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✍️ From the editor’s desk,
Welcome to the 58th issue of Deep Engineering!
On August 3, NTT announced that it has signed a capital and business alliance with OptQC, the University of Tokyo spinout building optical quantum processors, with both companies aiming at a fault-tolerant machine of one million qubits. The announcement from NTT lays out a phased roadmap. The companies aim to complete the system architecture and key component technologies by fiscal 2027 alongside a practical 10,000 qubit system, then begin verification work in fiscal 2028 and deliver a platform for running optical and classical machines together the year after.
One million is the largest number the field has yet attached to a headline, and it arrives in a year when the reported metric already shifted once. Through the first half of 2026 vendors moved from physical qubit counts to logical qubit counts as the figure worth announcing. Both are real results, and both leave out the properties that decide what a machine can actually compute, which is where this issue picks up.
Sebastian Hassinger has read claims like these from inside the companies making them, first on the IBM Quantum team and later leading go to market for AWS Quantum Technologies. He wrote The New Quantum Era for readers without a physics background, and today he walks us through which numbers carry the information and which ones do not.
You can watch the full session or read the transcript here.
Let’s get started.
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Expert Insight
Qubit Count Measures Register Size, Not Capability
by Saqib Jan with Sebastian Hassinger
Three logical qubit results landed in the first half of 2026, and in each one the number worth reading is not the number in the headline. QuEra published 96 logical qubits encoded across 448 neutral atoms, a ratio near five to one. Quantinuum reported 48 logical qubits drawn from 98 trapped ions, closer to two to one. IBM’s published fault tolerance roadmap targets 200 logical qubits from roughly 10,000 physical ones by 2029, a ratio near fifty to one.
Those ratios differ by an order of magnitude because the underlying codes differ, and the choice of code decides whether a logical qubit corrects errors or only detects them. A count reported on its own collapses all of that into a single integer, which is why the integer tells you very little about what the machine computes.
Sebastian Hassinger worked on the IBM Quantum team and later led go to market for AWS Quantum Technologies, and he wrote The New Quantum Era to give engineers without a physics background enough grounding to read results like these directly. During our interview when I asked him what actually carries information in a milestone result, he began by taking apart the metric the field has reported for a decade. “Qubit count is effectively the register size of that computer,” he said. Then came the harder line, aimed at a claim the field now repeats freely. “Anytime you hear somebody saying quantum computing is just a matter of engineering now, be suspicious of that person’s claims.”
Register size bounds information, not computation
The clearest demonstration that a raw count says little about capability comes from IBM’s own hardware history. And Hassinger was there for it. The technical roadmap produced a chip called Condor at just over a thousand qubits, which he describes as genuinely valuable for the research and fabrication effort it took to build. But it saw little use. Connectivity between qubits on the chip was low and the noise proved very difficult to manage, so researchers went back to the smaller machines in the 127 to 133 qubit range, which were more capable in practice.
The reason is architectural rather than numerical. Register size sets an upper bound on how much information you can load, and nothing beyond that. Hassinger points out that QuEra’s 256 qubit Aquila holds 256 bits at a time, which sounds unremarkable until you entangle those qubits and produce a state vector of two to the 256, a computational space you cannot physically recreate on classical hardware. The capability lives in the entanglement structure and in how well the problem maps onto it, so a machine with more qubits and worse connectivity computes less than a smaller machine with better ones.
That same logic now applies one level up. A logical qubit is an encoding, not a unit, and its value depends on the code family, the physical to logical ratio, and the error model the code assumes. Codes at distance two detect errors without correcting them, which is a different guarantee from correction, and the difference produced considerable argument when Microsoft and Quantinuum reported reliable logical qubits in 2024 using error detection with post-selection. Two systems reporting the same logical qubit count can therefore be doing categorically different things.
Code distance carries the information a count discards
Hassinger’s proposed substitute is quite specific, and it happens to be exactly the property that separates those cases. Fidelity and noise are what matter, he reasons, particularly the fidelity of one and two qubit gates, where two qubit operations mean entanglement. Those figures are hard to extract from a published result, so he offers a proxy that survives summarization.
Read the resilience of the error correction code, expressed as a distance or a d value. That is roughly how many errors the system absorbs before the encoded information collapses and the computation is lost, so a higher distance means a more resilient machine. He points to the Willow experiment as the useful reference, roughly a hundred physical qubits arranged in a surface code presenting as one logical qubit at distance seven. That snapshot carries what you need without the underlying gate fidelities, because the only two questions that determine what you can run are “how many logical qubits do I get, and how resilient is that error correction.”
Pair a logical qubit count with its code, its distance, and its encoding ratio and you have something you can reason about. Take the count alone and you have an integer that happens to increase.
Speculation hardens into certainty before it reaches you
There is a structural reason the public record runs ahead of the results, and it operates on the way from the lab to the summary rather than inside the science. Hassinger named the pressure that drives it, and he was unusually direct about where the gap opens.
