Beyond the Qubit

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.