acceptable yield is something very different.This episode is for investors, founders, and anyone trying to understand what it will take for quantum to move from lab to fab. In semiconductors, scaling did not happen through transistor innovation alone. It also required decades of progress in inspection, metrology, yield learning, process control, and manufacturing feedback loops. Quantum is only beginning that journey.That is what makes this conversation so important. If yield remains low and failure analysis stays slow, scaling becomes much harder. Without a real diagnostics layer, every failed chip stays a mystery instead of becoming a learning cycle. The companies that help the industry learn, improve, and manufacture quantum chips reliably at scale may end up becoming one of the most important layers in the value chain.💡 In this episode, we cover:Why diagnostics may be a critical missing layer in quantum manufacturingWhy quantum scaling needs more than better qubitsHow cryogenic inspection changes what chipmakers can actually learnWhy room-temperature measurements often miss the real problemHow process control, yield learning, and feedback loops could shape quantum manufacturingWhy “business as usual” is still one of the biggest bottlenecks in the marketHow QuantaMap thinks about becoming a deeply embedded diagnostics layerWhat investors should watch as quantum moves from lab to fabChapters 00:00 Why chip diagnostics matters in quantum00:44 How QuantaMap’s cryogenic measurement works 04:27 Why multimodal imaging matters 06:15 Why process control comes later 08:09 Why cryogenic scanning matters 15:29 Business as usual is the real competition 20:42 Barriers to entry and customer lock-in 23:44 The ASML-style ambition 28:57 Diagnostics as a service and tool sales 34:44 Why timing from lab to fab matters 46:53 What would increase conviction 51:13 Why QuantaMap matters in the value chain🔗 Resources / Links 🎧 Listen to all episodes →https://open.spotify.com/show/7HZpSCz1w7a782e1B26MYA?si=JjJ7gTAfRGaZwnYwMa65mQShare this episode with someone investing in or building in quantum, and make sure to subscribe or follow Beyond the Qubit for more conversations on quantum technology, markets, and investing.📌 Disclaimers: This is not investment advice. I do this under my personal name and do not represent any company.

What if one of the biggest winners in quantum is not the company building the qubits, but the one helping everyone understand what is going wrong inside them?In this episode, I unpack the key learnings from my deep dive with Johannes Jobst, CEO of QuantaMap. The deeper I go into quantum computing, the more I think this industry will become obsessed with measurement. Most people focus on the race for better qubits, higher fidelity, and larger systems. But after this conversation, I am no longer sure that is the full story.This episode is for investors, founders, and anyone trying to understand how the quantum value chain may evolve. One of the biggest bottlenecks may not just be building quantum chips. It may be inspection, metrology, defect detection, process supervision, and understanding the subtle material imperfections and microscopic noise sources that undermine coherence, repeatability, and yield.That is what makes this conversation so important. In classical semiconductors, advanced manufacturing scaled because an entire ecosystem was built around measurement, validation, and process control. Quantum does not yet have that same mature inspection layer. If that becomes a core bottleneck, the companies that help the industry see, diagnose, and improve quantum systems may become just as important as the companies building the hardware itself.💡 In this episode, we cover:Why measurement may become one of the most important layers in quantum computingWhy quantum chip inspection is still an underbuilt part of the stackHow subtle material defects and microscopic noise sources affect performanceWhy coherence, repeatability, and yield depend on better diagnosticsHow the quantum industry may shift from building systems to validating and controlling themWhy inspection, metrology, and process supervision could become strategically valuableWhat investors should learn from the semiconductor industry’s measurement ecosystemWhy quantum may become not just a computing revolution, but a measurement revolutionChapters 00:00 Why investors should care about QuantaMap01:48 Johannes Jobst’s background in physics and semiconductors 14:54 What defects really matter in quantum chips17:58 Why measurement matters more in quantum25:44 Where chip measurement fits in the quantum stack 33:04 Process control, defects, and root cause analysis 43:32 Yield loss and performance bottlenecks in quantum 44:56 Why volume inspection could become critical🔗 Resources / Links 🎧 Listen to all episodes → UCibVGKTQwLCsj0hBgrgWDpAShare this episode with someone investing in or building in quantum, and make sure to subscribe or follow Beyond the Qubit for more conversations on quantum technology, markets, and investing.📌 Disclaimers: This is not investment advice. I do this under my personal name and do not represent any company.

Could the real value in quantum sit above the hardware?Because after two hours with quantum founders, the real question is not only what they are building. It is also how that changes the value chain.In this episode, Henny Crauwels interviews me after my deep dive with ParityQC to unpack the biggest lessons from that conversation. One takeaway stood out: the most interesting quantum company may not be the one with the most qubits. It may be the one that helps everyone else get more out of their qubits.This episode is for investors, founders, and anyone trying to understand how quantum computing could evolve as a market. We look at why architecture matters, how ParityQC’s approach could reduce bottlenecks in today’s systems, and why enabling layers may matter so much in a market where it is still unclear which hardware path will win.That is what makes this format valuable. The deep dives explain the technology. The key learnings connect it back to markets, business models, strategy, and where value may actually accrue across the stack.  Why quantum value may not all sit in hardware What ParityQC is actually doing in simple terms Why architecture could become a critical layer in the quantum stack How enabling companies may have lower single-platform risk Why early monetization matters in quantum What ParityQC’s IBM benchmark really shows, and what it does not Which KPIs matter most for investors following ParityQC The biggest risks to watch, including adoption, neutrality, and access to capital  💡 In this episode, we cover:Chapters00:00 Key takeaways and disclaimers01:23 Why architecture changed my view of quantum03:58 What ParityQC actually does06:36 What the IBM benchmark proves, and what it does not09:39 Could ParityQC become the ARM of quantum?11:52 Where value may be captured in the quantum stack14:17 Why enabling companies may be more attractive early19:40 The investor KPIs I would track22:15 What would increase my conviction23:36 The biggest risks for ParityQC26:48 What this says about the broader quantum market🔗 Resources / Links Listen to all episodes: SpotifyBeyond the QubitShare this episode with someone investing in or building in quantum, and make sure to subscribe or follow Beyond the Qubit for more conversations on quantum technology, markets, and investing.📌 Disclaimer: This is not an investment advice. This post is shared on a personal basis, and I do not represent any company.

What if the company that captures the most value in quantum is the one that helps everyone else perform better?In this episode, I go deeper with Magdalena Hauser and Wolfgang Lechner of ParityQC to explore a very different way of thinking about value in quantum computing. A lot of the discussion still centers on hardware alone: more qubits, better fidelity, bigger systems. But ParityQC is making a different bet, one built around architecture, IP, and enabling software.This conversation is for investors, founders, and anyone trying to understand where value may really build across the quantum stack. We unpack ParityQC’s core idea of representing relative information instead of the information itself, why that could matter for connectivity and error correction, and why their business model looks closer to an architecture company than a full stack hardware player.That is what makes this episode so interesting. It is not just about physics. It is about business model, defensibility, IP, and what it means to control a critical layer of the stack in a market where not every winner will own the full machine.💡 In this episode, we cover:How ParityQC’s parity transformation changes the way quantum information is representedWhy relative information could help with connectivity, programmability, and redundancyHow ParityQC thinks about error correction differentlyWhy the company licenses IP and enabling software instead of building full stack hardwareWhy their model looks closer to ARM than a traditional quantum hardware companyHow ParityQC monetizes through licensing and softwareWhy profitability matters so much in today’s quantum marketWhat investors should watch if they want to track whether the model is workingChapters00:00 Why ParityQC’s architecture matters00:48 The parity transformation explained04:27 How ParityQC thinks about error correction07:47 Patents, IP, and barriers to entry13:00 How ParityQC makes money16:43 Why governments are buying early quantum systems27:34 Why ParityQC wants to be the ARM of quantum29:29 Profitable since 202333:13 The KPI investors should watch42:08 Why they are excited about the future🔗 Resources / LinksExplore ParityQC and their work in quantum architecture → https://parityqc.com/Magdalena Hauser on LinkedIn → https://www.linkedin.com/in/magdalena-hauser42/Wolfgang Lechner on LinkedIn → https://www.linkedin.com/in/wolfgang-lechner-2b36b634/Listen to all episodes: https://open.spotify.com/show/7HZpSCz1w7a782e1B26MYAShare this episode with someone investing in or building in quantum and make sure to subscribe or follow Beyond the Qubit for more conversations on quantum technology, markets, and investing.📌 Disclaimer: This post is shared on a personal basis and I do not represent any company.

