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IBM’s Nighthawk r2 quantum processor generated one million samples from a random quantum circuit in 19 seconds. The study’s authors estimate that a specified classical simulation approach would take more than a century on the Frontier supercomputer to generate a comparable sample set. That century figure is a model-based estimate under stated fidelity and memory assumptions—not a timed Frontier run, and not evidence that quantum computers can complete ordinary tasks 110 years faster.
What did IBM’s quantum computer do in 19 seconds?
The result comes from a random-circuit-sampling experiment reported in a September 2026 preprint by Tigran Sedrakyan and co-authors. The team used IBM’s 120-qubit Nighthawk r2 superconducting processor, identified in the study as ibm_phoenix, but ran the quoted experiment on 61 qubits. The circuit had a square-lattice layout, native CZ gates and 36 cycles. The authors say they used IBM’s standard cloud execution stack without benchmark-specific calibration. Read the preprint.
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Random-circuit sampling means running a selected random quantum circuit and collecting bit-string outputs that follow its output distribution. In this case, the measured task was collecting an ensemble of one million samples. The authors report that the 36-cycle run took 19 seconds and had a linear cross-entropy benchmarking fidelity, or FXEB, of 2.3 × 10−3.
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Where does the “more than 100 years” estimate come from?
The researchers model a classical method called bounded-fidelity rejection sampling to estimate the work needed to produce a comparable ensemble at the reported fidelity. Their estimate is 1.2 × 1027 machine operations; they characterize the corresponding runtime as more than a century on Frontier, assuming favorable memory conditions. This is the paper authors’ estimate for that particular method and target—not a report that Frontier actually ran the full comparison for a century, nor a forecast covering every classical algorithm or future computer.
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The comparison depends on the chosen circuit, sample count, fidelity target, classical simulation method and memory assumptions. A different algorithm, hardware configuration or target could change the estimate. The authors also report that two fidelity-estimation approaches—mirror benchmarking and cross-entropy benchmarking—agree across the measured circuit depths; that supports their account of the quantum output, but does not turn the classical runtime into a directly timed result.
Is this a practical quantum advantage?
It is a claimed advantage for a specialized benchmark, not a demonstration that the processor solved a useful chemistry, logistics, finance or consumer problem faster. Random-circuit sampling tests the ability to execute circuits and sample their outputs; the benchmark does not itself deliver a practical application answer.
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IBM’s own framework says a quantum-advantage claim should involve an output that can be rigorously validated and a task where quantum computing offers superior efficiency, cost-effectiveness or accuracy over classical computation. IBM also notes that quantum processors may augment classical workflows. This is IBM’s stated framework, not a universal definition adopted by every researcher. IBM’s explanation of quantum advantage provides its criteria and context.
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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesThe preprint authors call their result, to their knowledge, the first demonstration of quantum advantage for “vanilla” random-circuit sampling on a commercially and broadly accessible processor that non-experts can replicate. That is the authors’ characterization of this benchmark. The paper was submitted to arXiv on 23 September 2026; the available version is v1, a preprint rather than an established peer-reviewed journal publication.
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How this differs from IBM’s other quantum demonstrations
IBM has reported other milestones, but they involve different tasks and should not be treated as direct speed comparisons with the Nighthawk sampling result.
Quick Recap
| Demonstration | Workload and hardware | What the reported result establishes |
|---|---|---|
| Nighthawk r2, 2026 | Random-circuit sampling; 61 physical qubits used on a 120-qubit processor | One million samples in 19 seconds; the classical comparison is a modeled estimate for a specified method and assumptions. Study preprint. |
| Eagle, 2023 | Material-system spin dynamics on a 127-qubit processor, using error mitigation | A separate effort to model a physical system and compare its predictions with classical simulations. It was not the Nighthawk sampling benchmark. IBM’s 2023 announcement. |
| IBM and University of Chicago, 2026 | Logical-circuit demonstration; 70 logical qubits | A separate demonstration reporting approximately 15 minutes of quantum computation, not a random-circuit-sampling result. IBM’s announcement. |
What to take away
- The 19 seconds refers to collecting one million outputs for a 36-cycle random-circuit-sampling experiment using 61 qubits.
- The “more than a century” figure is the authors’ modeled estimate for a specified classical approach on Frontier, with favorable memory assumptions and a stated fidelity target.
- The result is a notable benchmark claim, but it does not show that quantum computers have a general speed advantage for everyday computing or a demonstrated practical application.
- The available paper is a September 2026 arXiv v1 preprint, not a peer-reviewed journal publication.
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