• 39 minutes 38 seconds
    Quantum Error Correction and the Road to Fault Tolerance with Benjamin Bloom

    Ben Bloom has an unusual vantage point on quantum computing: he built one of the world's most precise atomic clocks at JILA, learned semiconductor manufacturing discipline at Intel, watched a superconducting startup scale from the inside at Rigetti, and then founded Atom Computing in 2018 to apply alkaline-earth atomic physics to quantum computation. He returned as CEO in 2024 to push the company through what he describes as the transition from binary technical challenges to engineering execution.

    The conversation lands at a pivotal moment. In June 2026, Atom published a paper demonstrating sustained, many-round toric-code quantum error correction on neutral atoms — including mid-circuit measurement and mid-computation atom replacement from a reservoir — the first time this has been done on this platform. The same month, the company closed a $100M Series C, signed a $100M Letter of Intent with the Department of Commerce, and announced a photonic networking partnership with Nu Quantum. The Magne system, built jointly with Microsoft for Denmark's QuNorth, is due around the turn of the year. If you want to understand what "fault-tolerant quantum computing" looks like when it stops being a research milestone and starts being an engineering program, this is the episode.

    What We Get Into

    • Why the toric-code QEC result is different from prior milestones — Bloom explains that the June paper was designed to demonstrate every component exactly as it would be used in a utility-scale system: mid-circuit measurement, atom loss, and live atom replacement from a reservoir, all running for ~90 error-correction cycles.
    • How atom loss goes from fatal flaw to managed engineering problem — Neutral atoms can literally escape the trap mid-computation. Bloom describes how Atom now cycles atoms between data-qubit and ancilla roles and reloads from a reservoir, removing what he calls the last "binary challenge" for the platform.
    • Why the number of qubits in a neutral atom system is a software question as much as a hardware one — Because the array is fully reconfigurable, the same physical system can run a shallow circuit with many logical qubits or a deep circuit with fewer, higher-distance logical qubits. Bloom explains why Atom gives approximate qubit numbers on its roadmap.
    • What "Level 2" means for the Magne system — The ~50-logical-qubit machine being delivered to QuNorth in Denmark is described as the first commercially available system with enough logical qubits and error-correction distance to go meaningfully beyond what's possible with a handful of logical qubits today. Bloom explains how the spec was driven by use cases, not raw qubit counts.
    • How the Microsoft partnership actually works — Bloom describes the relationship as starting in R&D and expanding to go-to-market, with Microsoft contributing error-correction software and stack expertise above the hardware layer, while Atom owns the physical system.
    • Why Atom's real competitive differentiation is invisible — Bloom argues that while all neutral atom companies work with the same atoms, Atom's proprietary work on fast laser steering, pipelining around atom movement, and system integration is where the competitive moat actually lives — and none of it is in public papers.
    • The engineering ceiling on a single neutral atom module — Bloom estimates tens of thousands to hundreds of thousands of qubits may be achievable in a single chamber before networking becomes necessary, and explains why Atom has been building photonic interconnect capability for nearly two years.
    • What utility-scale looks like in 2030 and 2031-32 — Bloom sketches a near-term picture of single systems with tens of thousands of qubits serving national lab and government customers, followed by networked fleets with potentially millions of qubits serving pharmaceutical, materials, and chemical companies on a time-to-solution pricing model.
    • What DARPA's QBI Stage C forced Atom to confront — Bloom says the program required Atom to sharpen its pencil on runtime and cost, not just qubit count, and that the team had to return to the drawing board more than once to ensure their roadmap actually produces answers in reasonable wall-clock time.
    • The founder-physicist CEO perspective — Bloom reflects on stepping back to CTO, what Rob Hays's tenure accomplished in building hyperscaler relationships, and why returning as CEO made sense once the company's challenge shifted back toward deep technical execution.

    Resources & Links

    Guest & Company

    Papers & Technical Results

    Magne System & Partnerships

    Funding & Government Programs

    9 November 2026, 3:00 pm
  • 54 minutes 49 seconds
    Quantum Policy, Topological Qubits, and the Maryland Research Center with Charles Tahan

    Charles Tahan has one of the most unique career arcs in quantum: PhD in silicon quantum computing, postdoc across three continents, fifteen years at the Lab for Physical Sciences running quantum programs and doing original research, then the first Director of the National Quantum Coordination Office at the White House OSTP, and now a Partner at Microsoft Quantum. He is one of the very few people who has both written U.S. quantum policy and is now executing on it from inside a major hardware program. That dual vantage point — policymaker turned builder — gives him a kind of credibility that is rare in a field full of either pure researchers or pure advocates.

    The timing of this conversation matters too. Microsoft just opened a 15,000-square-foot quantum research center at the University of Maryland's Discovery District, and DARPA has been given on-site access to the Majorana 2 system as part of its Quantum Benchmarking Initiative — the first opportunity for a technically capable independent party to evaluate Microsoft's topological qubit claims. Tahan is candid about the difficulty of what Microsoft is attempting, honest about the timelines, and enthusiastic about the parts of this problem that most people in his position would prefer to keep vague.

    Listeners who follow quantum hardware development, national science policy, or the workforce and ecosystem questions around quantum will find this episode unusually grounded.