What if the real breakthrough in quantum computing is not just better qubits, but a better way to use them?In this episode, I explore why architecture may be one of the most overlooked layers in quantum computing with Magdalena Hauser and Wolfgang Lechner of ParityQC. Their latest result, implementing a 52-qubit Quantum Fourier Transform on an IBM Quantum Heron processor, suggests that progress may not only come from hardware improvements, but also from smarter architecture and compilation.This episode is for investors, founders, and anyone trying to understand where value may build across the quantum stack. If ParityQC is right, the winners in quantum may not only be the companies building the machines. They may also be the companies that make those machines more useful, more scalable, and more efficient.That is what makes this conversation so interesting. It shifts the question from who has the best hardware to who has the best architecture for turning hardware into real performance.💡 In this episode, we cover:Why ParityQC believes architecture is a core layer of value in quantum computingWhy the Quantum Fourier Transform is such an important benchmarkHow ParityQC and IBM reached a 52-qubit QFT resultWhy this result is about more than hardware aloneHow architecture and compilation can improve real quantum performanceWhy ParityQC’s approach is designed to work across multiple hardware platformsWhy removing swaps matters in quantum computingWhy investors may need to look beyond hardware to understand where value will accrueChapters00:00 ParityQC and why investors should care01:49 Why Quantum Fourier Transform matters03:00 The 52-qubit QFT breakthrough explained06:57 Does this accelerate the quantum timeline?11:16 Why ParityQC works across hardware platforms14:02 How ParityQC removes swaps17:04 Why architecture may capture more value24:04 The origin of ParityQC’s architecture30:09 How ParityQC works in simple terms🔗 Resources / LinksExplore ParityQC and their work in quantum architecture → https://parityqc.com/Magdalena Hauser on LinkedIn → https://www.linkedin.com/in/magdalena-hauser42/Wolfgang Lechner on LinkedIn → https://www.linkedin.com/in/wolfgang-lechner-2b36b634/Listen to all episodes: https://open.spotify.com/show/7HZpSCz1w7a782e1B26MYAShare this episode with someone investing in or building in quantum and make sure to subscribe or follow Beyond the Qubit for more conversations on quantum technology, markets, and investing.📌 Disclaimer: This post is shared on a personal basis and I do not represent any company.

In Part 3 of this conversation, Frank Dekker reflects on one of the biggest takeaways from his discussion with Olivier Ezratty: great science alone does not create a winning quantum ecosystem. Europe has deep talent, strong research, and serious technical capability, but turning that into globally relevant companies is a different challenge.This episode is for investors, founders, policymakers, and anyone trying to understand what it will really take for Europe to compete in quantum. The conversation goes beyond technology and looks at the harder questions around energy, coordination, ecosystem building, and long term strategy.That is what makes Olivier’s perspective so valuable. He is not only trying to understand where quantum is going. He is trying to improve the odds that Europe builds something meaningful around it.💡 In this episode, we cover:Why strong quantum talent does not automatically create strong quantum companiesWhy the Quantum Energy Initiative matters for the future economics of the industryWhy energy and power costs could shape who scales and who can deployWhy ecosystem building takes more than great technologyHow Olivier thinks about strengthening the French and European quantum landscapeWhy Europe needs stronger links between research, capital, policy, and industryWhy coordination may matter as much as technical progressWhat Europe needs most right now to improve its chances in quantumChapters00:00 Introduction to Olivier Ezratty00:48 Olivier’s background in software, Microsoft, and startups04:07 How curiosity led him into science and quantum05:52 From tech events to explaining quantum publicly10:25 Building a 1,500-page quantum guide13:36 Olivier’s goals for the next five years14:18 Why Europe has talent but not enough quantum companies35:39 Quantum Energy Initiative and why energy matters early37:53 The hidden classical costs behind useful quantum computing39:05 Why quantum needs a system-level engineering mindset41:31 Quantum matter, new materials, and Europe’s next opportunity🔗 Resources / LinksFollow Olivier Ezratty on LinkedIn: https://www.linkedin.com/in/ezratty/Listen to all episodes: https://open.spotify.com/show/7HZpSCz1w7a782e1B26MYAShare this episode with someone following Europe’s quantum future. Subscribe or follow Beyond the Qubit for more conversations on quantum technology, markets, and investing.📌 Disclaimer: This post is shared on a personal basis, and I do not represent any company.

Quantum computing has a hype problem. But the real challenge is much harder than most people think.In Part 2 of this conversation, Frank Dekker continues his deep dive with Olivier Ezratty and gets into what real progress in quantum actually looks like. One of the clearest takeaways is that every step forward can create a new bottleneck. Solve one problem, and another appears right behind it.This episode is for investors, founders, and anyone trying to understand why scaling quantum computing is so difficult. The challenge is not just adding more qubits. It is building a system that can handle noise, error correction, control complexity, and energy demands, while still producing something useful at a cost the market can bear.That is what makes this conversation so valuable. Olivier brings a grounded perspective that goes beyond exciting narratives and focuses on what it really takes to make the whole system work.💡 In this episode, we cover:Why quantum computing has a hype problemWhy solving one problem often creates anotherWhy scaling quantum is not just about adding more qubitsHow noise and control complexity slow real progressWhy error correction creates major system overheadWhy energy demands matter in the future of quantum computingWhy quantum is a full stack challenge across physics, engineering, software, control, and economicsWhy the winners in quantum will be the teams that can make the whole system workYouTube Chapters00:00 Introduction and the core question00:52 Does a quantum computer really work?05:45 Is a quantum computer really a computer?10:39 Quantum memory, QRAM, and communication14:28 How AI helps quantum and where it still does not19:38 Which quantum technology platform will win?27:20 Why every scaling solution creates a new problem40:05 Quantum Energy Initiative and why energy matters52:49 The three expensive classical costs behind FTQC56:10 Quantum engineering and the bigger opportunity in quantum🔗 Resources / LinksFollow Olivier Ezratty on LinkedIn: https://www.linkedin.com/in/ezratty/Listen to all episodes: https://open.spotify.com/show/7HZpSCz1w7a782e1B26MYAShare this episode with someone following quantum computing seriously.Subscribe or follow Beyond the Qubit for more conversations on quantum technology, markets, and investing.📌 Disclaimer: This post is shared on a personal basis and I do not represent any company.

