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.

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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

• 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.
• Multiverse Computing — Official Website — Home page for CompactifAI, Singularity, and Multiverse's full product portfolio.

Papers & Articles Discussed in This Episode

• arXiv 2605.05914 — "Quantum-enhanced Large Language Models on Quantum Hardware via Cayley Unitary Adapters" (May 2026) — The paper at the center of the episode: Llama 3.1 8B layers running on IBM's 156-qubit Quantum System Two. Start here if you want the technical details behind the quantum-in-an-LLM result.
• Multiverse Computing — "Talking to a Quantum Computer" (May 2026) — The accessible blog-post version of the Cayley Unitary Adapter experiment; a good entry point before tackling the arXiv paper.
• arXiv 2401.14109 — CompactifAI: Extreme Compression of Large Language Models Using Quantum-Inspired Tensor Networks — The foundational peer-reviewed paper introducing Multiverse's tensor network compression method.
• arXiv 2509.06653 — "Classical Neural Networks on Quantum Devices via Tensor Network Disentanglers" (Sep 2025, rev. Apr 2026) — Orús, Singh, and Aizpurua on hybrid classical-quantum execution for bottleneck neural network layers; the research underpinning the longer-term hybrid architecture vision.

Models & Products

• HyperNova 60B on Hugging Face — Open-source 50%-compressed model derived from GPT-OSS-120B; context for Multiverse's open model strategy.
• Pulsar 16B Launch with NVIDIA (June 2026) — Announcement of Multiverse's open reasoning model, scoring 87.22 on AIME 2025 at 16B parameters.

Funding & Company Context

• Series C Announcement — GlobeNewswire (July 2026) — $570M / €500M round at $1.7B pre-money valuation; covers investor lineup and deployment verticals.

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, ...

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

• EPJ Quantum Technology — "The quantum technology job market: data-driven analysis of 3,641 job posts" (March 2026) — Lewandowski's peer-reviewed paper on the quantum job market, the most rigorous published treatment of the data underlying qubitsok.
• 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.

Recognition

• Dr. Bob Sutor's "Quantum Follow Friday" (February 27, 2026) — Sutor Group newsletter highlighting Lewandowski as a key quantum community voice worth following; useful context for his growing recognition as an independent analyst.

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...

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
• 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

Resources & Links

Guest & Organization

• Richard Entrup — Worth Magazine Profile — Career arc from CIO/CISO roles at major global brands to KPMG's Emerging Solutions practice
• KPMG Quantum Dawn (2025) — KPMG's enterprise quantum readiness hub, introducing the Q-PREP framework and PQC implementation services, with Entrup as named lead
• KPMG Quantum homepage

Reports & Research

• KPMG — "The Quantum Threat Is No Longer Theoretical" (2026) — The threat brief discussed in this episode, charting the rapid decline in qubits needed to crack RSA-2048 and urging immediate PQC migration
• KPMG — "From Theory to Impact: Real-World Results in Quantum Machine Learning" (2026) — KPMG's joint report with IBM and Kipu Quantum on measurable quantum ML results on real hardware
• KPMG — "Prepare Now for Quantum Cyber Risk" — Board Leadership Article (2026) — C-suite and board-level guidance on integrating quantum risk into enterprise oversight
• arXiv — "Quantum-enhanced satellite image classification" (2026) — The underlying research paper behind the KPMG/IBM/Kipu Quantum ML results

Ecosystem & Events

• Chicago Quantum Exchange — KPMG Joins CQE (October 2024) — Announcement of KPMG's formal CQE membership, referenced in the episode as part of the firm's ecosystem-building strategy
• KPMG 2026 Quantum Consortium — The inaugural KPMG Quantum Consortium event (March 2026, Orlando) discussed in the episode

Independent Coverage

• Quantum Computing Report — KPMG joins Chicago Quantum Exchange (2024) — Independent coverage of KPMG's CQE partnership and enterprise quantum strategy
• Quantum Zeitgeist — Kipu Quantum satellite imagery coverage (Feb 2026) — Independent analysis of the KPMG/IBM/Kipu hybrid QML results

Key Quotes & Insights

> "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
• Ep. 38 —

Thaddeus Ladd has spent seventeen years at HRL as the theoretical anchor of its silicon spin qubit program — co-authoring the 2023 Nature paper that demonstrated universal logic with encoded spin qubits, and contributing to the 2026 QPU paper that integrated qubits, a cryo-CMOS controller, and a new superconducting ribbon cable into a single digitally controlled system. He is not a commentator on this acquisition; he is one of the people whose work made it happen.

