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.

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.

Can neutral atoms turn physical scale into logical qubit quality?

What matters more in neutral atoms: how many atoms you can trap, or whether you can turn them into high-quality logical qubits at acceptable overhead?

In this episode, I continue my deep dive with Matt Kinsella, CEO of Infleqtion, to unpack what I think is the real neutral atom question. Infleqtion has already shown a 1,600 physical qubit array, which is impressive. But physical qubits are not the final scoreboard. Logical qubits are. That is why the real investor question is not just whether neutral atoms can produce large arrays, but whether those arrays can be converted into useful logical qubits efficiently.

This episode is for investors, founders, and anyone trying to understand how neutral atom systems may actually scale. We get into why the bottleneck is not adding more atoms, but scaling the optics, lasers, readout, control software, and error correction around them. That is why photonics, spatial light modulators, photonic integrated circuits, dual-species atoms, and qubit movement are not side details. They are part of the scaling architecture.

That is what makes this conversation so important. Neutral atoms may have a real advantage because the qubits are naturally identical, highly packable, and movable. But that advantage only matters if the platform can cross the line from physical qubit headlines to logical qubit quality.

💡 In this episode, we cover:

• Why logical qubits matter more than physical qubit headlines

• Infleqtion’s roadmap from 12 logical qubits to 1,000

• Why neutral atoms have a natural scaling advantage

• Why optics, lasers, and readout become the real bottlenecks

• Why spatial light modulators matter for scalable control

• How photonic integrated circuits could improve stability and scale

• Why cesium and rubidium together could support a dual-species approach

• Why movable qubits may matter for error correction and overhead

Chapters

00:00 Why logical qubits are the real metric

03:56 Infleqtion’s logical qubit roadmap

06:51 Why neutral atoms scale differently

10:15 Cesium, rubidium, and the dual-species approach

12:13 Why spatial light modulators matter

15:35 Photonic integrated circuits and better lasers

16:50 Why quality can improve with quantity

17:20 Why movable qubits matter for error correction

23:06 The biggest technical bottlenecks ahead

26:48 What investors should really 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.

What if one of the most important questions in quantum is not how many qubits a company can build, but whether it can finance the journey?

In this episode, I go deep with Matt Kinsella, CEO of Infleqtion, to explore why the company’s neutral atom strategy may look very different from the standard quantum computing playbook. Most companies are still judged on the long-term roadmap: more qubits, better gates, lower error rates, logical qubits, fault tolerance. All of that matters. But Infleqtion is trying to build more than a quantum computer. It is trying to build a quantum technology company.

This episode is for investors, founders, and anyone trying to understand how quantum companies may actually survive long enough to reach useful quantum computing. Neutral atoms are not only useful as qubits. They can also be used for clocks and sensors, which opens up nearer-term markets in precision timing, GPS resilience, RF sensing, and inertial sensing. That creates a possible commercial bridge before useful quantum computing fully arrives.

That is what makes this conversation so interesting. The question is not only whether Infleqtion can build a useful quantum computer. It is also whether clocks, sensors, and timing systems can create the revenue bridge that helps fund the much longer computing roadmap.

💡 In this episode, we cover:

• Why Infleqtion is building a quantum technology company, not only a quantum computing company

• Why neutral atoms can be used for clocks, sensors, and computing

• Why precision timing is becoming a resilience problem, not just a science problem

• How GPS jamming and spoofing create demand for better local timing

• Why nearer-term sensing revenue could help fund the longer quantum computing roadmap

• Why Matt compares the business logic to Nvidia’s path into larger markets

• What investors should watch when judging commercial traction versus scientific promise

• Why financing the road may matter as much as the roadmap itself

  
  

Chapters

00:00 Why investors should care about Infleqtion

01:04 Why neutral atoms matter beyond quantum computing

03:54 The Nvidia analogy and the revenue bridge

04:46 Matt Kinsella’s path from investor to CEO

12:49 Why capital is one of the biggest questions in quantum

19:07 What quantum technology can do better than classical systems

19:58 Why GPS timing is fragile

24:28 How Infleqtion’s quantum clocks work

36:03 How clocks, sensors, and computing connect

42:01 How photonics and scale could drive cost down

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.