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Hardware benchmarks (e.g. error rates, Quantum Volume, CLOPS)

Hardware benchmarks quantify how capable a quantum processor is for running real workloads. Basic figures such as single- and two-qubit error rates or readout errors characterize the fidelity of elementary operations, but do not fully capture system-level performance. Aggregate metrics like quantum volume (QV) or circuit layer operations per second (CLOPS) instead quantify system-level measures such as throughput or connectivity. Together, such benchmarks guide hardware roadmaps, inform algorithm design, and should be interpreted as a complementary set rather than a single “score”.

Topics in the Subfield Software for quantum computing hardware:

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Error mitigation including noise learning at the software and hardware levels

Error mitigation refers to techniques that reduce the impact of noise in quantum computations without requiring a…

Resource estimation (e.g. gate count, qubit scaling, circuit depth)

Transpilation (e.g. qubits, gates)

Transpilation is the process of rewriting a quantum program so that it…

Noise models (e.g. depolarizing noise, cross talk, Hamiltonian learning)

Quantum processors are inherently fragile. Interactions with the environment and imperfect control lead to…

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Hardware benchmarks (e.g. error rates, Quantum Volume, CLOPS)

Topics in the Subfield Software for quantum computing hardware:

Error mitigation including noise learning at the software and hardware levels

Resource estimation (e.g. gate count, qubit scaling, circuit depth)

Transpilation (e.g. qubits, gates)

Noise models (e.g. depolarizing noise, cross talk, Hamiltonian learning)

Partners

Hardware bench­marks (e.g. error rates, Quantum Volume, CLOPS)

Topics in the Subfield Software for quantum compu­ting hardware:

Error mitiga­tion inclu­ding noise learning at the software and hardware levels

Resource estima­tion (e.g. gate count, qubit scaling, circuit depth)

Trans­pi­la­tion (e.g. qubits, gates)

Noise models (e.g. depola­ri­zing noise, cross talk, Hamil­to­nian learning)

Partners

Hardware benchmarks (e.g. error rates, Quantum Volume, CLOPS)

Topics in the Subfield Software for quantum computing hardware:

Error mitigation including noise learning at the software and hardware levels

Resource estimation (e.g. gate count, qubit scaling, circuit depth)

Transpilation (e.g. qubits, gates)

Noise models (e.g. depolarizing noise, cross talk, Hamiltonian learning)