Why is quantum computing still so hard to explain clearly, even for smart investors?In this episode of Beyond the Qubit, Frank Dekker sits down with Olivier Ezratty, one of the most respected independent voices in quantum technology. Olivier shares how he went from software engineering and Microsoft to becoming a key educator, researcher, and bridge builder across the quantum ecosystem.This conversation is for investors, founders, and deep tech professionals who want a clearer view of quantum computing without the hype. They discuss what sparked Olivier’s obsession with the field, why so many people still explain quantum poorly, and what it really takes to understand the space across hardware, software, physics, and market reality.💡 In this episode, we cover:How Olivier Ezratty became one of the most trusted independent researchers in quantum computingWhy early D-Wave claims pushed him to study quantum more seriouslyHow Understanding Quantum Technologies grew into a major free resourceWhy investors need to connect physics, hardware, and software to understand the marketWhat Europe gets right in quantum, and where it still struggles commerciallyWhy the Quantum Energy Initiative matters for the future of the industryHow long-form research, public learning, and sharing knowledge can build real influence in deep techWhat Olivier sees as the biggest bottlenecks over the next five yearsChapters 00:00 Introduction to Olivier Ezratty 04:09 Olivier’s background from software to Microsoft to deep tech 08:13 Why Olivier started creating long-form knowledge resources 33:09 What first sparked his interest in quantum computing37:20 Olivier’s goals: quantum energy, teaching, and Europe40:07 Why Europe needs both strong research and strong startups 50:17 The biggest bottlenecks for the next five years🔗 Resources / LinksFollow Olivier Ezratty on LinkedIn: https://www.linkedin.com/in/ezratty/Listen to all episodes: https://open.spotify.com/show/7HZpSCz1w7a782e1B26MYAShare this episode with someone investing in or building in quantum and make sure to subscribe or follow Beyond the Qubit for more conversations on quantum technology, markets, and investing.

DeepMind helped transform AI by using games as a training ground.Evert van Nieuwenburg wants to build the European DeepMind for quantumcomputing.In my conversation with Evertvan Nieuwenburg on Beyond the Qubit, one idea stood out:What if games are not just a way to explain quantum computing, but a way tounlock it?Thatsounds playful.Butthe ambition is serious.DeepMindshowed that games could be much more than entertainment.They became structured environments for learning, experimentation, search, anddiscovery.Evert’svision is that quantum computing may need something similar.Notjust better hardware.Notjust more qubits.Notjust lower error rates.It may also need a better playground for building intuition.Becausethe space of possible quantum circuits and quantum algorithms isenormous.Mostof it is noise.Usefulstructure is rare.Andintuition is hard to build.Thatis where quantumgames become interesting.Gamescreate structure. They give people rules, feedback, andgoals. And when a hard problem becomes game-like, it can become easier forhumans to explore and potentially easier for AI systems to learn from too.Thatis the part of the conversation I keep coming back to.Evertis not talking about games as a side project for outreach.He is pointing at a bigger idea:Europe may have a chance to build its own DeepMind for quantum computingnotby copying AI exactly,butby creating the structured environments that help peopleand machines discover what matters in quantum.Thatis a bold vision.Andbold visions are usually where the interesting companiesbegin.  #QuantumComputing #QuantumTechnology #AI #Gaming #DeepTech@evertvannieuwenburg📌 Disclaimer:This post is shared on a personal basis and I do not represent any company

What if games are not just a way to explain quantum, but a way to build realquantum intuition?Thatmay sound playful, but the idea is serious.Onereason DeepMind changed the direction of AI is that ittreated games as more than entertainment. They became environments forlearning, experimentation, search, and discovery.Thatmatters for quantum computing too.Becausein quantum, the challenge is not only building betterhardware. It is also learning how to navigate an enormous space of possiblequantum circuits, quantum algorithms, and interactions.Mostof that space is noise.Usefulstructure is rare.Andintuition is hard to build.Thatis where games become interesting.Gamescreate rules, feedback, and goals. They give people a morestructured way to explore complexity.Andif a quantum problem can be turned into somethinggame-like, it may become easier for humans to experiment, easier for creativethinkers to engage, and potentially more accessible to AI methods that havealready proven powerful in game environments.Thatis why this conversation stood out to me.Maybegames can do for quantum computing what they once did forAI:notsolve everything,butcreate the interface that helps people discover whatmatters.Part 2 with Evert van Nieuwenburg is out now on Beyond the Qubit.Do you think games could become a real tool for quantum research andquantum algorithm discovery, or will they remain mostly educational?#QuantumComputing #QuantumAlgorithms #QuantumResearch #AI #Gaming#DeepTech #BeyondTheQubit

In Part 1 of my conversation with Evert van Nieuwenburg on Beyond the Qubit, weexplored whether games could help unlock the next phase of quantum computing.Thatmay sound playful, but the idea is serious.Gamescreate structure. They make complex systems easier toexplore, help people build intuition, and may open the door to more creativeways of thinking about quantum problems.If a quantum problem can be shaped into something game-like, it may alsobecome more accessible to AI methods that have already proven powerful in gameenvironments.WhatI found most interesting is that this is not just abouteducation.It is about creativity, interaction, and discovery.Sometimesthe next breakthrough starts when more people can actuallyengage with a technology.Thatis what made this conversation with Evert stand out to me. Here are thelinks:Youtube: https://youtu.be/hu2kWdRDa_4  #QuantumComputing #QuantumTechnology #AI #Gaming #DeepTech@evertvannieuwenburg📌 Disclaimer:This post is shared on a personal basis and I do not represent any company

Here is the questionthat decides whether quantum scales. Can we model the real device physicsbefore we commit to the lab? Quantum is notblocked by qubits alone. It is blocked by the missing quantum EDA stack.I just recorded a 30minute summary with Jonathon Riddell, CEO of Kothar Computing, and his messageis concrete.A big barrier tobuilding quantum computers today is, in his words, strangely still on theclassical computing side.Because theclassical EDA toolchain was never built to capture the quantum physics thatdetermines whether these devices work.So teams simulate,hand designs to a fab, fabricate the chip, measure it, and only then realizekey effects were not captured before committing to the lab.Jonathon calls thisbeing blind, and he adds an important nuance. That is a bit harsh, because wedo capture some effects.But not the fullphysics we actually care about.That is why quantumEDA matters.He describes it as atop to bottom simulation suite that lets engineers design quantum chips whilecapturing the physics that matters, before expensive lab cycles.Where Kothar fits inthe value chain is as the compute engine underneath that stack.They built alanguage, Aleph, combining symbolic computing with numerical high performancecomputing.The idea is simple.In quantum many body problems, symbolic reduction is leverage.Skip it, and you canend up solving something one hundred to one thousand times harder thannecessary.My investortakeaway.If you only trackqubit counts, you are missing a parallel race. The 30 min summaryof the deep dive interview with CEO Jonathon Riddell is out now.Link:https://youtu.be/ZAc-1nnNLa0 #quantum #eda#quantumcomputing #semiconductors #chipdesign #hpc #scientificcomputing#deeptech #simulation #designautomation #QuantumSoftware #HybridComputing #HPC#DeveloperTools#ProgrammingLanguages #Compilers #Compilation #Reproducibility#PerformanceEngineering#SoftwareEngineering #ComputeAcceleration #DeepTech#KotharComputing#physics #deeptech #BeyondTheQubit #FutureOfCompute @Kotharcomputing@JonathonRiddell 📌 Disclaimer: This post is shared on a personal basis and I do notrepresent any company

