There is no single performance number that makes a quantum computer directly comparable to a classical supercomputer. They run different kinds of workloads, and metrics such as qubit count, quantum volume, CLOPS, and FLOP/s describe different things. A meaningful comparison uses the same well-defined task, requires the same result quality, and measures the work end to end—including the relevant classical and quantum processing.
What makes a fair comparison?
Start with the job each system is being asked to do, not its headline hardware number. A quantum processor executes quantum circuits for selected tasks; a classical supercomputer runs conventional numerical and data-intensive workloads. To compare them, specify the problem, the acceptable result, and which parts of each system’s work count toward the measurement.
| Comparison axis | Quantum system | Classical supercomputer | What to match or report |
|---|---|---|---|
| Workload | A named quantum application or circuit | A classical implementation of the same task | Confirm both solve the same problem and produce equivalent outputs. |
| Result quality | Fidelity, error rate, or target success probability | Accuracy or error tolerance | Require the same acceptable result, rather than comparing runtimes at different quality levels. |
| Workload capacity | Circuit width and depth, or a stated capability region | Benchmark problem size and relevant memory or workload limits | Describe the tested problem; a processor’s qubit count alone does not show what it can successfully execute. |
| Throughput | A named quantum metric such as CLOPS, with protocol version and conditions | A named benchmark such as HPL or HPCG | Keep each score attached to its benchmark. Unlike units are not directly convertible. |
| Time | End-to-end wall-clock time for the specified task | End-to-end wall-clock time for the same task | State consistently whether compilation, setup, data movement, mitigation or correction, and post-processing are included. |
| Resources | Cost and energy, if measured | Cost and energy, if measured | Compare only figures with a supported, comparable system boundary. |
Quantum workloads also involve classical software and hardware: compiling and scheduling circuits, controlling execution, and processing results. If one approach needs substantial work outside the processor, that work belongs in the comparison when it is part of the actual task.
What quantum computer performance metrics measure
Quantum volume: a particular circuit reliability test
Quantum volume compresses circuit width and depth into one figure. Its protocol runs square random circuits and validates performance through a Heavy Output Generation sampling task. A device that validates circuits of size n receives a score of 2n under the benchmark reference.
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The score reflects several factors, including gate fidelity, coherence time, chip topology, and transpilation. But it is not an application runtime or a comparison score against classical supercomputers. The square-circuit profile is only one kind of workload, and the test focuses on a subset of the processor’s best qubits rather than necessarily representing the full chip.
CLOPS: hybrid circuit throughput, with protocol details required
CLOPS measures how quickly a quantum system and its classical runtime execute batches of parameterized circuits. In the described workflow, circuits run sequentially and the output of one informs the parameters of the next, so the measure incorporates both quantum execution and classical processing.
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A CLOPS value is interpretable only with its protocol and conditions. The historical Quantum Volume-derived metric and the hardware-aware update define circuit layers differently; the hardware-aware form takes device connectivity and parallelizable gates into account. Check the version, layer definition, circuit conditions, and wall-clock inclusions before comparing two reported values. A CLOPS rate is not a FLOP/s rate.
Application measures and capability regions
Application-oriented quantum benchmarks can vary problem size and map output fidelity across circuit width and depth. They may also measure parts of the execution pipeline and time to solution. This is more relevant to an application claim than a generic qubit count, but it cannot establish an advantage without a comparable classical implementation, the same quality target, and a transparent runtime boundary.
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Sandia’s QUOPS framework describes a quantum computer’s capability region: the programs it can execute successfully, organized by circuit width and gate count. It also defines a QUOPS rate for how quickly the system executes those units, and is intended to apply to physical-qubit and fault-tolerant systems. QUOPS is a developing quantum-side framework, not a conversion to classical FLOP/s or a replacement for a task-matched classical baseline.
What classical supercomputer metrics measure
Classical benchmark results are meaningful only with their benchmark and precision attached. In its 2025 report, TOP500 listed El Capitan at 1.742 exaflop/s on HPL, 17.41 petaflop/s on HPCG, and 16.7 exaflop/s on HPL-MxP. These are results from that report, not timeless system specifications.
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| Benchmark | El Capitan result in TOP500’s 2025 report | What to keep in mind |
|---|---|---|
| HPL | 1.742 exaflop/s | A result for the HPL numerical workload. |
| HPCG | 17.41 petaflop/s | A complementary benchmark; the report’s system entry gives this more precise figure. |
| HPL-MxP | 16.7 exaflop/s | A result in the mixed-precision benchmark category. |
HPL, HPCG, and HPL-MxP measure different workloads or precision regimes, so their scores are not interchangeable. These figures also cannot be used to calculate a speed ratio against a quantum benchmark. Rankings and submissions change; consult the relevant list edition and system entry when using a reported result.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to compare a quantum system with a supercomputer
- Name the task. State the problem and whether the result is a useful application output or a special-purpose sampling or benchmark task.
- Set the result-quality target. Specify the required accuracy, fidelity, error tolerance, or success probability for both implementations.
- Define the system boundary. Say whether the clock includes compilation, scheduling, setup, data movement, error mitigation or correction, control, and post-processing. Include material stages or identify exclusions.
- Report end-to-end time to solution. Use wall-clock time for the stated task and quality target. Show benchmark-specific hardware measures separately rather than substituting them for task time.
- Identify the configuration and date. Name the device, software and runtime configuration, benchmark version, and measurement date; results depend on these details.
- Compare cost and energy only when supported. Use figures measured across comparable boundaries. A throughput score does not establish either resource measure.
What the numbers can—and cannot—tell you
- Qubit count and classical FLOP/s describe different resources; neither says whether a system completes the same useful task.
- Quantum volume characterizes one square-circuit reliability test, not every application or the full chip.
- CLOPS describes hybrid circuit throughput, and its protocol variants need to be identified before comparing values.
- HPL, HPCG, and HPL-MxP are distinct classical benchmark results and must remain labeled as such.
The available benchmark descriptions and classical results do not establish general quantum superiority over classical supercomputers on useful workloads. An advantage claim should be limited to the studied task and baseline unless matched evidence supports a broader conclusion. The comparison requires a quantum and classical implementation of the same task, run to the same quality target, with a transparent and comparable accounting of time and resources.
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