Your host, Sebastian Hassinger, interviews brilliant research scientists, software developers, engineers and others actively exploring the possibilities of our new quantum era. We will cover topics in quantum computing, networking and sensing, focusing on hardware, algorithms and general theory. The show aims for accessibility - Sebastian is not a physicist - and we'll try to provide context for the terminology and glimpses at the fascinating history of this new field as it evolves in real time.
Site
RSS
Apple
Data updated on 27/10/2026
Recent rankings
Latest chart positions across Apple Podcasts and Spotify rankings.
Shared links between episodes and podcasts
Links found in episode descriptions and other podcasts that share them.
Atomic-Precision Silicon Qubits and the Path to Fault Tolerance with Michelle Simmons
Episode 116
Monday, October 26, 2026 • Duration 38:24
Michelle Simmons is the founder and CEO of Silicon Quantum Computing (SQC) and a Scientia Professor at UNSW Sydney. She is one of the few people in the field who has simultaneously run a 200-person academic research centre, founded Australia's first vertically integrated quantum computing company, shaped how the community publishes its results (as inaugural editor-in-chief of npj Quantum Information), and influenced national quantum policy. That combination of roles gives her an unusually grounded view of what is actually hard — in the physics, in the manufacturing, and in the business.
The conversation is happening at a specific inflection point. SQC published two landmark Nature papers within ten weeks of each other in late 2025 and early 2026 — an 11-qubit silicon processor with two-qubit gate fidelities reaching 99.99%, and a 15,000-site analogue simulator demonstrating a metal-insulator transition. The company has advanced to Stage B of DARPA's Quantum Benchmarking Initiative, a program whose stated opening position is skepticism that fault-tolerant quantum computing is achievable by 2033. And SQC is now shipping an analog quantum AI chip to commercial customers. This episode is for anyone who wants to understand what silicon spin qubits actually are, why the donor-qubit approach is architecturally distinct from the CMOS-foundry approaches most competitors use, and what a credible long-range quantum roadmap looks like when it is grounded in manufacturing reality rather than theoretical aspiration.
What We Get Into
Why qubit quality improves with scale in SQC's system — the physical intuition behind using phosphorus donor atoms as "nature's perfect qubit," and why minimizing interfaces is the key insight that separates this approach from gate-defined quantum dots
The STM as a manufacturing tool, not just a microscope — how Simmons reframed the scanning tunneling microscope as the atomic-scale equivalent of an ASML stepper, and why 25 years of development now yields half a million registers in half a day at sub-nanometer precision
What the 15,000-site analogue simulator actually demonstrated — the distinction between analogue simulation and gate-model computation, what a metal-insulator transition tells you about correlated quantum matter, and why this device is also a tool for validating the gate-model processor as it scales
Six qubits per lead and all-to-all connectivity — how hyperfine frequency selection lets SQC address multiple nuclear spin qubits through a single electrical lead, and why that matters for error correction overhead
The eight-stage roadmap, set in 2014, and how it has held up — Simmons mapped her milestones onto the 1947–1981 arc of classical semiconductor development; two of the three near-term milestones came in ahead of schedule
DARPA QBI Stage B: what it validates and what it doesn't — why Simmons describes the program as "gold for a company" and how external scrutiny is forcing SQC to solve operating-environment questions earlier than planned
Quantum reservoir computing as a near-term product — how SQC's analog AI chip is already in customers' hands, cutting one communications company's training time from three weeks to two days, and why this is a bridge to the fault-tolerant era rather than a detour from it
Why you cannot outsource quantum manufacturing — Simmons's argument, drawn from decades of hands-on fabrication, that the teams who treated manufacturing as a commodity have paid a steep price
The hype question, answered by someone who has lived it — as a journal editor, APS division chair, and company CEO, Simmons has a rare vantage point on where the field's communication problems actually originate
What "fault tolerant" will need to mean in 2033 — and why the definition of that phrase is going to require as much parsing as the hardware milestone itself
Resources & Links
Guest & Company
Silicon Quantum Computing (SQC) — SQC's official site; the news feed covers all major 2025–2026 milestones including the DARPA Stage B announcement and commercial partnerships
"Nature's perfect qubit." Simmons's phrase for the phosphorus donor atom in silicon: the qubit state forms naturally when the atom is placed, without complex materials engineering, in an environment where the silicon matrix acts as the vacuum does for trapped ions.
"Every atom counts." SQC's company motto, and a literal description of their manufacturing philosophy — because they know the position and behavior of every atom in every device, every chip is a learning opportunity.
> "I've never really seen...
Building a Regional Quantum Ecosystem with Corey Stambaugh
Episode 115
Monday, October 19, 2026 • Duration 30:18
Corey Stambaugh is one of the few people in the quantum world who has worked at the bench, inside federal policy, and now as the operational lead of a major public-private ecosystem initiative. He came up as a precision measurement physicist at NIST, moved to the White House's National Quantum Coordination Office as a senior policy advisor, and now directs the Capital of Quantum — Maryland's statewide effort to translate decades of university research into a commercial quantum industry cluster anchored in the Discovery District at the University of Maryland.