Most people thinkquantum progress is a qubit story.Jonathon Riddellargues a big part of the bottleneck is the missing quantum EDA stack.In part 2 of my deepdive with Jonathon, CEO of Kothar Computing, the punchline is blunt.We are stillbuilding quantum hardware at the scientific experiment layer, because classicalEDA was never built to capture the quantum physics that determines whetherthese devices work.So teams simulate,hand designs to a fab, fabricate the chip, measure it… and only then realizekey effects weren’t captured before committing to the lab.Jonathon calls thisbeing “blind,” and he also notes that’s a bit harsh because we do capture someeffects.But the pointstands.This is not just aninnovation pace problem.It is a design loopproblem.Here’s my takeawayanalogy.Classicalsemiconductors became an industry when EDA helped turn physics into repeatableengineering workflows.Quantum will needits own version of that.Jonathon’s vision isa vertically integrated quantum EDA workflow, from design choices all the wayto a file you hand off to a fab, without stitching together disjoint tools.He thinks the fieldis heading there within about five years.Kothar’s wedge isthe compute engine underneath that stack.They built alanguage, Aleph, combining symbolic computing with numerical high-performancecomputing.In quantum many-bodyproblems, symbolic reduction matters: skip it, and you can end up solvingsomething 100 to 1000 times harder than necessary.That is also why thepartnership with Nanoacademic Technologies’ QT CAD is telling.QT CAD buildsrealistic bottom-up chip models.Kothar focuses onthe hard quantum many-body solving layer once the models get real.Modeling plussolving.That is how an EDAecosystem forms.One investor linethat stuck with me.The value can becompressing a 12-month project into a week by removing orchestration frictionand making workflows robust enough to scale.If you areunderwriting quantum, track qubit counts.But also ask themore boring, more powerful question.Who is building theEDA layer that makes quantum engineering repeatable?Because the winnersmay not be the team with the most qubits first.They may be the teamwith the fastest design loop.#quantum #eda#quantumcomputing #semiconductors #chipdesign #hpc #scientificcomputing#deeptech #simulation #designautomation📌 Disclaimer: This post is shared on a personal basisand I do not represent any company

Most people talk about quantum as if the hard part is the qubits.In my interview withJonathon Riddell, CEO of Kothar Computing, the bottleneck looked different: theclassical layer that has to run the science.Because real quantumworkflows are hybrid. Quantum plus classical.And hybrid workflowslive or die on orchestration, reproducibility, performance, and deployment.Here is theuncomfortable truth.Python is perfectfor exploration. It is the front door. Python has great compiler-adjacenttools, but the workflow is still fragmented and hard to make robust end-to-end.The pain starts whenyou move from notebooks to real workloads and you need predictable execution,repeatable builds, and optimized, validated runs across heterogeneous hardware.That is the compilegap.The jump fromPython-first workflows to a reliable compilation and transpilation pipelinethat targets CPUs, GPUs, and QPUs. And it shows up everywhere you care about inphysics and quantum: dynamiclanguages make certain classes of errors harder to catch early, and largescientific stacks accumulate risk through runtime shape/type/unit mismatches.Assystems grow, you want more errors caught before runtime, and failures that areloud and actionable.  This is why I findAleph so interesting.Aleph is Kothar’sattempt to raise the ceiling for scientific and quantum computing: a languagedesigned to feel natural for researchers, while still being built forcompilation and performance.The idea is simplebut powerful.Keep the ergonomicsscientists love.Add the compilerbackbone production systems require.Make hybridworkflows feel normal, not fragile.If you are buildingor investing in quantum, I think this framing matters.The winners will notjust have better qubits.They will havebetter tooling that turns quantum into a usable accelerator inside a largerscientific workflow.Part 1 of the deepdive is out now.Link:https://youtu.be/T_idIcdYSgc   Also curious: wheredo you feel the pain most today, compilation, debugging, or reproducibility?#QuantumComputing#QuantumSoftware #HybridComputing #ScientificComputing #HPC#DeveloperTools#ProgrammingLanguages #Compilers #Compilation #Reproducibility#PerformanceEngineering#SoftwareEngineering #ComputeAcceleration #DeepTech#KotharComputing#physics #deeptech #BeyondTheQubit #FutureOfCompute @Kotharcomputing@JonathonRiddell  📌 Disclaimer: This post is shared on a personal basis and I do notrepresent any company

Because it moves the field from impressive lab demos toworkloads you can actually run.After a year of hosting Beyond the Qubit, I have learnedthis.The real challenge is not the physics.It is knowing what is real progress, while the answer is still uncertain.Here is what I have learned so far.First.Quantum is no longer one story.There are multiple credible technology paths, and it is genuinely difficulttoday to say which one will win.Second.Scaling is still underestimated.Not just more qubits on a chip/unit, but also clustering chips together, andimproving error correction so you need fewer physical qubits for each logicalqubit.Third.A simple truth I keep repeating to myself.A logical qubit is an error corrected qubit you can compute with reliably.And today, the world still has no or only a very small number of them.That is why the next milestone matters.My working heuristic is this.Around 100 logical qubits is where the first meaningful applications may startto appear.And somewhere around 1,000 to 2,000 logical qubits is where many of the bigapplications start to open up, like molecular modelling and large scaleoptimization.The exact number will depend on the application and theerror rates.But the order of magnitude matters.So if the industry reaches 100 logical qubits, it is notjust a benchmark.It is a strong signal that scaling is working.And it makes the path toward 1,000 plus feel less like science fiction and morelike an engineering roadmap.That shift changes things.Capital.Talent.Time horizons.And the way society talks about quantum.Now a second lesson for investors and builders.This market could consolidate around a few winners.That is exciting, but it also means technology risk remains high.So where do you look if you want exposure without betting ona single horse?Suppliers.The enablers.Because scaling does not just mean better chips.It depends on the technology, for super conducting qubits it means morechannels, more calibration, more test, more wiring, more cooling, and bettererror correction tooling.This is why I like studying the enabling layer.Chip testing, control systems, interconnects, cryogenics, and error correctionsoftware.These companies often aim to support more than one quantum technology path,which can mean earlier revenue and lower single technology risk.Two examples I personally find interesting are OrangeQuantum Systems and QC Design.Not investment advice, just examples of the enabling layer.One more observation.The world is spending enormous amounts on compute for AI.Quantum is not the same as AI compute, and AI spend is not a direct driver ofquantum.But it can accelerate adjacent tooling, packaging, photonics, and engineeringtalent that the quantum ecosystem also depends on.Add geopolitics and digital sovereignty, and quantum becomeseven more strategic.So yes.Quantum still has uncertainty.But the direction of travel is clear.The next years are about proving that logical qubits canscale.Through scale up, scale out, and better error correction.That is what I will keep tracking on Beyond the Qubit.Now I am curious about your view.Which unlock do you think comes first on the road to 100logical qubits.Scale up, scale out, or error correction. If you want to receive the presentation, post presentationbelow in the comments. Here are the links for:Youtube:  Spotify:  📌 Disclaimer: Thispost is shared on a personal basis and I do not represent any company#QuantumComputing #QuantumTechnology#FaultTolerantQuantumComputing #LogicalQubits #QuantumErrorCorrection#QuantumHardware #DeepTechInvesting #Semiconductors

