Beyond the Qubit

Before a quantum scale-up raises its next €50 million, one question matters: what exactly needs to become true with that money? In Part 1 of my Beyond the Qubit conversation with Yvonne Greeuw from Invest-NL, we discuss how investors evaluate deep-tech companies beyond the technology itself. Yvonne explains how they think about investment theses, key milestones, commercial validation, and the progress a company needs to make before its next financing round. One idea stood out: do not blindly optimize for KPIs. A company can hit its targets and still be working on the wrong things. For quantum computing startups, the critical milestone might be proving manufacturability, securing a reference customer, or showing that a technology can solve a meaningful problem for end users. We also explore the Dutch quantum ecosystem, different quantum computing modalities, the transition from research to commercial products, and what investors look for as companies mature. For founders and investors, the challenge is not simply to measure progress. It is to identify which achievements will materially strengthen the investment case. 💡 In this episode, we cover: How Invest-NL evaluates deep-tech investment opportunities Why a strong investment thesis starts with identifying the real bottleneck What value inflection points mean for a company's next funding round Why founders should not optimize blindly for KPIs How quantum startups can move from technical progress to commercial validation Why qubit counts alone do not tell the full story How different quantum computing modalities affect investment decisions What changes when a startup moves from early-stage funding toward Series A and Series B Why investor conversations should focus on the risks that need to be removed How to think about the next €50 million as a set of milestones, not just more runway Chapters 00:00 - Meet Yvonne Greeuw from Invest-NL 02:00 - What Invest-NL does and how it evaluates companies 04:00 - Building an investment thesis around the real bottleneck 06:00 - Evaluating technology and the opportunity it creates 08:00 - Testing assumptions and validating the investment case 10:00 - The role of people, communication, and alignment 14:00 - Keeping founders and stakeholders aligned as things change 16:00 - When a company's original investment thesis needs to change 18:00 - Strengthening the investment thesis with evidence 20:00 - Invest-NL and the Dutch quantum ecosystem 24:00 - Building the quantum ecosystem around startups 26:00 - Evaluating different quantum computing modalities 29:00 - Learning from experts and market analysis 31:00 - Moving from research progress toward end-user relevance 34:00 - What changes as a company approaches Series B 36:00 - Technical challenges and the path toward useful quantum systems 38:00 - Quantum hardware, infrastructure, and scaling challenges 41:00 - What creates a durable competitive advantage 44:00 - Common mistakes founders make when speaking to investors 46:00 - Pitching as a commercial strategy 48:00 - Why rejection does not always mean the company is wrong 50:00 - Defining milestones across technology, commercial progress, and team 51:00 - Why founders should not blindly optimize for KPIs 52:00 - Preparing a company for institutional investment If you work in quantum computing, deep tech, or venture capital, share this episode with someone thinking about how to turn technical progress into a stronger investment case. Subscribe to Beyond the Qubit for more conversations about the technologies, companies, and investment decisions shaping the quantum industry. Disclaimer: This post is shared on a personal basis. I do not represent any company. This is not investment advice.

What could make a cryo-CMOS company difficult to replace, even if a competitor develops a technically better chip? In Part 2 of my Beyond the Qubit conversation with Eric W. Cao, Co-Founder and CEO of Qifrost, we explore what could separate the winners in the growing cryo-CMOS market. Beyond intellectual property and chip performance, the discussion turns to execution, hard-to-find engineering talent, customer qualification, and the challenge of getting cryogenic control technology out of the lab and into real quantum systems. We also discuss why the next 24 months could be important for identifying the leaders in cryogenic control electronics, how different quantum computing architectures shape demand, and what commercial progress might look like. For investors, the key question is whether a company can turn technical expertise into a solution that customers can deploy, trust, and keep using. 💡 In this episode, we cover: Why cryo-CMOS could become essential to scaling quantum computers What may differentiate Qifrost from foundries, instrument companies, and other competitors Why cryo-CMOS engineering talent is so difficult to find Why laboratory results are not enough to prove a commercial solution How quantum computing architectures influence cryogenic control requirements Why the next 24 months could reveal early market leaders The role of 4K electronics, multiplexing, packaging, and reducing fridge cabling Why engineering contracts, design agreements, and commercial milestones matter What investors should look for when evaluating progress in quantum hardware infrastructure Chapters 00:00 - Why Qifrost could win in cryo-CMOS 02:00 - The shortage of cryo-CMOS engineering talent 04:00 - What separates Qifrost from potential competitors 08:00 - Why lab validation is not enough 10:00 - Different technologies and the cryo-CMOS opportunity 12:00 - Which quantum computing architectures need cryo-CMOS 14:00 - Why market leaders could emerge in the next 24 months 16:00 - Multiplexing and scaling quantum control 18:00 - Platform agnostic technology and quantum modalities 20:00 - Quantum benchmarking and industry validation 22:00 - What the 4K control layer can do 24:00 - Hybrid bonding, packaging, and integration 25:00 - Why engineering contracts matter to investors 27:00 - What it takes to become a market leader 32:00 - Commercial milestones and proving progress 34:00 - Reducing cabling without sacrificing qubit fidelity If you follow quantum computing, cryo-CMOS, or deep-tech investing, share this episode with someone who would find it useful. Subscribe to Beyond the Qubit for more conversations about the companies, technologies, and business models shaping the quantum industry. Disclaimer: This post is shared on a personal basis. I do not represent any company. This is not investment advice.