This conversation was recorded at the close of the 2026 Quantum World Congress, a week in which Maryland opened the Microsoft Quantum Research Center, announced Riverlane's U.S. headquarters in the Discovery District, welcomed QuEra Computing into the building, and unveiled a new partnership with Paladin Capital Group. If you follow quantum economic development, workforce policy, or the question of which regions are actually building durable quantum infrastructure versus generating press releases, this episode gives you a rare inside view from someone threading all of it together.
What We Get Into
How the Capital of Quantum actually came together — the decades of university research that preceded the January 2025 launch, and what it took to stand up a formal initiative on top of that foundation
What "150,000 square feet and a fridge at 14 millikelvin" means in practice — the Discovery Center build-out, from Microsoft buying the building to pumping down their dilution refrigerator in under a year
Why the tenant list spans almost every major hardware modality — IQM (superconducting), Quantum Motion (silicon CMOS), Microsoft (topological), IonQ (trapped ion), QuEra (neutral atom), and Riverlane (error correction software) are all in or adjacent to the same building, and whether that's strategy or circumstance
The DARPA Capital Quantum Benchmarking Hub — Maryland is one of three states with a memorandum of agreement under DARPA's QBI program, and what that independent benchmarking infrastructure means for an ecosystem that also hosts the companies being evaluated
Quantum sensing as the near-term economic story — why Stambaugh, a metrologist by training, sees sensing startups like Euclid (NV diamond magnetometers for semiconductor inspection) as having clearer market paths than computing or networking right now
Quantum Workforce, Sensing, and the Long Game of Operationalization with Isabella Martinez
Episode 114
Monday, October 12, 2026 • Duration 37:39
Isabella Martinez sits at an unusual intersection in the quantum ecosystem: she's not primarily a hardware researcher or an algorithm theorist, but a practitioner who has to make quantum work in operational environments — for customers who have real missions, real constraints, and very little patience for technology that can't be deployed. Her team at Booz Allen spans quantum computing, quantum sensing, and post-quantum cryptography, which means she's watching all three of the field's major near-term vectors simultaneously.
This conversation was recorded at Quantum World Congress 2026, and it reflects the mood of that moment: sensing is on the cusp of real deployment, PQC migration is being driven by policy urgency, and quantum computing is still asking the question of when — but the "when" is getting more specific. If you work in quantum and care about how the technology actually crosses the gap from lab to mission, this episode is for you. It's also a genuinely useful listen for anyone thinking about quantum careers, workforce development, or how to build teams that can navigate a technology that doesn't yet have a playbook.
What We Get Into
Why Isabella's biomedical engineering capstone — not a physics class — shaped her entire approach to quantum: The lesson wasn't about building a better device; it was about why a life-saving tool sat unused because no one had time to get trained on it.
The four-stage commercialization arc she uses to frame quantum maturity: Solve for physics, then engineering, then business case, then operationalization — and why that last step is the one the field talks about least.
What quantum sensing deployment actually looks like right now: Including a vivid example of a quantum PNT sensor that "can't get wet" and what happens when you try to put it in a waterproof box without destroying its precision advantage.
Why quantum sensing and quantum computing will diverge in their adoption paths: A drone operator doesn't need to know their navigation data is quantum-derived. A quantum computing user, at least for now, probably does — and that changes everything about how you design for adoption.
How Booz Allen's team discovered the wrong part of a drug discovery workflow was being targeted for quantum speedup: A computational biologist and chemist sat down with the physics team and revealed that the step everyone assumed was the bottleneck wasn't the bottleneck at all.
Superconducting Qubits, STAR Architecture, and Diamond-Spin Qubits with Dr. Shintaro Sato
Episode 113
Monday, October 5, 2026 • Duration 41:20
Dr. Shintaro Sato is a Fellow and Head of the Quantum Laboratory at Fujitsu Research, and Deputy Director of the RIKEN RQC-Fujitsu Collaboration Centre. He oversees Fujitsu's entire quantum effort — from device fabrication through error correction architecture, software, and application research — and has been building toward commercial quantum systems since Fujitsu began its serious quantum R&D push around 2020. With over 164 publications spanning graphene nanoelectronics, superconducting qubit design, and fault-tolerant architectures, he brings both deep technical credibility and a rare full-stack perspective.
This conversation is timely because two significant developments converged almost simultaneously just before recording: Fujitsu announced a tin-vacancy (SnV) diamond-spin prototype developed with TU Delft and QuTech, and began testing its STAR error-correction architecture on neutral-atom hardware with startup Yaqumo — an explicit signal that Fujitsu is betting on hardware-agnostic software layers even as it races to scale its own superconducting devices. Listeners who follow quantum hardware roadmaps, fault-tolerant computing, or Japan's national quantum strategy will find this episode unusually specific and candid.
