Benchmarking quantum co-processors in an application-centric, hardware-agnostic and scalable way

Abstract

Existing protocols for benchmarking current quantum co-processors fail to meet the usual standards for assessing the performance of High-Performance-Computing platforms. After a synthetic review of these protocols -- whether at the gate, circuit or application level -- we introduce a new benchmark, dubbed Atos Q-score (TM), that is application-centric, hardware-agnostic and scalable to quantum advantage processor sizes and beyond. The Q-score measures the maximum number of qubits that can be used effectively to solve the MaxCut combinatorial optimization problem with the Quantum Approximate Optimization Algorithm. We give a robust definition of the notion of effective performance by introducing an improved approximation ratio based on the scaling of random and optimal algorithms. We illustrate the behavior of Q-score using perfect and noisy simulations of quantum processors. Finally, we provide an open-source implementation of Q-score that makes it easy to compute the Q-score of any quantum hardware.

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Cited by in corpus (19)

• Challenges and Opportunities in Quantum Optimization
• A density-matrix renormalization group algorithm for simulating quantum circuits with a finite fidelity
• Benchmarking quantum computers
• Optimizing resource efficiencies for scalable full-stack quantum computers
• Quantum utility -- definition and assessment of a practical quantum advantage
• Quantum pricing-based column-generation framework for hard combinatorial problems
• On-Premises Superconducting Quantum Computer for Education and Research
• QAOA with $N\cdot p\ge 200$
• Benchmarking Quantum Generative Learning: A Study on Scalability and Noise Resilience using QUARK
• Benchmarking Quantum Computers: Towards a Standard Performance Evaluation Approach
• A Classically Efficient Quantum Scalable Fermi-Hubbard Benchmark
• Quantum Mini-Apps: A Framework for Developing and Benchmarking Quantum-HPC Applications
• Evaluating Three Levels of Quantum Metrics on Quantum-Inspire Hardware
• AppQSim: Application-oriented benchmarks for Hamiltonian simulation on a quantum computer
• Extending the Q-score to an Application-level Quantum Metric Framework
• Q-Profile: Profiling Tool for Quantum Control Stacks applied to the Quantum Approximate Optimization Algorithm
• Quantum Optimization Benchmarking Library - The Intractable Decathlon
• Missing Puzzle Pieces in the Performance Landscape of the Quantum Approximate Optimization Algorithm
• Entropy density benchmarking of near-term quantum circuits