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.
Highlights
Recent years have witnessed great progress in the field of quantum technologies, whether on the hardware side—with growing computer sizes and quantum operation fidelities— or on the software side—with many algorithmic improvements
To define the Q-score, we carefully investigate the sizedependence of the average performance of random and optimal classical algorithms, as well as the quantum approximate optimization algorithm (QAOA) quantum algorithm, for solving the MaxCut problem on classes of random graphs
One major reason for this deficiency is that randomized benchmarking (RB) gives little information about crosstalk errors, which influence the performance of a quantum processing units (QPUs) at the circuit level
Summary
Recent years have witnessed great progress in the field of quantum technologies, whether on the hardware side—with growing computer sizes and quantum operation fidelities— or on the software side—with many algorithmic improvements. Many different hardware platforms with many different algorithmic ideas are vying for this goal today This diversity of quantum hardware and software candidates for quantum advantage requires a precise metric of success in order to appraise the relative power of each quantum computing stack for outperforming classical computers. This metric will provide a much-needed synthetic overview of the current status of the field to end-users such as the highperformance-computing (HPC) community, but it will help fuel the quantum community’s efforts toward real-world applications. We explain how to run this benchmark using an open-source script we provide online (Section IV)
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