What We Get Into
How Fujitsu actually fabricates its superconducting chips — Sato explains how the team adapted Nakamura-sensei's original RIKEN designs, developed their own Josephson junction uniformity techniques, and built a research fab capability from scratch rather than licensing finished devices.
Why packaging is the hardest problem at 1,000+ qubits — not the qubits themselves, but the superconducting wiring, chip-to-chip interconnects, interposers, and the sheer number of control lines running from room temperature to millikelvin. Sato is candid that his engineers "don't want to do it anymore."
What STAR architecture actually does — it's not a new error-correction code; it operates at the logical gate layer above the surface code, replacing the notoriously expensive T gate with phase-rotation gates and selectively reintroducing T gates only when rotation angles would otherwise accumulate too much error. Version 3 achieves roughly a 10x accuracy improvement over version 2 for the same physical qubit count.
Quantum Venture Investing from the Lab to the Market with Marie Lepske
Episode 112
Monday, September 28, 2026 • Duration 41:02
Marie Lepske brings a combination that's genuinely rare in venture: a background in applied mathematics and physics, early experience covering quantum at Runa Capital before most generalist funds knew the field existed, and now a GP seat at Constructor Capital, which closed a $110M Fund I in February 2026 with more than half its capital directed toward next-generation computing including quantum. She backed Qnami at Runa — a quantum sensing company acquired by Quantum Design in June 2026, one of the few clean sensing exits the field has produced — and Constructor's portfolio includes QuEra, which raised over $230M in a round led by Google Quantum AI and SoftBank.
The conversation matters now because the quantum investment landscape is genuinely changing. SPAC activity, mega-rounds from hyperscalers, and rising valuations are pulling in non-specialist capital at the same time that the science is getting harder to evaluate from the outside. Lepske is one of the people who has to navigate that tension every day, and she's willing to name the failure modes.
Founders building quantum or deep-tech companies, investors trying to understand where specialist and generalist capital intersect, and technically curious listeners who want to understand how the business side of quantum actually works will all find this episode useful.
What We Get Into
Why technical training matters at the earliest stages — and what it actually buys you when a founding team's only asset is a laboratory and an optical table, before there's even a legal entity
How the specialist-versus-generalist divide is evolving — Lepske's 2022 argument that early-stage quantum would stay specialist territory, and how she reads the arrival of Google, SoftBank, and NVIDIA in QuEra's cap table
The due diligence framework for pre-product science startups — team provenance, patent landscape, IP legal review, and how Constructor uses its scientific advisory board to validate founders they can't fully assess internally
What a "no" looks like — the specific signals that make Constructor wait rather than invest, including teams with strong science but no credible path to market and IP positions that are already crowded
Rational Quantum Mechanics and the Qubit Ceiling with Tim Palmer
Episode 111
Monday, September 21, 2026 • Duration 46:32
Tim Palmer is not a quantum computing skeptic from the outside. He is a Fellow of the Royal Society, a CBE, an IPCC lead author, and the inventor of probabilistic ensemble forecasting — techniques now used in every major weather prediction center on Earth. He did his PhD in general relativity under Roger Penrose. When someone with that profile publishes a peer-reviewed paper in PNAS arguing that the entire fault-tolerant quantum computing roadmap may rest on a mathematical assumption that is subtly and profoundly wrong, it is worth paying close attention.
The timing matters. The quantum computing industry is spending billions on the assumption that standard quantum mechanics scales indefinitely — that if you can build enough error-corrected qubits, Shor's algorithm will eventually factor RSA-2048. Palmer's RaQM framework, reviewed by leading quantum foundations researchers including Lucien Hardy and Nicolas Gisin, makes a concrete, falsifiable prediction that this assumption will fail somewhere between 200 and 1,000 error-corrected qubits. That falsification window is opening right now. This episode is for anyone who cares about the foundations of quantum mechanics, the long-term viability of fault-tolerant quantum computing, or the rare pleasure of watching a serious scientist put a real stake in the ground.
What We Get Into
Why Palmer left general relativity and came back to quantum foundations decades later — the conceptual thread connecting chaos theory, climate forecasting, and the geometry of quantum state space
What Rational Quantum Mechanics actually proposes — specifically, why Palmer argues that Hilbert space should be defined over rational numbers rather than the full continuum of complex numbers, and what that means physically
The information-theoretic argument for a qubit ceiling — why, in RaQM, the information content of n entangled qubits grows linearly with n rather than exponentially, and why that creates a hard conflict with the quantum Fourier transform above a few hundred qubits
Why the 2022 Nobel Prize did not prove non-locality — Palmer's careful distinction between "Bell's inequality is violated" (experimentally established) and "the world is non-local" (an interpretation), and why that distinction matters enormously
Classical Reversible Computing on Quantum Dots with Daniel Loss
Episode 110
Monday, September 14, 2026 • Duration 49:01
Daniel Loss is RDIA Chair Professor of Quantum Computing and Director of the Quantum Center at King Fahd University of Petroleum and Minerals in Saudi Arabia, where this work was done. He is also one of the most influential theorists in quantum computing. The 1997 Loss-DiVincenzo proposal — that electron spins in quantum dots could serve as qubits — now has more than 9,000 citations and is the conceptual foundation for the semiconductor spin-qubit platforms that Intel, HRL, Diraq, and a wave of European startups are actively building toward. In 2025, Loss was named a Clarivate Citation Laureate in Physics, a designation with a strong historical track record as a Nobel Prize predictor.
