Before writing this, I realized NVIDIA Ising already showed up in Chapter 12 of the book, the one on AI-assisted calibration and error correction. That chapter only had NVIDIA's side of it. A few days after that post went up, IEEE Spectrum ran a piece by Edd Gent that talked to Q-CTRL, Riverlane, IBM, and Google about the same problem, and the picture got more interesting. This post is what Chapter 12 looks like with that reporting folded in.
I have spent the past several months writing about quantum computing. Topological qubits, neutral atoms, silicon spin, photonics. Each one has its own physics, its own failure modes, its own argument for why it will win. Putting the book together, I noticed something missing from every chapter: what runs the thing once you turn it on.
The answer is classical computers. A lot of them. A recent IEEE Spectrum piece by Edd Gent lays out why, and it changes how I think about the supply chain side of this industry too.
Qubits are not reliable the way transistors are. A transistor comes off the line and works, billions of times, with no babysitting. A qubit drifts. It decoheres. It needs constant tuning just to stay usable, and that tuning happens in two stages. The first, called “bring up,” determines the frequency each qubit resonates at, how long it holds its quantum state, how sensitive it is to control pulses, and how strongly it interacts with its neighbors. Every one of those numbers affects error rates and how the qubit responds to control signals.
Done by hand, bring up still requires someone with a PhD and can take days or weeks, according to Jay Guilmart, lead product manager at Q-CTRL. The process resists scripting because each step depends on the result of the one before it. So Q-CTRL built software that examines each measurement, diagnoses what failed, and decides whether to proceed, repeat, or back up, rather than running a fixed sequence. Calibration does not end once a machine is running either. Parameters drift over time, so the system needs “runtime recalibration” to nudge things back into spec. But every cycle spent recalibrating is a cycle not spent running a circuit. As Guilmart puts it, if recalibration eats your uptime, the high fidelity you maintained is worthless.
Then there is error correction. Quantum information gets spread across many physical qubits to form a “logical qubit,” so that errors in individual qubits can be caught and fixed without destroying the encoded information. Measuring a qubit directly collapses its state, so error detection works indirectly through “parity checks,” which compare pairs of qubits to see if they agree. The pattern of agreements and disagreements is called a syndrome, and classical algorithms called decoders read that syndrome to locate errors.
This has to happen fast. Superconducting and silicon spin qubits hold their state for only microseconds to milliseconds, so decoding has to finish inside that window or the algorithm stalls. That rules out general-purpose processors. Decoders run on FPGAs or ASICs, chips built specifically for speed, according to Jerry Chow, CTO of quantum-centric supercomputing at IBM.
This is where the AI argument gets interesting, and where the field splits. In April, Nvidia released two models aimed at this problem. One uses a vision-language model to read calibration plots and feed an AI agent that decides how to adjust the processor. The other uses a convolutional neural network to catch the simple, localized errors that make up most faults, passing only the harder cases to a traditional decoder, for roughly a 2x speedup. Sam Stanwyck, Nvidia's director of quantum product, makes the case for AI on inference speed: models take time to train, but once trained they run fast and parallelize across chips as qubit counts grow.
Marco Ghibaudi, VP of engineering at Riverlane, pushes back on a different axis. Running anything on a GPU adds latency, even with massive throughput. His framing: you can have a very fat pipe, but if it is also a very long pipe, the data still arrives late. Riverlane's approach has been to shorten the pipe itself and make every stage faster, rather than chase raw throughput.
IBM's Chow lands in a similar place on calibration. He thinks AI shows real promise for understanding new architectures or unfamiliar circuit types, where you do not yet know what you are looking for. But for a well-characterized device where you are hunting small deviations from a known baseline, simpler physics-informed methods are cheaper and faster. Google's Adam Zalcman frames the two approaches as complementary rather than competing: neural networks are good at finding hidden patterns in syndrome data that handwritten decoders miss, so Google is building architectures that can run both, including its AlphaQubit 2 model.
Andi Gu, a Harvard PhD student working on AI decoders, takes the long view. He expects the “bitter lesson” to apply here eventually, the same pattern that played out in other parts of AI: general learning methods, given enough data and a large enough model, outperform hand-built algorithms over time. The obstacle right now is latency. Gu's group is working on shrinking AI decoders enough to fit on an FPGA, though smaller models trade off some accuracy, and finding that balance is still unresolved.
What stands out reading this against the QSCA work is how much of the “quantum workforce” conversation undersells this layer. When people talk about quantum jobs, they tend to mean physicists and people who understand qubits. But calibration software, decoder design, FPGA and ASIC engineering, and the AI models being built to assist both are squarely classical computing jobs. Guilmart's own warning, that calibration overhead will “blow up” past a thousand qubits and that current techniques will not scale, is as much a hiring problem as a technical one. Nobody in the article claims to have this solved, and Guilmart says plainly that no one is winning this battle today.
Every qubit type I have written about, no matter how exotic, sits on top of a rack of classical silicon doing the unglamorous work of keeping it alive.
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Download Quantum from the Ground Up, a free PDF now available at gordostuff.com. This first edition covers posts through June 2026 and runs 50 pages across 19 chapters. It is written for someone entering the quantum workforce, or seriously considering it, who has a technical background but has not taken a graduate course in quantum mechanics.
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