Beyond the Qubit

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.