The reason to listen now is that Loss has turned his attention to a question that predates quantum computing itself: can reversible, energy-efficient classical logic be physically realized? His 2026 paper argues that the spin-qubit hardware the field has spent decades developing is, almost incidentally, the ideal platform to do exactly that — and that the energy advantage over room-temperature CMOS could be so large it would matter enormously for AI inference workloads and data-center power budgets. This episode is for anyone following the spin-qubit roadmap, the energy crisis in classical computing, or the deeper question of what semiconductor quantum hardware is ultimately good for.
What We Get Into
Why reversible computing is having a moment now: The ideas of Landauer, Bennett, Fredkin, and Toffoli have been around since the early 1980s — Loss explains what changed experimentally that makes the proposal feel like engineering rather than philosophy.
The core energy claim, unpacked: Loss walks through why a spin-qubit Toffoli gate operating near 4 Kelvin could cost roughly 10⁵ times less energy than its CMOS equivalent, even after accounting for refrigeration overhead — and where the accounting is still incomplete.
What makes the computation classical and the hardware quantum: The gate uses coherent quantum dynamics internally, but inputs and outputs are classical spin states. No superposition is required between gate operations, which dramatically relaxes the error-correction burden.
The iToffoli gate and why it works for classical logic: Loss describes how a target spin hopping between quantum dots, controlled by two neighboring spins, implements a universal reversible gate using only DC voltage pulses — no radio-frequency drives required.
Quantum-Inspired AI and Tensor Network Compression with Román Orús
Episode 109
Monday, September 7, 2026 • Duration 40:57
Román Orús is one of the rare physicists who built a foundational mathematical tool — tensor networks — and then watched it become the engine of a unicorn. His 2013 introduction to tensor networks has been cited over 2,000 times; his company, Multiverse Computing, just announced a $570 million Series C at a $1.7 billion pre-money valuation. That arc — from condensed matter theory to Europe's largest quantum software company — is worth understanding on its own terms. But what makes this conversation particularly timely is a May 2026 paper Orús co-authored demonstrating that individual layers of Meta's Llama 3.1 8B language model can be encoded as quantum circuits and executed on IBM's 156-qubit Quantum System Two while the model generates text. It's a proof of concept, not a product — but it's a real result, and Orús is honest about what it does and doesn't prove.
This episode is for listeners who want a technically grounded, hype-free account of the quantum-AI intersection: what tensor networks actually are, why they keep getting rediscovered across different fields, where classical simulation of quantum systems genuinely competes with quantum hardware, and what it looks like to build a company at the boundary between those two worlds.
Sponsor Message
The Capital of Quantum is a people story. Built on a top-five quantum PhD program and 35-plus years of quantum research leadership. It's the billion-dollar initiative behind Discovery Center, launching this month with Microsoft, IQM, and Quantum Motion inside. That's why IonQ was born and is headquartered here, and why global companies keep choosing a spot minutes from Washington, D.C. This is where quantum is transforming the world. Come see it at the Quantum World Congress, September 23rd through 25th, College Park, Maryland. CapitalOfQuantum.com.
What We Get Into
What tensor networks actually are — Orús explains the core idea without equations: tensors as the "DNA" of a quantum state, and how a network of them lets you see and quantify the internal correlations (entanglement) that matter versus the ones you can safely ignore.
Quantum Workforce Intelligence from qubitsok.com with Piotr Lewandowski
Episode 108
Monday, August 31, 2026 • Duration 46:17
Piotr Lewandowski is a software engineer based in Poland who, as a side project, has built something that no well-funded research institute has: a continuously updated, ontology-tagged intelligence platform that ingests the entire quant-ph archive, tracks thousands of open quantum roles across hundreds of companies, parses patents and grants and open-source repositories, and links all of it to individual researcher profiles. He is not a tenured academic or a hardware engineer. He is an independent data practitioner, and that outsider position gives him a vantage point on the quantum workforce that insiders rarely have — or rarely share.
This conversation matters now because the quantum industry is simultaneously claiming a generational workforce opportunity and struggling to fill highly specialized roles. Lewandowski's data offers a rare ground-truth check on both claims. If you work in quantum hiring, research, policy, or investment — or if you're a student trying to understand what the field actually looks like from the outside — this episode will give you a more honest picture than almost anything else currently available.