The cryo-CMOS market may still look early. But the leaders may already be starting to emerge. In this episode, I explore my biggest takeaways from my Beyond the Qubit interview with Eric W. Cao, Co-Founder and CEO of Qifrost. Eric believes the scaling bottleneck that cryo-CMOS aims to solve could become critical around 2030, give or take a couple of years. But companies cannot wait until then to prepare. The next 24 months could be when the leaders begin to separate themselves from the rest of the market. This episode is for investors, founders, and anyone trying to understand where value may emerge in the quantum hardware supply chain. Cryo-CMOS aims to address the scaling challenges of controlling quantum processors at cryogenic temperatures. But laboratory validation is only the first step. The next challenge is proving that the technology works inside commercial quantum processing units, alongside real architectures and roadmaps. That is what makes Qifrost interesting. The progression is clear: laboratory validation, commercial validation, standardization, and eventually mass manufacturing. The race may not be won by the company with the most impressive laboratory demonstration, but by the one that integrates its technology with commercial quantum hardware first. 💡 In this episode, we cover: Why cryo-CMOS could become a critical quantum scaling technology Why the next 24 months may help determine the market leaders How cryogenic control electronics could reduce wiring complexity Why laboratory validation is no longer enough How commercial validation differs from proving technology in the lab Why co-development with quantum processor companies matters How standardization and mass manufacturing fit into the roadmap What investors should watch as Qifrost moves toward commercial adoption Chapters 00:00 Why Qifrost matters for quantum investors 01:00 Why existing quantum control infrastructure will not scale 02:30 Why the next 24 months matter for cryo-CMOS 04:30 From laboratory validation to commercial validation 11:00 The physical scaling bottleneck in quantum hardware 36:30 Why cryo-CMOS is a strategic opportunity 43:00 Why commercial execution matters more than the technology alone 65:30 Building relationships with quantum hardware developers 72:30 What it takes to prove cryo-CMOS commercially 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.

The most important scaling unit in quantum may not be the qubit. It may be the tile. In this episode, I continue my conversation with James, CEO of Quantum Motion, to explore why the company’s tile architecture could change how investors think about quantum scaling. Most quantum discussions start with qubit count. But the semiconductor industry taught us a different lesson: scaling happens when you prove the repeating building block and then replicate it. That is the idea behind Quantum Motion’s tile architecture. A tile is not just a place where qubits sit. It is designed as a functional unit containing the key ingredients needed for a scalable architecture: shuttling, readout, single-qubit operations, and interfaces for interactions between neighbouring tiles. This episode is for investors, founders, and anyone trying to understand why manufacturability may matter more than impressive qubit numbers. The question is not only whether Quantum Motion can make more qubits. It is whether they can prove a manufacturable tile that can be repeated at scale. Because if the tile works, the scaling problem changes. It moves from invention risk toward replication risk. 💡 In this episode, we cover: Why the tile may be more important than raw qubit count How semiconductor scaling principles apply to quantum computing Why a repeatable building block changes the scaling challenge What Quantum Motion’s tile architecture contains Why manufacturing yield and control are critical How shuttling and readout fit into the architecture Why the TV pixel analogy helps explain quantum scaling What investors should watch beyond qubit numbers Chapters 00:00 Why qubit count may not be the right metric 00:41 Manufacturing yield as a key KPI 02:37 Why control is the real bottleneck 05:26 The quantum tile architecture explained 17:12 The TV pixel analogy for quantum scaling 27:32 Shuttling, readout and the functional tile 31:00 Why replication could change the scaling equation 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.