“Since at least the beginning of the Q2B conferences put on by QCWare, there has been a recurring chorus demanding to know what are quantum computing’s use cases, how will it be useful for enterprises,” he told us. “Marketing can be tempted to take speculative ideas and present them as certainties, stretching the truth about a scientist’s speculation to reframe it as definitive. The other question is always when, so timelines are also something that marketing can take liberties with.”
Both distortions are directional, which makes them correctable. A researcher’s conditional loses its condition, and a scientific dependency acquires a date. Reading a result back through those two transformations usually recovers something close to the original claim.
Roadmaps model engineering determinism onto unsolved physics
The deeper issue Hassinger identifies is a category problem. A roadmap projects milestones one year out, three years, five years, and he is blunt about what kind of document that is. “That’s an engineering document,” he says, “and engineering is much more deterministic than the underlying scientific breakthroughs that are required to enable the engineering to deliver those milestones.”
Transduction is the concrete case. Superconducting qubits operate inside a dilution refrigerator near absolute zero, and a refrigerator has finite volume, so scaling past one fridge means entangling qubits across separate cryostats. That requires converting the quantum state to a photonic frequency used in telecom, carrying it over fiber as what the field calls flying qubits, then converting back at the far end. None of the known conversion methods delivers the fidelity a reliable device needs, and nobody yet knows what closing that gap requires. “It’s not just hard work,” he says of that class of problem. “It’s a lot of hard work, but it’s also luck, because we don’t know what we don’t know.”
This is the reason he treats specifications and milestone dates as the least informative part of any hardware program, and the unsolved science underneath as the part that determines whether the dates mean anything. It is also why his sharpest formulation of the field’s position lands where it does. “A qubit is a very interesting device with no intrinsic commercial value,” he says, and converting it into something useful still depends on physics nobody has finished.
Classical simulability is the only threshold that changes anything
Hassinger’s position does not end in skepticism, because the field is converging on one measurable target regardless of which architecture reaches it. “The consensus is we need to deliver fault tolerant logical qubits at a scale that is not simulatable by a classical computer,” he says. “That’s the North Star we’re all sailing towards.” Once a system passes the point where your laptop or your GPU cluster can reproduce its output, running it on quantum hardware becomes necessary rather than interesting, and nothing before that crossing changes what you can compute.
That threshold also tells you where the physics pays off first, and his answer is narrower than the general coverage implies. Materials science arrives first because condensed matter behaviour maps naturally onto these systems, with small molecule chemistry close behind, while optimization, cryptography, and machine learning all wait on thousands of logical qubits.
So the technical reading is straightforward. When a new result publishes, work out its encoding ratio and its code distance before you compare it to anything, since those two numbers determine what the machine tolerates and the logical qubit count does not. And when a roadmap updates, separate the engineering milestones from the scientific dependencies underneath them and check which unsolved physics the far dates rest on. Then put the effort into building quantum intuition inside your own team, because recognizing the high dimensional structure in your own problems transfers whichever architecture crosses the threshold first.
In case you missed
Quantum Computing Beyond the Hype with Sebastian Hassinger
How to tell genuine quantum progress from hype, why qubit count misleads, where the technology delivers value first, and how a classical developer starts.
🛠️ Tool of the Week
Stim is an open source stabilizer circuit simulator maintained under Google’s quantumlib organisation, built for analysing quantum error correction circuits at speed.
Highlights
Derives a circuit’s actual code distance instead of relying on the number a vendor publishes.
Turns a noisy circuit into a detector error model ready to configure matching-based decoders.
Samples circuits with thousands of qubits and millions of operations at kilohertz rates.
Installs as a Python package and also runs as a C++ library or a command line tool.
📎 Tech Briefs
Anthropic ships inference hooks for Claude Enterprise - Governed prompts now route to customer security servers before inference, centralizing DLP across Claude Enterprise surfaces.
OpenAI cuts GPT-5.6 prices and adds Fast mode - Luna and Terra get lower API pricing, while Sol Fast mode offers 2.5× speed at 2× cost.
D-Wave and Nasdaq Verafin agree a quantum proof of concept for financial crime detection - Nasdaq Verafin will test D-Wave quantum-hybrid workflows on hundreds of financial-crime signals and network relationships.
Cloud SQL makes PSC reconciliation default - New or newly enabled PSC instances now close existing connections after project removal from allowed lists.
Palo Alto Networks integrates Prisma AIRS with Claude Enterprise - Prisma AIRS can inspect Claude prompts before inference, applying existing DLP policies across Claude Enterprise surfaces.
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Saqib Jan
Editor-in-Chief, Deep Engineering
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Code distance and encoding ratio say far more about a machine’s usable state than raw register size. The decoder and noise model belong beside them. Two systems can report the same nominal distance while behaving differently because the observed errors do not match the assumptions built into the code. That is the detail a qubit count erases.