What We Get Into
How qubitsok's 500-tag ontology was built from scratch — why Lewandowski chose a tree-structured, parent-child tag system rather than relying on existing academic metadata infrastructure, and how it steers AI toward the most specific and useful classification rather than broad category labels.
What the full quant-ph corpus reveals about researcher mobility — which countries are gaining quantum talent (Germany and China are notable winners) and which are losing it (the US and Australia are among the top brain-drain sources), based on tracking affiliation changes over time in published papers.
The rising share of industry authorship in quantum research — industry-affiliated authors have grown from roughly 3.4% of quant-ph papers in 2005 to nearly 14% in 2026, and what that structural shift might mean for what gets published — and what doesn't.
Why the platform tracks open-source contributions alongside papers — when researchers join industry and their publication rate drops, their open-source activity becomes a meaningful proxy for continued technical engagement, and qubitsok indexes both.
Quantum Risk, Readiness, and the Enterprise Boardroom with Richard Entrup
Episode 107
Monday, August 24, 2026 • Duration 35:32
Richard Entrup is unusual in quantum circles: he's not a physicist, and he doesn't pretend to be. He spent decades as a CIO, CTO, CDO, and CISO at organizations including Verizon, Christie's, Tiffany & Company, MoMA, Disney/ABC, and Time Warner before joining KPMG to lead its Emerging Solutions practice. That background — deep operational experience on the client side — shapes everything about how he thinks about quantum. He's not selling a hardware roadmap; he's thinking about what it actually takes to get a large, complex organization to change its cryptographic infrastructure before a threat materializes.
The conversation matters now because the signals are accelerating. NIST has finalized its first post-quantum cryptography standards, executive orders in the US are pushing federal agencies toward PQC migration, and the algorithmic efficiency gains that reduce the qubit threshold for breaking RSA-2048 keep coming. Listeners who work in enterprise technology, cybersecurity, or quantum strategy — or who advise organizations that do — will find Entrup's practitioner perspective a useful counterweight to the more hardware-focused conversations that dominate the field.
What We Get Into
Why Q-Day's exact date is the wrong question — and why the more important issue is how long it will take enterprises to even inventory their cryptographic exposure, let alone remediate it
The scale of the cryptographic migration problem, including why a single laptop may contain hundreds of individual cryptographic components and why upstream/downstream API dependencies make this a supply-chain-wide challenge, not just an internal IT project
Why "harvest now, decrypt later" creates urgency today, regardless of when fault-tolerant quantum computers arrive — and how compliance and regulatory timelines interact with that threat model
What crypto agility actually means in practice — moving from a "set it and forget it" cryptographic posture to a dynamic, continuously monitored framework, including the pressure SSL certificate renewal windows are already creating
How KPMG built its PQC practice, incubated it within the firm, and handed it off to the cybersecurity advisory team as a core service offering
Related Shows Based on Content Similarities
Discover shows related to The New Quantum Era - innovation in quantum computing, science and technology, based on actual content similarities. Explore podcasts with similar topics, themes, and formats, backed by real data.
The Paladin Capital partnership and the DC venture gap — why the mid-Atlantic VC culture differs from the West Coast model and what the initiative is doing to change the capital formation picture
Workforce development from Q12 to the makerspace — a classroom next to a working dilution refrigerator, high school students from across the street getting their first look at a cryostat, and the lineage from early NSF quantum education workshops to the UN International Year of Quantum
How Stambaugh thinks about competing ecosystems — Colorado, Illinois, New Mexico — and why he frames them as complementary rather than adversarial, while still being clear about Maryland's competitive positioning
"Tell me where a national lab is, I'll tell you where a physicist is, and I'll tell you where quantum is." — Stambaugh on why quantum clusters form where they do, and why the lineage from NIST, Sandia, and Argonne to today's startups is not accidental.
"We ...
Why Isabella actively recruits people with criminology degrees, economics backgrounds, and education startup experience onto a quantum team: And why she's more worried about the rise of pure quantum-only graduate programs than she is about the talent shortage.
The AI parallel that's shaping how enterprise customers think about quantum timing: Customers who watched AI capabilities leap unexpectedly are now asking "when" instead of "what" — but they're also more willing to wait for the right moment rather than jump in early.
Her honest read on NISQ-era value: Real scientific output is happening on today's devices, but the early 2030s are where she expects the first commercially meaningful fault-tolerant results to appear.
What the quantum community risks losing as the field matures: The collaborative, cross-disciplinary spirit that has defined the early era — and why she thinks that spirit is actually a competitive asset worth protecting.
> "The problem wasn't actually an engineering problem. An engineering tool existed for the problem they were having us solve. The problem was that getting certified to use that tool took so long that most nurses didn't have the time to get trained on it."
— Isabella on the capstone project that shaped her entire career philosophy: adoption is often a harder problem than invention.
> "First you have to solve for physics, then you have to solve for engineering, and then you have to solve for business case. But I would argue then you have to solve for operationalizing something — which is even beyond them."