The real spin-qubit question is not: Can you make one? It is: Can you control millions? In this episode, I explore my biggest takeaways from my Beyond the Qubit interview with James, CEO of Quantum Motion. Spin qubits have one of the most interesting scaling stories in quantum because they build on something the semiconductor industry already understands: CMOS manufacturing. But CMOS does not make quantum easy. It changes where the hard problem sits. Instead of removing complexity, spin qubits move the challenge toward control, tuning, automation, and manufacturing. A single electron spin can hold quantum information, but scaling that into a useful quantum computer means controlling millions of slightly different qubits with high precision. This episode is for investors, founders, and anyone trying to understand why semiconductor-style manufacturing could matter in quantum. Quantum Motion’s approach is built around making qubits fit the factory from day one. The question is not only whether spin qubits work. It is whether they can become manufactured systems. Density creates the opportunity. Control determines whether it becomes a computer. 💡 In this episode, we cover: Why spin qubits are attractive from a semiconductor perspective How CMOS manufacturing changes the quantum scaling challenge Why millions of qubits create a control problem Why manufacturing discipline matters for quantum computers How automation and scalable readout become critical Why Quantum Motion started with a 300mm CMOS approach Why density alone does not create a useful quantum computer What investors should watch in spin-qubit platforms Chapters 00:00 Why Quantum Motion and spin qubits matter 01:12 Why CMOS is the foundation of the strategy 03:18 Why semiconductor manufacturing creates an advantage 05:10 The challenge of manufacturing quantum devices 11:12 Why controlling millions of qubits is the real problem 12:17 Why scalable readout matters 17:34 Why yield and automation determine success 27:34 Why 300mm CMOS matters for quantum scaling 31:28 From individual qubits to scalable systems 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.

The quantum technology worked. Then the real problems started. In this episode, I continue my conversation with Ingrid Romijn from Falqon® Systems to explore what happens when deep tech moves from demonstration into real-world deployment. One of my biggest takeaways was that the hardest problems often appear after the technology works. For Falqon®, the challenge was no longer simply whether quantum key distribution works. The challenge became integration: how do you connect quantum security technology with existing infrastructure, telecom networks, fibre providers, and customer security systems? This episode is for investors, founders, and anyone trying to understand how deep-tech companies move from technology development to commercial adoption. Real customers create real constraints. Government deployments push reliability requirements. Telecom customers push deployability. Enterprise users demand visibility into performance and operations. That customer feedback loop is where companies mature. The strategic question is not only whether a technology works. It is: How quickly can each customer make the next deployment easier? 💡 In this episode, we cover: Why real-world deployment creates new bottlenecks after the technology works What Falqon® Systems learned from customer deployments How the Port of Rotterdam exposed integration challenges Why quantum security needs to work with existing infrastructure How customer requirements shaped the Quantum Domain Controller Why telecom customers care about deployability and operations Why early customers become part of the innovation engine How field experience can become a long-term competitive advantage Chapters 00:00 Why deployment creates new challenges 01:27 The Port of Rotterdam customer lesson 02:50 Integrating quantum keys into existing infrastructure 12:16 Learning from early customer deployments 15:13 Building the Quantum Domain Controller 31:13 What customers need to trust the system 34:00 Why customer feedback creates a competitive moat 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.

Europe is focused on which cryptography to use. But the bigger question may be: Who controls it? In this episode, I explore my biggest takeaways from my conversation with Ingrid Romijn of Falqon® Systems and why post-quantum security is not only a cryptography question. Moving from vulnerable cryptography to quantum-safe solutions does not automatically create technological sovereignty. The question is also who controls the implementation, key management, orchestration, and security infrastructure around it. This episode is for investors, founders, and anyone trying to understand the emerging quantum security market. Ingrid explains why the future may not be about choosing only one technology. A layered approach combining post-quantum cryptography, QKD where appropriate, and existing security infrastructure may become the more realistic path. That creates a new strategic layer: the control plane. As quantum security becomes more complex, the companies that can manage and orchestrate different technologies may become just as important as the companies building individual components. The investor question is not only: Who creates the cryptography? It is also: Who controls how the entire security stack works together? 💡 In this episode, we cover: Why quantum security is also a sovereignty question Why adopting post-quantum cryptography does not automatically remove dependencies How PQC, QKD, and existing cryptography can work together Why key management and orchestration could become strategic layers How Falqon® Systems is moving beyond QKD hardware Why control planes may become valuable in quantum security The role of European technology independence Where investors should look for value in the quantum security stack Chapters 00:00 Why quantum security is a sovereignty question 02:08 Why Europe matters in the quantum security landscape 04:28 Building infrastructure for quantum-safe communications 08:20 Why Falqon moved beyond QKD hardware 09:08 Combining QKD, PQC, and PKI 11:41 Why the control plane matters 18:13 The future of quantum key management 29:10 Why orchestration may become the strategic layer 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.