    What We Get Into

    • How Tahan ended up in government in the first place — starting as a DARPA technical consultant in 2007, helping write the milestones for the first quantum computing programs, and eventually becoming the first Director of the National Quantum Coordination Office
    • What the NQI actually accomplished — and why Tahan argues its most important effect wasn't the money itself but the signal it sent globally, triggering coordinated national strategies across dozens of countries
    • Why topological qubits are worth the difficulty — Tahan's honest case for why a high-risk, high-reward approach makes sense even when the engineering challenges are severe, and what the broader field has already gained from Microsoft's pursuit of Majorana physics
    • The DARPA Quantum Benchmarking Initiative as an independent audit — why having DARPA's technical team on-site to evaluate Majorana 2 is meaningful, and how Tahan thinks about external validation as a feature rather than a threat
    • Why Tahan now believes the next five years may equal the last thirty — his specific reasoning about how AI-accelerated science, the scale of capital flowing into computing infrastructure, and demonstrated quantum error correction have changed his sense of what's possible
    • The "simplest quantum computer" argument — what Tahan was actually proposing in his 2024 arXiv preprint (written as a retrospective from 2040, presented at APS in a Doc Brown costume), and how the idea of using quantum computers to generate training data for classical models fits into a near-term business model
    • What a quantum hardware makerspace actually is — the specific vision for the Maryland center's hands-on space, including dilution refrigerators, hardware from AMD, IQM, Quantum Motion, Fermilab's QICK tools, and a model Tahan hopes can be replicated and scaled
    • Why the workforce gap is a hardware problem, not just a software one — and how Tahan's own path (his father was an HVAC technician at the Smithsonian who got him a high school internship in astrophysics) shapes his conviction that hands-on experience is how scientists and engineers are actually made
    • Microsoft as a platform company — why Tahan frames Microsoft's quantum strategy as wanting the best computers for customers, full stop, which means partnering with and supporting other modalities, not just betting everything on topological qubits

    Resources & Links

    Guest Links

    Papers & Articles

    Organizations & Programs

    2 November 2026, 3:00 pm
  • 38 minutes 24 seconds
    Atomic-Precision Silicon Qubits and the Path to Fault Tolerance with Michelle Simmons

    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

    Papers & Results

    DARPA Program

    Context & Analysis

    Key Quotes & Insights

    "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...

    26 October 2026, 4:54 pm
  • 30 minutes 18 seconds
    Building a Regional Quantum Ecosystem with Corey Stambaugh

    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
    • 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

    Resources & Links

    Guest & Initiative

    Key Announcements Referenced in This Episode

    Background & Context

    Key Quotes & Insights

    "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 ...

    19 October 2026, 5:00 pm
  • 37 minutes 39 seconds
    Quantum Workforce, Sensing, and the Long Game of Operationalization with Isabella Martinez

    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.
    • 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.

    Resources & Links

    Guest & Organization

    Articles by Isabella Martinez

    Context & Policy

    Key Quotes & Insights

    > "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 ...

    12 October 2026, 5:00 pm
  • 41 minutes 20 seconds
    Superconducting Qubits, STAR Architecture, and Diamond-Spin Qubits with Dr. Shintaro Sato

    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.
    • 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.

    Resources & Links

    Guest & Organization

    Hardware Roadmap & STAR Architecture

    Diamond-Spin & SnV Prototype

    Neutral-Atom / Yaqumo Collaboration

    5 October 2026, 5:00 pm
  • 41 minutes 2 seconds
    Quantum Venture Investing from the Lab to the Market with Marie Lepske

    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
    • 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

    Portfolio Companies Referenced

    Background Reading

    Recent Constructor News

    Key Quotes & Insights

    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

    28 September 2026, 2:55 pm
  • 46 minutes 32 seconds
    Rational Quantum Mechanics and the Qubit Ceiling with Tim Palmer

    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
    • 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

    Resources & Links

    Guest

    Papers & Articles

    Further Reading

    • The Primacy of Doubt — Oxford University Press — Palmer's 2022 trade book synthesizing chaos, climate, and quantum uncertainty; accessible entry point to his broader thinking
    • 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 ...

    21 September 2026, 5:00 pm
  • 49 minutes 1 second
    Classical Reversible Computing on Quantum Dots with Daniel Loss

    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.
    • 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.

    Resources & Links

    Guest & Lab

    Papers & Articles

    Organizations

    Key Quotes & Insights

    > "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...

    14 September 2026, 5:00 pm
  • 40 minutes 57 seconds
    Quantum-Inspired AI and Tensor Network Compression with Román Orús

    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.
    • 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

    Papers & Articles Discussed in This Episode

    Models & Products

    Funding & Company Context

    Key Quotes & Insights

    > "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, ...

    7 September 2026, 8:24 pm
  • 46 minutes 17 seconds
    Quantum Workforce Intelligence from qubitsok.com with Piotr Lewandowski

    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.
    • 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.
    • Piotr Lewandowski on YouTube — His channel covering quantum computing job market analysis and platform updates.

    Papers & Reports

    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.

    Recognition

    Key Quotes & Insights

    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...

    31 August 2026, 9:48 pm
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