— On the four-stage arc that the quantum field's commercialization narrative tends to stop one step too early.
Insight: When Booz Allen sat a computational biologist and chemist down with their quantum algorithm team to walk through an actual drug discovery workflow, the team discovered that the step they'd assumed was the computational bottleneck wasn't the bottleneck at all. The right use case only became visible when the domain experts were in the room.
> "I need to do this purposefully. I have to purposefully open reqs where we are looking for people with more than just a physics degree."
— On why interdisciplinary hiring is a deliberate strategy, not a nice-to-have.
Insight: Isabella draws a direct parallel between the rise ...
Why STAR is hardware-agnostic — because it operates at the logical operation layer rather than the physical error-correction layer, it can in principle run on any hardware modality. Fujitsu has now begun testing it on Yaqumo's neutral-atom platform, and Sato mentions QuEra has also demonstrated STAR independently.
What tin-vacancy (SnV) diamond-spin qubits are actually for — not a replacement for superconducting qubits, but a photonic interconnect technology. SnV centers emit photons that can carry quantum information between modules, potentially linking separate dilution refrigerators — a transduction approach that sidesteps one of the hardest problems in scaling superconducting systems.
The speed mismatch problem in hybrid architectures — superconducting qubits operate at gigahertz speeds; nuclear spins in diamond-spin systems operate at kilohertz. Sato acknowledges this directly and frames it as an architectural challenge analogous to CPU-memory hierarchies in classical computing.
What Fujitsu's open-source strategy looks like in practice — the collaboration with Osaka University on Project Octopus, which produced an open-source full-stack control and software platform, is now being adopted as part of Fujitsu's commercial offering.
What 2030 actually looks like — Sato is measured: a 10,000-qubit machine will still be a "very small scale logical quantum computer," most useful for quantum chemistry in hybrid HPC+quantum workflows. Revolutionary applications will come incrementally, not all at once.
The long-term vision — Sato's stated dream is a quantum computer small enough to fit in a smartphone, which he acknowledges requires entirely different physics than anything on today's roadmap.
Shintaro Sato — ResearchGate Profile — Full publication list (164 papers, 3,096 citations); useful for tracing his path from graphene nanoelectronics to quantum architecture.
Interview with Shintaro Sato — QuTech — Sato discusses the NV-center and diamond-spin collaboration with QuTech and the vision for quantum-as-a-service.
STAR Architecture Overview — Fujitsu Global — Technical explainer for STAR ver. 3 (announced March 2026), including the qubit-reduction claims for ruthenium-catalyst calculations.
The Qnami exit and what it reveals about quantum sensing — why sensing applications are currently concentrated in R&D markets, which verticals Lepske thinks will break out first, and why sensing competes with computing for buyer attention
The SPAC problem — why the wave of quantum public listings concerns her, and why evaluating roadmap credibility requires the kind of deep familiarity that takes years to build
How Constructor supports portfolio companies beyond capital — executive hiring, software development guidance, co-investor introductions, and the limits of that hands-on model
AI and quantum as parallel tracks — why she doesn't see AI as cannibalizing quantum talent or capital, and where she thinks the two fields will eventually converge
Resources & Links
Guest & Fund
Marie Lepske — LinkedIn — Primary professional profile; includes keynote highlights and 2026 speaking engagements
Constructor Capital — Official Website — Fund homepage with portfolio listings, team bios, and investment thesis across DeepTech, Software Tech, and Knowledge Tech
On what early-stage diligence actually looks like: > "They have only laboratory, and they have optical table which they want to show to you and you should understand if they have some interesting and useful patents or they can file them during the next one to two years."
On where specialist and generalist capital divide: > "These bigger players, they're coming later. They're coming when we already investigated that this particular technology and this particular team actually could win."
On the SPAC problem: > "It's very difficult for [non-specialist investors] to evaluate if they're doing proper investment or not, or if this valuation is good or not… You should be able to find out these roadmaps and to understand if this is a real roadmap or just written for the SPAC."
Insight — sensing vs. computing for buyer attention: Lepske observes that quantum sensing is structurally underappreciated relative to computing, not because the products aren't real, but because governments and corporates perceive computing as the larger prize — which she suggests is not obviously correct.
Insight — quantum talent scarcity as a leading indicator: She notes that quantum computing companies are now competing for talent the way AI companies did four or five years ago, with roughly 400–450 relevant laboratories globally — a number that puts the field's scale in sharp relief.
