Application-level benchmarks
Measure end-to-end performance on programs representative of real workloads, from algorithm subroutines to full application suites.
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IonQ's single-number application metric, the largest width at which a six-algorithm circuit suite clears a 1/e fidelity bar, retired in 2025 in favor of industry-standard metrics.
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MetriQs-France benchmark suite scoring whole quantum stacks on optimization, linear systems, many-body simulation, and factoring, aggregated into one user-weighted figure of merit.
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More than 1.5 million pre-encoded qubit Hamiltonians (spin models, chemistry, and combinatorial optimization) supplying standardized problem instances for application-level quantum benchmarking.
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IonQ's MLPerf-inspired framework scoring whole quantum workloads end to end by solution quality and time-to-solution, wall time from job submission to a result that meets a predefined quality threshold.
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Scores digital and analog quantum processors by the largest system size at which they reproduce quenched transverse-field Ising correlation functions within a chosen error threshold.
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Atos/Eviden's application-level metric: the largest MaxCut instance a quantum system can solve effectively, judged by beating random guessing by a set fraction of optimal-solver scaling.
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PNNL's collection of OpenQASM 2 circuits, spanning chemistry to cryptography at 2 to 433+ qubits, used to evaluate NISQ hardware, compilers, and simulators.
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DARPA Quantum Benchmarking program's ground-state energy estimation benchmark: classical and quantum solvers are scored on solvability, accuracy, and runtime over a shared library of molecular Hamiltonians.
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Early hybrid quantum-classical benchmark scoring how well a trained shallow circuit samples the bars-and-stripes distribution, reported as an F1 score of precision and recall.
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Open-source consortium suite that scores devices on ~16 algorithm workloads, from Bernstein-Vazirani to VQE and MaxCut, via normalized fidelity on width-by-depth volumetric plots.
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Xanadu's suite for benchmarking quantum machine-learning models against out-of-the-box classical baselines, which won on every task tested.
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Open library of ten classically hard, practically motivated optimization problem classes (the Intractable Decathlon) with common reporting rules for quantum and classical solvers.
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TU Delft benchmark suite that runs QAOA and VQE optimization workloads end to end and scores runtime, accuracy, scalability, and capacity.
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BMW Group's open-source framework for defining, orchestrating, and reproducing application-level quantum benchmarks drawn from industry use cases.
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Scalable, hardware-agnostic suite of eight application-level benchmarks, with a six-dimensional feature vector that profiles how each workload stresses a device.