This post is a short summary of a longer conversation on theBeyond the Qubit podcast.I sat down with the CEO of QC Design to talk about whatquantum computing really is once you strip away the metaphors.A qubit is not a bit with uncertainty.It is a fragile physical state.You cannot read it without destroying it.You cannot copy it.You cannot inspect what it contains.You never really ask a qubit a question.What you actually do is prepare a physical system, let itevolve under carefully designed control pulses, and then force a measurement.You get a single outcome. Not an answer, but a sample.The qubit does not reason, choose, or understand thequestion.It simply reacts to physical forces and collapses.Seen this way, quantum computation looks less likecomputation and more like continuous damage control layered on top of aphysical process that barely exists long enough to be manipulated.This perspective matters.Because it exposes why scaling quantum systems is fundamentally an engineeringproblem, not a software one.The full conversation goes much deeper into what this meansfor system design, abstractions, and where many roadmaps quietly break down.More in the full episode of Beyond the Qubit.https://youtu.be/9ydv1tFjgqo Quantum #QuantumArchitecture#ErrorCorrection #QuantumSoftware #BeyondTheQubit @IshDhand @QC_Design 📌 Disclaimer: Thispost is shared on a personal basis and I do not represent any company

It’s blocked by design choices.”That was one of thestrongest realizations from Part 2 of my deepdive with Ish Dhand, co-founder of QCDesign, on Beyond the Qubit.Most people talkabout fault-tolerant quantum computing as if it’s a single problem.In reality, it’s a design-space explosion.That reframed how Ithink about progress in quantum.What stood out to mein this part of the conversation:• Hardware teamsdon’t struggle with one error, they struggle with many interacting imperfections at the same time• Open-sourcesimulators can scale to thousands of qubits, but usually only by assuming very simplified error models• Real hardware hasto deal with leakage, coherent errors, pulse timing, idling, cross-talk,  all at once• Many of theseeffects only become visible at the scale of thousandsof physical qubits per logical qubitThis is where QCDesign plays a unique role.Rather than bettingon a single error-correction code or architecture, they help hardware teams simulate realistic fault-tolerant systems beforebuilding them,  across platforms,codes, decoders, and noise models.What really changedmy perspective:Error correctionisn’t just about finding a better code.It’s aboutunderstanding where engineering effort actuallypays off.If leakage hurtsyour logical qubits more than erasures,why spend yearsoptimizing the wrong thing?If longer pulsesimprove gate fidelity but quietly destroy system performance through idlingerrors,where’s the realoptimum?These aren’tacademic questions.They determine cost, timelines, and whether scaling is even feasible.One line from Ishreally stuck with me:Today, the cost of a truly useful fault-tolerantquantum computer is effectively infinite.The real progress is making that number finite, andthen bringing it down.That single sentencereframes the entire industry.In this episode, wego deep into:• why decoding speedmatters as much as code efficiency• why “software willfix it later” is usually the wrong mindset• why logicalfidelity matters more than raw qubit counts• and why faulttolerance is becoming a full-stack engineeringproblemIf you care about how quantum computers will actually be built,  not just announced,  this conversation is worth your time.🎙️Beyond the Qubit — Part 2 with Ish Dhand🔗https://youtu.be/ugo3g1Mws2M#FaultTolerantQuantum#QuantumArchitecture#ErrorCorrection#QuantumSoftware #BeyondTheQubit ⁨@IshDhand⁩ ⁨@QC_Design⁩ 📌 Disclaimer: This post is shared on a personal basis and I do notrepresent any company

That’s the question Ish Dhand has been obsessed with for years.It’s also what ledhim from academia, to Xanadu, and now toco-founding QC Design.I’m excited to sharethat Ish is joining me on Beyond the Qubit.What struck me mostin our conversation wasn’t hype or timelines, it was how hard the problem really is.A few takeaways thatstayed with me:• Fault-tolerantquantum computers aren’t blocked by a single breakthrough, but by thousands of interacting design decisions• Error correctionisn’t just a physics problem, it’s an architecture,control, and decoding problem all at once• Many hardwareteams underestimate how early they needto think about fault tolerance• Software canunlock orders-of-magnitude improvements,but only if it’s grounded in realistic noise models and hardware constraintsAt one point, Ishdescribed QC Design as the Cadence / Synopsysof quantum computing.Not building thehardware itself, but helping hardware teams understand what they’re actually building before they build it.What I appreciatedmost was his bias for action:ship early, getfeedback from real hardware teams, iterate fast, even when the problem space ismessy and incomplete.In this episode, wego deep into:• how logical qubitsreally emerge from physical ones• why differentqubit platforms face fundamentally different error profiles• why “software willfix it later” is often the wrong mental model• and what actuallyneeds to go right for fault-tolerant quantum computing to arriveIf you care about how quantum systems are designed,  not just announced,  this is a conversation worth your time.🎙️Beyond the Qubit,  episodewith Ish Dhand🔗 (link)https://youtu.be/GOuYABNmfjM #QuantumComputing#FaultTolerantQuantum #QuantumArchitecture#ErrorCorrection#QuantumSoftware #BeyondTheQubit ⁨@IshDhand⁩ ⁨@QC_Design⁩ 📌 Disclaimer: This post is shared on a personal basis and I do notrepresent any company

Quantum’s “impossible problem” is finally shiftingfrom science → engineering. In Part 2 of myconversation with Yuval Boger (CCO, QuEra)on Beyond the Qubit, we went deep into Error correction, Scaling and the physics that will determine which platforms survive.Hereare the insights 👇 https://youtu.be/Ndr7cbcDHRc  1. Every qubit technology has a fundamental weakness…until you correct it.Yuval put itbluntly:“Qubitsare fragile. Everything in the universe wants to disturb them.”Cosmic rays,vibrations, electromagnetic noise, even a gate failing 1 in 10,000 times becomes catastrophic when your algorithmrequires millions of operations.This is why error correction is the real battleground.Not qubit count.Not coherence time.Not marketingslides.This clicked for me:you don’t win by adding more qubits, you win byadding the right ones. 2. Mobility changes everything about logical qubits.Most qubit platformsare fixed in place.Neutral atoms move, and that changes the math.Yuval gave a visualI can’t unsee:Two logical qubits,each made of five physical qubits.Static hardware?You must connectthem pair by pair, accumulating errorswith every handshake.Neutral atoms?➡️ Move the qubits physically➡️ Apply one pulse of light➡️ Create all interactionsin parallelParallelism → feweroperationsFewer operations →fewer errorsFewer errors → farfewer physical qubits needed per logical qubitNeutral atoms aren’tjust another modality, they’re a differentscaling strategy. 3. The telecom analogy that reframes the entirearchitectureI comparedsuperconducting qubits to fixed fiber and neutral atoms to wireless networks.Yuval extended itbeautifully:If the central nodefails, fixed-line users are stuck.Wireless? You movethe tower closer and reconnect.Neutral atomsprovide that same architectural freedom:  4. Neutral-atom scaling isn’t PowerPoint. It’sphysics.Many companies claimthey’ll scale.Yuval asked thequestion that matters:“Areyou relying on miracles, or engineering?”Neutral atoms scalethrough: Scale-out acrossmachines? Possible.But what struck meis that scaling within a single system has aclear physics-based roadmap, unlike many competing architectures. 5. Error correction is no longer theoretical, QuEra isdemonstrating it.Yuvalwalked through the early steps:  He didn’t revealtheir full roadmap, but the direction is unmistakable. 6. Quantum is becoming practical and customers arevoting with usage, not words.Yuval shared severalsignals that quantum is crossing from research into industry: These aren’texperiments, they’re real workloads on real systems.Talk is cheap; usageis not. This isn’t hype.This is earlyindustrialization. 🤔 Which modality do you believe reaches fault-tolerant scale first?Neutral atoms?Superconducting? Trapped ions? Photonics?Or somethingcompletely different?I’d love to hearyour perspective. #QuantumComputing,#QuantumTechnology #DeepTech, #BeyondTheQubit, #QuEra, #NeutralAtoms#QuantumHardware,#FutureOfComputing, #QuantumAdvantage, #RydbergAtoms, #TechInnovation#ScienceAndTechnology,#FrontierTech, #MIT, #Harvard, #Podcast @Yuval Boger@QuEra  📌 Disclaimer: This post is shared on a personal basis and I do notrepresent any company