Related Episodes
Ep. 22 — Trapped Ions and Quantum VCs with Chiara Decaroli — Another physicist-turned-investor conversation; useful companion for understanding how scientific training shapes investment judgment in quantum hardware
Ep. 96 —
How gravity enters the picture — Palmer's argument, drawing on Penrose-style thinking, that gravity is the physical mechanism responsible for discretizing Hilbert space, and how that determines the numerical qubit ceiling
Why the ceiling is technology-dependent but bounded absolutely — how Palmer estimates ~400 qubits for current photonic technology and argues that no technology, however exotic, can push the limit above ~1,000
What happens to Shor's algorithm specifically — why the quantum Fourier transform is the precise point of failure, and what that means for RSA encryption and the post-quantum cryptography transition
The optimistic read on a potentially negative result — Palmer's argument that a fundamental discovery about quantum mechanics, even one that limits quantum computing, could open doors we cannot yet imagine, in the same way general relativity eventually gave us GPS
214 is a delightful bonus to the conversation, a short story by Noel Gorelick that imagines what might happen if RaQM turns out to be true.
Key Quotes & Insights
> "It's not that nature ...
The Quantum Zeno trick for classical memory: Frequent projective measurement of a spin state can stabilize it against relaxation, turning one of quantum computing's central headaches into a feature for classical storage.
Why AI inference is the natural first application: Error tolerance, parallelizability, and the absence of a need for new algorithms make AI inference workloads a compelling early target — and Loss argues this de-risks the spin-qubit enterprise regardless of whether fault-tolerant quantum computing arrives on schedule.
The dual-use platform argument: The same germanium/silicon quantum-dot array could, in principle, run quantum algorithms when superposition is useful and classical reversible logic when it is not — a flexibility that changes the economic calculus for building the hardware.
What the experimental community needs to do next: Loss describes the first falsifiable tests — a three-spin iToffoli truth table, energy budget measurements, and a five-dot reversible adder — and names the groups best positioned to run them.
How the brain comparison reframes the stakes: Loss notes that his energy estimates put spin-qubit classical computing roughly four orders of magnitude below the energy cost of a biological synapse, which has implications for how we think about the physical limits of AI.
Daniel Loss — Wikipedia — Comprehensive biographical overview, career history, and awards list for listeners who want background before or after listening.
> "If the physical platform is successful, we are guaranteed to have killer applications — because I don't need to find new algorithms for this." > — Loss on why classical reversible computing de-risks the spin-qubit investment, regardless of the timeline for fault-tolerant quantum algorithms.
> "The energy difference is a factor of ten to the fifth. And this is mind-blowing." > — Loss summarizing the device-level energy advantage of a spin-qubit Toffoli gate over its CMOS equivalent, after accounting for refrigeration overhead.
Insight: Loss argues that the Quantum Zeno effect — the tendency of frequent measurement to freeze a quantum state — can be used deliberately to stabilize classical spin states against relaxation, turning a well-known quantum-computing obstacle into a memory-stabilization tool for classical logic.
Insight: The proposal requires superposition only inside a gate operation, not between operations. This means the error-correction burden is classical (majority voting) rather than quantum (surface codes or similar), which is a qualit...
Why the same math keeps appearing in different fields — from condensed matter simulation to quantum computing simulation to machine learning, and why Orús sees that recurrence as a sign of something deep rather than a coincidence.
How ChatGPT changed Multiverse's trajectory — the company was already applying tensor networks to machine learning before 2022; the emergence of large language models gave them a problem where the fit was obvious and the market was enormous.
What "90–95% compression with minimal accuracy loss" actually means — Orús explains the overparameterization problem in current AI models and why he believes tensor networks address a genuine structural inefficiency, not just a tuning opportunity.
The IBM kicked Ising model episode — Orús describes how his team rapidly produced a classical tensor network simulation of an experiment IBM had presented as evidence of quantum utility, and what that kind of competition between classical and quantum methods actually does for the field.
The Cayley Unitary Adapter experiment — how Multiverse sliced individual layers out of Llama 3.1 8B, encoded them as quantum circuits, ran them on a 156-qubit IBM processor, and achieved a 1.4% perplexity improvement — and why Orús argues the improvement-per-parameter ratio is the number that matters, not the headline percentage.
Why edge deployment is the real commercial driver — drones, satellites, vehicles, and industrial devices that cannot rely on cloud connectivity are the market pulling Multiverse toward smaller, more efficient models, not just benchmark competition with frontier labs.
How Orús thinks about Multiverse's identity — he calls it a "quantum AI company," not a quantum company or an AI company, and explains what that distinction means for how they allocate research effort and where they expect to be when fault-tolerant quantum hardware matures.
What he'd tell a PhD student today — a genuinely honest answer about the trade-offs between academic research and deep-tech industry, from someone who has lived both simultaneously.
Resources & Links
Guest & Company
Román Orús — Personal Site — Lists talks, reviews, affiliations, and awards including the 2024 Physics, Innovation and Technology Prize from the Royal Spanish Society of Physics.
> "We are using atomic bombs to kill a mosquito." Orús on the overparameterization of current large language models — and why he believes the transformer-attention paradigm, however successful, ...
The "qubie" talent-matching tool Lewandowski is building — rather than keyword overlap, qubie dispatches sub-agents to extract specific, claim-level evidence from a researcher's papers, dissertations, and other public writing, then returns a structured profile and interview guide for each candidate.
