Beyond the Qubit

Frank Dekker

The nr1 Quantum Technology podcast for investors.

  1. 2 天前

    Why quantum testing needs hardware, software and data to work together

    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 workflows How OrangeQS MAX combines hardware infrastructure for quantum chip testing Why OrangeQS built Juice instead of relying on simple lab scripts How software enables repeatable, traceable, multi-user testing Why testing becomes inseparable from manufacturing at scale How measurement data can become manufacturing insight Why integrated systems may create stronger moats than individual products The importance of data governance in quantum manufacturing Chapters 00:00 Why quantum testing needs a complete system 01:50 What OrangeQS is building for quantum chip testing 08:10 Why testing and manufacturing need feedback loops 14:40 Turning test data into manufacturing learning 29:33 Why OrangeQS Juice became a hidden gem 30:54 Moving from lab scripts to scalable workflows 35:00 Hardware, software, data and domain knowledge together 42:15 The future of quantum manufacturing infrastructure Share 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.

  2. 8月21日

    Deep-Tech Founders Need Procurement-Market Fit

    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 fit Why expensive hardware requires a different sales approach How OrangeQS learned to navigate CapEx decisions and procurement Why technical validation does not automatically lead to purchases How partnership programs can reduce customer risk Why customers need a buying process they can defend internally How deep-tech founders can turn interest into purchase orders Why adoption is often as difficult as the technology itself Chapters 00:00 Why deep tech needs procurement-market fit 08:34 Why million-euro hardware changes the buying process 17:32 Building a partnership program to reduce risk 22:19 The hidden challenge of CapEx decisions 40:29 Why procurement can make or break adoption Share 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.

  3. 8月14日

    Why modular quantum computing creates a new software problem

    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 challenge Why more qubits do not automatically mean more useful computation How inter-chip connections can create new fidelity bottlenecks Why algorithms need to understand quantum hardware topology Why hardware-aware software may matter more than hardware-agnostic approaches today How Kipu thinks about extracting value from imperfect quantum systems Why scaling quantum hardware creates new algorithmic challenges What investors should watch in quantum software companies Chapters 00:00 Why Kipu Quantum focuses on hardware-aware software 01:29 Why algorithms must adapt as hardware changes 03:26 Why architecture matters in quantum systems 06:46 Fidelity limits inside quantum architectures 08:03 Why modularity changes the algorithm 09:01 Building quantum systems beyond single chips 15:04 Why inter-chip connections create new challenges 15:21 Why small fidelity differences matter 16:30 Why algorithms must know the machine Share 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.

  4. 8月7日

    Can quantum software create value before perfect hardware arrives?

    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 first Why current quantum limitations should become design constraints How hardware-aware algorithms can extract more value from existing machines Why Kipu focuses on industrial problems and customer needs How algorithm compression can reduce the requirements for quantum hardware Why hybrid quantum-classical workflows may matter before fault tolerance Why quantum software companies may create value earlier than expected What investors should watch in quantum software businesses Chapters 00:00 Why Kipu Quantum matters for investors 01:10 Building value with today’s quantum hardware 03:33 Why Kipu does not wait for fault tolerance 04:15 Starting from hardware, not use cases 05:59 Finding industrial problems quantum can solve 06:59 Customer applications and commercial validation 08:56 Why current hardware is still worth using 28:57 Why Kipu rejects blaming the hardware 31:27 Accepting reality conditions in quantum 38:05 Hardware-aware algorithms and compression 50:53 Why software must adapt as hardware improves Share 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.

  5. 7月31日

    Why photonic quantum computing needs better photons

    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 quantity Why indistinguishability is the key challenge behind photon errors How photon distillation can reduce overhead before errors compound Why better physics can translate into better economics How error reduction affects hardware requirements and scaling costs Why QuiX is focused on difficult engineering problems early What feed-forward and system integration mean for photonic quantum systems The investor question: can photonic quantum computing scale economically? Chapters 00:00 QuiX’s latest progress in photonic quantum computing 09:40 Why photon quality matters for scaling 10:09 Loss versus indistinguishability explained 14:51 How photon distillation reduces overhead 17:30 Why better photons improve quantum economics 20:30 The challenge of building scalable photonic systems 22:00 Delivering photonic quantum computers to customers 24:00 The future of photonic quantum computing Share 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.

  6. 7月24日

    Why quantum’s biggest moat may be systems integration

    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 systems Why systems integration could become a major quantum moat How photonic quantum computing differs from other approaches Why low-loss photonic chips are strategically important Why packaging, detectors, software, and control systems matter together How early customers help deep tech companies mature faster Why investors should look beyond individual components Chapters00:00 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 advantage Share 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.

  7. 7月17日

    Why quantum needs a trust layer before enterprises can scale

    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 verification Why provider claims are not enough for enterprise adoption How SoftQuantus approaches quantum orchestration and control What QCOS is designed to solve across fragmented quantum platforms Why telemetry, traceability, and reliability scoring matter How quantum backends differ across hardware modalities Why certification and compatibility layers could become strategically important Why trust infrastructure may become a valuable layer in the quantum stack Chapters 00:00 Why quantum needs a trust layer 00:48 SoftQuantus and the quantum control layer 02:00 Why quantum needs orchestration and traceability 04:20 Building a unified quantum operating system 08:30 Connecting quantum systems with enterprise workflows 11:24 Why benchmarking quantum hardware is difficult 17:19 Creating trust through measurement and scoring 22:00 Why enterprises need vendor comparison 27:30 The future of quantum interoperability Share 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.

  8. 7月10日

    Why cryogenic cooling may be a strategic bottleneck in quantum

    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 thought Why testing speed can become a major bottleneck in quantum hardware Why faster cooling and reloading can accelerate the learning cycle Why the heat budget becomes critical as systems scale Why microwatts matter more than most people realize Why helium-3 creates a supply chain and sovereignty question Why cooling also matters for other modalities beyond superconducting and spin qubits Why modular cooling architecture could matter as systems become larger and more deployable Chapters 00:00 What changed in my thinking after Kiutra 00:35 Why cryogenic cooling is essential 00:59 Why testing speed is a real bottleneck 01:52 Why the heat budget matters so much 03:10 Why cooling also matters beyond superconducting qubits 04:17 Helium-3 scarcity and sovereignty risk 05:17 How Kiutra’s magnetic cooling works 06:43 Why faster testing changes the learning cycle 07:43 Heat budget explained in simple terms 10:43 Where helium-free cooling really starts Share 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.

簡介

The nr1 Quantum Technology podcast for investors.

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