“Quantum computers today still can’t outperformclassical systems, and that’s exactly why this moment matters.”That was one of thefirst things Yuval Boger (CCO, QuEra)told me during our deep dive on Beyond theQubit.And it genuinelyreframed how I look at the entire quantum industry.Here’swhat most people miss 👇1. Quantum today isn’tabout solving problems. It’s about preparing for the moment it can.Yuval explained thatwith fewer than ~50 perfect qubits, a supercomputer can simulate everythinganyway.So the currentsystems, noisy, small, early,  are notthe point.What matters is trajectory.QuEra’s mission?➡️ Build quantum computers with hundreds, thousands, andeventually tens of thousands ofhigh-fidelity neutral-atom qubits. Because that’s where classical methodsbreak,  and quantum starts to matter. 2. Neutral atoms don’t just improve quantum computing.They change the rules.Most qubittechnologies are manufactured.Neutral atoms are natural, identical, and stable at room temperature.A few thingssurprised me: This isn’ttheoretical. It’s running today on AWS Braket. 3. One advantage people underrate: MIT +Harvard → QuEraFour founders camefrom MIT and Harvard.Two still contributeweekly.Neutral-atom systemsevolve fast in academia,  beingphysically close lets QuEra commercialize breakthroughs months or even yearsahead of competitors That proximityenables: It’s an unfair advantage, in the best possible way. 4. Yuval’s biggest lesson (and the most transferableone): customer curiosityNot physics.Not algorithms.Not hardware.➡️Customer curiosity.He listens not forwhat customers say today, but for what they’ll need 18 months from now.It’s a mindset thatcontributed to QuEra’s roadmap. #QuantumComputing,#QuantumTechnology #DeepTech, #BeyondTheQubit, #QuEra, #NeutralAtoms#QuantumHardware,#FutureOfComputing, #QuantumAdvantage, #RydbergAtoms, #TechInnovation#ScienceAndTechnology,#FrontierTech, #MIT, #Harvard, #Podcast @Yuval Boger@QuEra https://youtu.be/XEh6PiLnXjchttps://open.spotify.com/episode/1SyBtguc5fqEQe1PivVdrs?si=z0aHKAUrRFSnQddfSal2zQ  📌 Disclaimer: This post is shared on a personal basis and I do notrepresent any company

This week on Beyond the Qubit, I sat down with Yuval Boger, CCO of QuEra, and he said something that hit me hard:“Quantum computers today are almost useless… butthat’s exactly why now is the most exciting time.”  Here’swhat I learned 👇1. Neutral atoms might be the first scalable path to real quantum advantageMost qubits aremanufactured.Neutral atoms are perfect by nature.No fabricationdefects.No calibrationbattles.No cryogenic fridgesthe size of a room.A laser tweezertraps each atom.A laser moves itwherever you want.A single laser pulsecan operate on multiple qubits in parallel. This means: It’s wild.2. Customer-first thinking is QuEra’s secret weaponBefore we eventouched physics, Yuval talked about… listening.Not to qubits.To customers.  It’s rare indeep-tech.And it’s exactly whyQuEra builds things people actually use: This is no longeracademic curiosity.3. Why QuEra’s proximity to MIT & Harvard mattersFour founders camefrom Harvard and MIT.Two are still deeply involved.And the labs areliterally a bike ride away.  This creates aflywheel: That speed ofiteration is something other modalities can’t replicate.4. The big picture: error correction & scaleYuval gave a simpleanalogy:If you’re shoutingyour credit card number in the wind,you repeat eachdigit multiple times so it arrives correctly.Logical qubits workthe same way.And neutral atomsallow parallel operations between allphysical qubits in a logical block, something static qubits cannot do.  This dramaticallyaccelerates progress toward error-corrected systems.5. The most important signal?“We’ve moved fromscientific challenges → engineering challenges.”That’s how you knowa technology is about to break out. Transcript summaryQuEra My takeawayNeutral atoms are nolonger the “dark horse.”They’re becoming theworkhorse of quantum computing.And QuEra is notbuilding a B-2 bomber (beautiful but rare).They’re building theAirbus A350 of quantum:usable, scalable,and built for the real world.  If you want tounderstand how quantum will scale fromhundreds to tens of thousands of qubits, this episode is a must-listen.🎙️Episode link: Neutral atoms just went from dark horse →workhorse. And it changes everything.     #QuantumComputing,#QuantumTechnology #DeepTech, #BeyondTheQubit, #QuEra, #NeutralAtoms#QuantumHardware,#FutureOfComputing, #QuantumAdvantage, #RydbergAtoms, #TechInnovation#ScienceAndTechnology,#FrontierTech, #MIT, #Harvard, #Podcast @Yuval Boger@QuEra  📌 Disclaimer: This post is shared on a personal basis and I do notrepresent any company

The future of compute might not get hotter. It mightget colder.In Part 2 of my deep-dive with Thomas Ohki, CTO and co-founder of Emergence Quantum, we explored how cryogenic engineering could reshape everything we know about performance, scaling, andenergy use.Thomas and his teamare building control electronics that can operate close to absolute zero. Thesechips could solve one of the biggest bottlenecks in computing, bringingclassical and quantum systems together.What stood out mostto me in this conversation: 1). Why scalingquantum systems isn’t about adding more qubits, but removing the IO bottleneck2). How energyefficiency is becoming the next competitive frontier in AI and data centers3). Why the nextgeneration of computing might evolve where it’s cold, not hot Thomas put it in away that stuck with me:“Thenext revolution in compute might not be hotter. It might be cold.”#BeyondTheQubit#QuantumComputing #CryoCMOS #AIHardware #DeepTech #EmergenceQuantum #Innovation#Podcast 📌 Disclaimer: This post is shared on a personal basis and I do not represent any company The future of compute might not get hotter. It mightget colder.InPart 2 of my deep-dive with Thomas Ohki, CTO and co-founder of Emergence Quantum, we explored how cryogenic engineering could reshape everything we know about performance, scaling, andenergy use.Thomas and his teamare building control electronics that can operate close to absolute zero. Thesechips could solve one of the biggest bottlenecks in computing, bringingclassical and quantum systems together.What stood out mostto me in this conversation: 1). Why scalingquantum systems isn’t about adding more qubits, but removing the IO bottleneck2). How energyefficiency is becoming the next competitive frontier in AI and data centers3). Why the nextgeneration of computing might evolve where it’s cold, not hot Thomas put it in away that stuck with me:“Thenext revolution in compute might not be hotter. It might be cold.”🎧 Listento Beyond the Qubit –Emergence Quantum (Deep Dive Part 2) 👉 [Insert Spotify or YouTube link] #BeyondTheQubit#QuantumComputing #CryoCMOS #AIHardware #DeepTech #EmergenceQuantum #Innovation#Podcast 📌 Disclaimer: This post is shared on a personal basis and I do notrepresent any company 