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 architecture Why transduction is the interface problem between quantum computers and networks How quantum switches differ from classical switches Why different quantum modalities create a connectivity challenge How Cisco’s universal quantum switch approaches translation between encodings Why ecosystem collaboration matters for quantum networking Why the network layer could become a valuable control point What investors should watch as quantum infrastructure develops Chapters 00:00 Why transduction matters in quantum networking 01:35 Why quantum needs a network layer 02:39 The NIC card analogy for quantum 03:24 Why transduction is harder than classical networking 04:51 Why quantum networks cannot inspect information 07:05 Building a universal quantum switch 08:45 Translating different quantum encodings 10:48 Why ecosystem collaboration matters 12:47 Keeping the network simple and complexity at the edge 14:48 Why transduction could become a strategic control point 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.

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 architecture Why quantum information cannot be inspected like classical packets How quantum switches and entanglement sources could enable scaling Why networking between QPUs may become a major bottleneck Why Cisco is focusing on the infrastructure layer around quantum computers How photonics and existing fiber infrastructure could improve deployability Why quantum networks may also connect sensors and shared resources What investors should watch in the quantum networking stack Chapters 00:00 Why Cisco is exploring quantum networking 00:50 Why quantum needs a network layer 03:22 Quantum clusters and the need for connectivity 04:47 What quantum networking actually means 05:23 The quantum switch challenge 08:01 Why classical networking cannot be copied 10:03 Why quantum networks cannot inspect information 12:29 Building networks without destroying quantum states 14:42 Why Cisco focuses on photonics and fiber 17:39 Connecting quantum computers and sensors 27:14 The current state of quantum networking 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.

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.

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.

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.

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.

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.

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 Chapters 00:00 Introduction to QuiX Quantum and photonic computing 01:56 Why silicon nitride matters for photonic quantum computing 04:18 From photonic processors to quantum systems 10:00 Why integration is the real challenge in quantum 11:51 Building the full photonic quantum stack 15:38 Why early customers accelerate deep tech learning 17:17 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.

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.

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.

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 quantum Why cooling architecture matters as systems become larger and more complex How kiutra’s X-Type changes the mental model of cryogenic infrastructure Why separating cooling from the quantum payload could improve deployment and upgrades Why future systems will need cooling at multiple temperature stages, not only one extreme cold point How modular cooling could scale from microwatts to hundreds of microwatts and beyond Why shipping, installation, and integration may become real bottlenecks Why deployable infrastructure may become just as important as the qubit roadmap itself Chapters 00:00 Magnetocaloric cooling basics 20:43 Why X-Type is a different architecture 21:20 Why integrated cryostats do not scale well 22:48 Separating cooling from the quantum payload 24:14 Why modular cooling matters 25:20 Zero helium-3 and 20 millikelvin 26:00 Scaling cooling power with modules 42:46 From 1 microwatt to 20 microwatts and beyond 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.

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 bottleneck Why cryogenic cooling is part of the scaling problem How helium-3 dependence creates a supply chain risk What magnetocaloric cooling is and why kiutra uses it Why faster testing can compress the quantum learning cycle How throughput and feedback speed affect iteration and yield learning Why failed wire bonds and slow cool-downs are more costly than they look Why the fastest learner may gain the biggest advantage in quantum Chapters 00:00 Why investors should care about kiutra 00:58 The helium-3 problem in quantum cooling 03:05 Magnetocaloric cooling explained 03:38 Alexander Regnat’s background and kiutra’s origin 35:47 Why testing and qualification matter so much 36:41 Why traditional dilution fridges slow the learning cycle 38:41 How kiutra cuts interaction time to minutes 39:42 Why faster feedback changes quantum R&D 45:51 The 3 to 10x throughput advantage 46:25 Why the fastest learning system may win 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.

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 size Why neutral atoms still have a strong scaling advantage Why photonics, lasers, and vacuum systems are central to the roadmap How readout connects physical arrays to logical qubit quality Why quantum sensing deserves more investor attention How clocks, RF systems, and inertial sensing could become earlier markets Why GPS resilience makes sensing more than a niche science story The two investor questions that matter most for Infleqtion Chapters 00:00 The two biggest investor takeaways 01:00 Why sensing could become a revenue bridge 02:23 Infleqtion’s ambitious logical qubit roadmap 03:03 Neutral atoms explained simply 05:24 Why control and readout matter more now 05:37 Why sensing changes the business model 06:48 GPS jamming, spoofing, and why sensing matters 10:12 Why photonics and lasers matter for logical qubits 12:43 Integrated photonics and scaling the control stack 16:50 The two questions investors should watch 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.