The conversation is recorded eleven days after IBM announced a definitive agreement to acquire HRL from Boeing and General Motors — a deal that has not yet closed. That timing makes this one of the few technically grounded, insider-adjacent conversations available about what IBM is actually buying, why the exchange-only spin qubit architecture is strategically distinctive, and what the combination of HRL's research culture with IBM's fabrication ambitions could produce. Listeners who follow quantum hardware, quantum computing strategy, or the evolution of industrial research labs will find this episode unusually substantive.

What We Get Into

• Why the 2026 QPU paper is a systems story, not just a fidelity story — the qubit chip, the cryo-CMOS controller operating at four Kelvin, and the new superconducting ribbon cable are all part of one integrated QPU, and that framing is central to understanding what IBM acquired.
• What "exchange-only" actually means — why using only voltage-controlled exchange interactions (no microwaves, no local oscillators, no phase tracking during idle) is both a technical constraint and a significant engineering advantage for scaling.
• Why the jump from six dots to fifty-four dots happened so fast — and what was happening in HRL's fabrication program that wasn't being published.
• What EUV lithography has to do with spin qubit scaling — and why the connection between HRL's process and IBM's Anderon 300 mm quantum foundry is one of the clearest pieces of strategic logic in the acquisition announcement.
• How HRL's cryo-CMOS work could benefit IBM's superconducting program — and why the control-and-interconnect bottleneck is a shared problem across modalities, not a spin-qubit-specific one.
• The "chandelier" reframe — Thaddeus's argument that the cables, filters, and control electronics surrounding a superconducting qubit chip are not overhead; they are part of the QPU, and understanding that changes how you read the HRL acquisition.
• Which modality Thaddeus thinks will reach commercially useful scale first — and why he still believes spin qubits are the long-term answer, using an analogy to vacuum tubes and silicon microprocessors that is worth hearing in full.
• What the acquisition means for HRL as an institution — the context of lost program funding, the December 2025 Q2B meeting, and what it means for a defense-oriented industrial research lab to find a commercial path through IBM.

Resources & Links

Guest

• Thaddeus D. Ladd — Personal Website & Publications — Self-curated, annotated bibliography; the best single source for his research arc across spin qubits and quantum communication.
• Thaddeus Ladd — Hertz Foundation Profile — Biographical overview of his career and role at HRL.
• Thaddeus D. Ladd — Google Scholar — Full citation record.

Papers & Articles

• A Digitally Controlled Silicon Quantum Processing Unit — arXiv (April 2026) — The QPU paper discussed at length in this episode: 54-dot device, cryo-CMOS controller at 4 K, superconducting ribbon cable, and error correction experiments — all working as one integrated system.
• Universal Logic with Encoded Spin Qubits in Silicon — Nature (2023) — The landmark result demonstrating universal logic with exchange-only encoded qubits; Ladd was co-author and lead theorist.
• Two-Dimensional Si Spin Qubit Arrays with Multilevel Interconnects — PRX Quantum (2025) — Scalable 2D spin-qubit arrays achieving greater than 99.9% single-qubit gate fidelity; the step between the 2023 and 2026 results.
• Silicon Encoded Spin Qubits Achieve Universality — HRL (2023) — HRL's public announcement of the Nature result; accessible summary for non-specialists.
• Semiconductor Spin Qubits: The Certainty of Progress — HRL (December 2025) — HRL's public framing of the platform's trajectory, published shortly before the Q2B meeting where Sebastian and Thaddeus first met.

Acquisition & IBM Strategy

• IBM to Acquire HRL Laboratories — IBM Newsroom (July 23, 2026) — The definitive transaction announcement; names the full scope of what IBM says it is acquiring.
• IBM to Acquire HRL Laboratories — HRL Laboratories (July 23, 2026) — HRL's concise confirmation of the deal and the beginning of regulatory review.
• What Are Spin Qubits? — IBM Quantum (July 23, 2026) — IBM's technical explanation of HRL's Si/SiGe exchange-only qubits and the shared silicon fabrication argument.
• A Brief History of HRL Laboratories — IBM Research (July 23, 2026) — IBM's institutional framing of HRL, including its history with the laser, self-aligned-gate MOS fabrication, and the 2026 spin-qubit QPU.
• IBM and U.S. Department of Commerce Announce Anderon Quantum Foundry (May 21, 2026) — The 300 mm quantum foundry announcement that Thaddeus identifies as one of the clearest pieces of strategic logic behind the HRL acquisition.
• IBM Commits More Than $10 Billion to Quantum Computing (June 2, 2026) — Capital context for the acquisition: R&D, manufacturing scale-up, M&A, and ecosystem investment over five years.

Tools & Platforms

• spinQICK — GitHub — HRL's open-source FPGA-based spin-qubit control toolkit; a concrete artifact of HRL's approach to open tooling.
• HRL Launches Open-Source spinQICK — HRL (July 2025) — Announcement and context for the spinQICK release.
• HRL Quantum — Collection of HRL quantum research, talks, and news.

Organizations

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