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Systematic benchmarking of quantum computers: status and recommendations
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Architectures for quantum computing can only be scaled up when they are accompanied by suitable benchmarking techniques. The document provides a comprehensive overview of the state and recommendations for systematic benchmarking of quantum computers. Benchmarking is crucial for assessing the performance of quantum computers, including the hardware, software, as well as algorithms and applications. The document highlights key aspects such as component-level, system-level, software-level, HPC-level, and application-level benchmarks. Component-level benchmarks focus on the performance of individual qubits and gates, while system-level benchmarks evaluate the entire quantum processor. Software-level benchmarks consider the compiler's efficiency and error mitigation techniques. HPC-level and cloud benchmarks address integration with classical systems and cloud platforms, respectively. Application-level benchmarks measure performance in real-world use cases. The document also discusses the importance of standardization to ensure reproducibility and comparability of benchmarks, and highlights ongoing efforts in the quantum computing community towards establishing these benchmarks. Recommendations for future steps emphasize the need for developing standardized evaluation routines and integrating benchmarks with broader quantum technology activities.
Forward citations
Cited by 8 Pith papers
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Clifford Volume and Free Fermion Volume: Complementary Scalable Benchmarks for Quantum Computers
Two new classically verifiable benchmark scores, Clifford Volume and Free Fermion Volume, are defined, simulated under noise, and Clifford Volume is measured on the Quantinuum H2-1 device as 34 qubits.
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Quantum Computer Benchmarking: An Explorative Systematic Literature Review
A systematic review of 329 quantum benchmarking studies yields a stack-aligned taxonomy and definitions for hardware-, software-, and application-focused benchmarks.
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Quantum Fidelity-per-Cost: A Metric for Evaluation of Quantum Computing Systems
Cost-aware ranking of cloud QPUs via QFC disagrees with fidelity-only ranking; billing model, not hardware, fixes how the score scales with shot count.
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Design and Benchmarking of a Quantum Photonic Chip
RP000, a room-temperature CMOS photonic three-qubit processor, delivers higher or comparable accuracy to parameter-matched classical nets on ML classification and better noise tolerance than a superconducting processor.
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CleanQRL: Lightweight Single-file Implementations of Quantum Reinforcement Learning Algorithms
The paper introduces CleanQRL, a collection of single-file implementations of quantum reinforcement learning algorithms designed to make QRL research easier to replicate and compare.
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A Survey on Integrating Quantum Computers into High Performance Computing Systems
A structured review of 107 papers on quantum-HPC integration, organized into seven categories, finds a flourishing tool ecosystem but little standardization.
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