Beyond the Qubit

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.