Inside Emergence Quantum: Engineering at the Edge ofCold ComputingIn the first part of my deep-dive interview, I sit down with Thomas Ohki, CTO andco-founder of Emergence Quantum.We explored how innovation really happens and why the future of computing might be colder than we think.Most people see quantum as a story about qubits.Thomas explains that the real breakthroughs might come from everything around them: the architecture, the materials, the cooling, the engineering.Before founding Emergence Quantum, Thomas worked at the frontiers of physics at Raytheon BBN and Microsoft Quantum.He brings a rare mix of scientific depth and hands-on engineering.What stood out most to me was his view on building and learning.“If you want to build something truly new, you have to accept that failure is part of the process. The goal isn’t to avoid mistakes, it’s to learn from themfaster than anyone else.”💡 Key insights from Part 1🧊 Cryogenic technology could redefine performance inAI and quantum systems⚙️ Engineering at cryogenic temperatures is essentialfor scaling quantum computers🧠 Innovation depends as much on mindset as onphysics🎧 Listento Beyondthe Qubit – Part 1: Emergence Quantum (Deep Dive)https://youtu.be/mWU-oPYtDSo  #BeyondTheQubit#QuantumComputing #AIHardware #CryoCMOS #DeepTech #EmergenceQuantum #Innovation#Podcast 📌 Disclaimer: This post is shared on a personal basis and I do notrepresent any company

The Next Tectonic Shift in Compute MightNot Be Quantum, It Might Be ColdEveryone is talkingabout GPUs, AI, and compute power.But what if the next revolution isn’t about more energy, it’s about less? In the new episode of Beyond theQubit, I sit down with Thomas Ohki, CTO of Emergence Quantum a startup born from the Microsoft Quantum team that’s now pioneering a newfrontier: cryo-CMOS and cryogenicelectronics.These are the systems that bridge today’s classical chips with tomorrow’s quantum processors,the connective tissue between the hot and the cold, the digital and the quantum.And here’s the crazypart:❄️ They built itwithoutexternal capital.No VC rounds. Nohype cycles. Just science, conviction, and partnerships.We talked about: Thomas saidsomething that stuck with me:“Research isn’t engineering. It’s learning to fail productively.”This episode will make you rethink what “compute” really means in a post-AI world.🎧 Watch or listen here → https://youtu.be/R_852Ar-HRw  #BeyondTheQubit#QuantumComputing #DeepTech #AIHardware #CryoCMOS #Semiconductors #Innovation#Podcast 📌 Disclaimer: This post is shared on a personal basis and I do notrepresent any company

The future of quantum is already touching patientsIn Part 2 of my deep-dive with MatthiasKaiser, CEO of Pex Machina, we explore how quantum simulationis changing drug discovery and why the next medical breakthroughs might comefrom software, not the lab.Pex Machina’smission is simple: help companies develop better cancer treatments.Quantum simulation is the tool that makes it possible, cutting the time, cost, and risk of new drug development.What impressed memost is how Matthias thinks about execution.He built a team that combines deep domain knowledge from pharma with real quantum expertise.They move fast, learn from every iteration, and build proof instead of PowerPoints.Matthias said something that stayed with me:“We’re not building technology for technology’s sake. We’re building tools that solve real problems.”What stood out in this part of our conversation:💡 Why precision medicine needs better simulationtools to scale🧠 How pharma collaboration is about access andtrust, not just algorithms🚀 Why execution speed and real-world validationmatter more than hype🎧 Listento Beyondthe Qubit – Pex Machina (Deep Dive Part 2)👉 [Insert Spotify or YouTube link]https://youtu.be/KN3LWO07Ol8  #BeyondTheQubit#QuantumComputing #PrecisionMedicine #DeepTech #Innovation #PexMachina #AI#DrugDiscovery Please do not forget to check out the presentationfrom Matthias (CEO of Pex Machina)https://drive.google.com/file/d/18LwU4QNtl3nRDAIcPWQ9kb2VLintVQEY/view?usp=drive_link 📌 Disclaimer: This post is shared on a personal basis and I do notrepresent any company

Building the bridge between quantum and real-worldimpactIn Part 1 of my deep-dive with MatthiasKaiser, CEO of Pex Machina, we talked about what it reallytakes to turn quantum research into something that matters.Matthias started out as a physicist but spent years working in business development before launching his startup. That mix of science and sales shaped how he looks at innovation.He learned early that many quantum companies start with the technology and then go searching for a problem.Pex Machina flips that logic. They start with real problems in oncology and use quantumsimulation as a tool, not as the story.Matthias put it simply:“We don’t want to push quantum computing. We want to solve problems that matter.”What I liked most about this conversation:💡 How sales experience helps founders stay groundedin what customers actually need🧠 Why quantum simulation could change how we developcancer treatments🚀 Why the real challenge in deep tech isn’t science,it’s product-market fit🎧 Listento Beyondthe Qubit – Pex Machina (Deep Dive Part 1)👉 YouTube link: https://youtu.be/mvHWPsyjF9A#BeyondTheQubit#QuantumComputing #PrecisionMedicine #DeepTech #PexMachina #Innovation #Podcast Please do not forget to check out the presentationfrom Matthias (CEO of Pex Machina)https://drive.google.com/file/d/18LwU4QNtl3nRDAIcPWQ9kb2VLintVQEY/view?usp=drive_link 📌 Disclaimer: This post is shared on a personal basis and I do not represent any company

In the latest episode of Beyond theQubit, I spoke with Matthias Kaiser, CEO of Pex Machina.What struck me most is how focused he is on solving real problems instead of chasing thehype around quantum computing.Pex Machina develops quantumsimulations for precision medicine, helpingpharmaceutical companies design better cancer treatments faster.Matthias started as aphysicist but spent years in business development before launching his startup.That combination gives him a rare perspective — one that blends deep sciencewith commercial discipline.He put it simply:“Our goal isn’t to push quantum computing. It’s to solve problems that matter topatients.”What I liked mostabout this conversation:💡 Quantum simulation as a bridge between AI,chemistry, and pharma🧠 The role of precision medicine in making cancertreatment more personal🚀 How real-world traction matters more thantheoretical breakthroughs #BeyondTheQubit#QuantumComputing #PrecisionMedicine #DeepTech #PharmaTech #Innovation#PexMachina #Podcast Please do not forget to check out the presentationfrom Matthias (CEO of Pex Machina) https://drive.google.com/file/d/18LwU4QNtl3nRDAIcPWQ9kb2VLintVQEY/view?usp=drive_link 📌 Disclaimer: This post is shared on a personal basis and I do not represent any company

🚀 Quantum security is no longer a future problemIt’s here. Today.When I sat down with Olga Mamlyga, co-founder of Quantum Scout Apps, one insight kept echoing:“Hackersdon’t follow the rules. If you only protect one layer, they’ll find another.”Olga is building a 7-layer quantum-secure network that protects every layer of the OSI stack: from physicalfiber to application logic.Not in ten years. Now.🔑Key takeaways fromthe conversation:• Quantum KeyDistribution (QKD) isn’t enough,  itcovers a single layer and is too costly for large-scale rollout.• Quantum threatslike store-now-decrypt-later mean thatdata stolen today can be cracked by tomorrow’s quantum computers.• Criticalinfrastructure: banking, power grids, hospitals,  needs protection before quantum decryption is practical.• Even cryptonetworks are far less secure than we assume.💡Why it matters for investors & builders  If you care aboutwhere deep tech meets national security,this episode is a wake-up call.Let’s make quantumsecurity visible  before the threat is visible to everyone else.#BeyondTheQubit#QuantumComputing #Cybersecurity #QuantumNetworks #StoreNowDecryptLater#DeepTech Please make sure tocheck Olga her presenation:  https://drive.google.com/file/d/1hN5a38MUbDyvP7CAobwNu1C1lnAzFq8n/view?usp=sharing 📌 Disclaimer: This post is shared on a personal basis and I do notrepresent any company.  #BeyondTheQubit#QuantumSecurity #CyberSecurity #DeepTech #StoreNowDecryptLater