Why sourcing quantum talent is a fundamentally different problem than general tech recruiting — the evidence of what a quantum researcher can actually do is largely public and published, but no one has had the infrastructure to read it systematically at scale until now.
The two product directions Lewandowski is weighing — analytics and competitive intelligence for investors and companies versus talent matching for quantum hiring — and why he's currently prioritizing the latter.
What it means to build a field-level intelligence platform as an outsider — Lewandowski is neither employed by a quantum company nor affiliated with a university, and that independence shapes both what he can see and what he can say.
Resources & Links
Guest Links
qubitsok.com — The platform itself: quantum job board, daily arXiv paper digest with semantic tagging, and researcher collaboration search. All free for researchers and job seekers.
qubitsok.com/collaborate — Search for quantum researchers by expertise, ontology tag, and affiliation — useful for finding collaborators or co-authors.
Piotr Lewandowski on LinkedIn — The best place to reach him directly, especially if you're a company interested in early access to the qubie talent-matching product.
2026 Quantum Computing Salary Report — Analysis of 194 salary data points drawn from real job postings; a useful benchmark for researchers and hiring managers alike.
Fastest-Growing Quantum Skills 2026 — Lewandowski's analysis of 1,212 quantum jobs and 14,975 papers identifying which technical skills are accelerating and which are declining in the labor market.
Tools & Platforms
qubitsok.com/jobs — Live quantum job listings, filtered by the platform's 500-tag ontology.
qubitsok.com/region/europe — Live European quantum jobs market data, useful context for the EU talent and salary discussion.
qubitsok.com/hire — Information for companies looking to use qubitsok's candidate database and, soon, the qubie matching tool.
On why the published record is a better hiring signal than a LinkedIn profile: > "Research gives you this unique lens to see people's work before you talk to them. You can be really prepared, and this helps on two sides — you talk to people actually capable of filling the role, and you're not wasting their time asking questions they already answered via their published work."
On what brain-drain data actually shows: > "The biggest country that gained quantum computing talent in the last twenty-four months is Germany — which is not something someone could expect. And the biggest countries getting brain-drained are, interestingly, the United States."
Insight — on the soul-crushing reality of quantum sourcing: Lewandowski's first job in college was sourcing — going through profiles with pen and paper, making cold calls that nobody wanted to receive. His argument is that quantum hiring doesn't have to work that way, because the evidence of what a researcher can do is already public. The problem has never been a lack of signal; it's been a lack of infrastructure to read it.
On the rising share of industry authorship: Industry-affiliated authors have grown from roughly 3.4% of quant-ph papers in 2005 to nearly 14% in 2026 — a structural shift in who is producing the science, with implications for what gets published and what gets quietly redirected into proprietary pipelines.
Insight — on the limits of the data: Lewandowski is consistently careful about w...
The "good quantum" side of the ledger — how KPMG's emerging research function is approaching quantum computing as a source of competitive advantage, not just risk, and what sectors are furthest along in exploring it
The AI-quantum convergence, including Entrup's observation that AI is already being used to read and crack code — and what that means for the urgency of cryptographic modernization
Why the enterprise quantum opportunity still has a long tail, and how the current moment compares to the early infrastructure phase of the internet — when everyone was talking about TCP/IP and DNS, not Uber or Netflix
KPMG Quantum Dawn (2025) — KPMG's enterprise quantum readiness hub, introducing the Q-PREP framework and PQC implementation services, with Entrup as named lead
> "It's not if but when. And it could be five years, could be three years, could be ten years. The fact is organizations are not gonna be ready. And that's the scary part." — Richard Entrup on Q-Day
> "This is not just the CISO. This is gonna be the software engineering app dev guys. This is gonna be all your partners, upstream and downstream, who have to also be compliant — because if you change your crypto and they don't, that stuff's gonna break." — On why PQC migration is an enterprise-wide, supply-chain-wide problem
Insight: Entrup draws a sharp distinction between the "bad quantum" (cryptographic risk requiring urgent defensive action) and the "good quantum" (competitive opportunity with a longer tail) — and argues that most organizations aren't adequately addressing either.
Insight: The analogy to the early internet is deliberate: just as the 1990s were consumed with TCP/IP and DNS rather than the applications those protocols would eventually enable, the current quantum moment is still largely an infrastructure conversation — and that's normal, not a sign of failure.
> "AI is expediting all of this. If AI is doing one thing, the use case is reading code and cracking it. That's pretty scary." — On the intersection of AI capability and cryptographic vulnerability
Related Episodes
Ep. 81 — Quantum LDPC Error Correction with Larry Cohen and Paul Webster — Directly relevant: Cohen and Webster discuss how QLDPC error correction reduces the qubit overhead needed for RSA cryptanalysis, the technical underpinning of the threat timeline Entrup describes
The New Quantum Era - innovation in quantum computing, science and technology, a podcast by Sebastian Hassinger - Stats, Episodes and Rankings - My Podcast Data