🔒 The next cyber-attack won’t wait for quantum computers.On the new Beyond the Qubit I sat downwith Olga Mamlyga, co founder of Quantum Scout Apps.Her team builds physics-based quantum networks that secure all 7 layers of the internet stack far beyond what post-quantum cryptography can cover.“Hackersdon’t follow the rules. If you protect only one layer, they’ll simply go aroundit.”Why it matters now• “Store-now,decrypt-later” theft is already happening.• Nation-stateactors are probing grids, hospitals, and crypto networks today.• True QuantumNetworks can roll out on existing fiber tomorrowno decade-long wait.Quantum security isthe new digital backbone.How many layers ofyour organization are really safe? #BeyondTheQubit#QuantumSecurity #CyberSecurity #DeepTech #StoreNowDecryptLater Please make sure tocheck Olga her presentation: https://drive.google.com/file/d/1hN5a38MUbDyvP7CAobwNu1C1lnAzFq8n/view?usp=sharing 📌 Disclaimer: This post is shared on a personal basis and I do not represent any company.

Please don't forget to check out the presentation from Olga: https://drive.google.com/file/d/1hN5a38MUbDyvP7CAobwNu1C1lnAzFq8n/view?usp=sharingQuantum cybersecurityisn’t 10 years away it’s happening now.Jump to a section thatinterests you:⏩Chapters00:00 Introduction –Quantum cybersecurity is already here02:10 Meet OlgaMamlyga and True Quantum Networks (TQN)05:30 From Ukraine toquantum: Olga’s journey into deep-tech security09:15 Why “store now,decrypt later” attacks are an urgent threat13:40 PQC vs QKD –what every decision-maker must understand18:05 Defending all 7OSI layers with quantum networking23:20 Hybrid AI +quantum cyberattacks: the next big risk27:45 Deployingquantum networks today – practical steps and timelines32:30 Building aquantum-secure ecosystem for critical infrastructure37:10 Investoroutlook: the business case for quantum cybersecurity41:55 Final insights& key takeaways💬 What’s your view?• Will post-quantum cryptography (PQC) be enough, ordo we need quantum key distribution (QKD)now?• How urgent is the “store-now, decrypt-later” threat for yourindustry?👇 Share your thoughts, like the video, and subscribefor more deep-tech conversations on quantum computing, quantum networks, and criticalinfrastructure security.#QuantumCybersecurity#TrueQuantumNetworks #BeyondTheQubit #PQC #QKD #QuantumComputing#CriticalInfrastructure #AI #CyberSecurity #StoreNowDecryptLater📌 Disclaimer: This post is shared on a personal basisand I do not represent any company.

Beyond the Qubit – Deep Dive with Amir Naveh,Co-Founder & CPO of Classiq (Part 2)Quantum computingwon’t succeed without a software layer that bridges research and hardware. InPart 2 of my conversation with Amir Naveh,co-founder and Chief Product Officer at Classiq,we dive into how software abstraction is unlocking real-world applications.From derivative pricing with banks to thedevelopment of a quantum copilot, Amirexplains why Classiq is becoming the “Microsoft moment” for quantum — creatingthe layer that will make hardware truly useful.💡What you’ll learn inPart 2:• Where Classiq fits in the quantum software stack• Why abstraction is critical for building scalable quantum applications• Real-world use cases: finance, optimization, and derivative pricing• How Classiq integrates with IBM, IonQ, Quantinuum, Rigetti, NVIDIA, and others• The role of AI in building the first quantum coding assistant• Who will win in quantum: integrated hardware/software players or specialized software leaders?🌍Why it matters:Just as Microsoftunlocked the PC revolution, Classiq is building the software foundation thatcould define the next tectonic shift in computing.🎧 Listen to Part 2 now on Spotify, YouTube, or yourfavorite podcast app.#QuantumComputing#Classiq #BeyondTheQubit #QuantumSoftware #Investing #DeepTech #VentureCapital

Beyond the Qubit – Deep Dive with Amir Naveh, Co-Founder & CPO of ClassiqQuantum computing won’t reach its potential without scalable software. In Part 1 of this deep dive, I sit down with Amir Naveh, co-founder and Chief Product Officer of Classiq, the world’s leading independent quantum software company.From building satellites in Israel’s defense industry to raising $170M for Classiq, Amir shares the lessons, beliefs, and breakthroughs shaping the next wave of quantum.💡 What you’ll learn in this episode:• How Amir’s background in the Israeli defense & space sector shaped his vision• Why software is the true bottleneck in quantum computing• How Classiq enables scalable programming beyond manual gate-level coding• The founding story: from Talpiot, to IBM Quantum, to launching Classiq during COVID• Why investors should pay close attention to the software layer of the quantum stack🌍 Why it matters:Just like Microsoft unlocked the PC era, Classiq aims to create the software layer that makes quantum computers useful for finance, pharma, logistics, and beyond.🎧 Available now on Spotify, YouTube, and all major podcast platforms.#QuantumComputing #Classiq #BeyondTheQubit #QuantumSoftware #DeepTech #Investing #VentureCapital

Beyond the QubitSummary interview with Amir Naveh, Co-Founder & CPO of ClassiqQuantum computing won’t transform industries without powerful software. In this episode, I sitdown with Amir Naveh, co-founder andChief Product Officer of Classiq, theworld’s leading independent quantum software company.From buildingsatellites in Israel’s defense sector to raising $170Mfor Classiq, Amir shares the lessons, beliefs, and breakthroughs that areshaping the future of quantum.💡What you’ll learn inthis episode:🌍Why it matters:Just as operatingsystems unlocked the PC revolution, abstraction and automation in quantumprogramming will determine who wins the next wave of computing.🎧 Listen now on Spotify, YouTube, or your favoritepodcast app.#QuantumComputing#BeyondTheQubit #Classiq #QuantumSoftware #Investing #DeepTech #VentureCapital 📌 Disclaimer: This post is shared on a personal basis and does not represent any company

🔑 Logical Qubits, Machine Learning & the Future ofQuantum ApplicationsWhen will quantumcomputers truly impact society?In part 2 of Beyond the Qubit with Koen Groenland, we dug deeper into applications — and cleared up somemisconceptions along the way.💡 Key takeaways: Koen put it simply:“Quantumcomputers won’t replace classical ones. They’re special-purpose machines —powerful only for very specific problems.” 📖 Bonus: Don’t miss the presentation from Koen for a deeper dive intotheir vision and technology.   https://drive.google.com/file/d/17XnSkwlj1IWm3xZgsqyDx9u2ExSp5aGJ/view?usp=sharing  📌 Disclaimer: This post is shared ona personal basis and does not represent any company.@Koen Groenland#QuantumComputing #Quantum algorithms#BeyondTheQubit #Quantum Software #ScalingQuantum #VentureCapital  #Blockchain #Bitcoin  #Quantum #Error correction  🎧 Listen to the full conversation here: Youtube: https://youtu.be/ehubBArKRDs