ArsoNISQ: Analyzing Quantum Algorithms on Near-Term Architectures

• URL: http://arxiv.org/abs/2301.07264v2
• Date: Mon, 24 Jul 2023 16:02:17 GMT
• Title: ArsoNISQ: Analyzing Quantum Algorithms on Near-Term Architectures
• Authors: Sebastian Brandhofer, Simon Devitt, Ilia Polian
• Abstract summary: We introduce the ArsoNISQ framework that determines the tolerable error rate of a given quantum algorithm. ArsoNISQ is based on simulations of quantum circuits subject to errors according to the Pauli error model.
• Score: 0.18188255328029254
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While scalable, fully error corrected quantum computing is years or even decades away, there is considerable interest in noisy intermediate-scale quantum computing (NISQ). In this paper, we introduce the ArsoNISQ framework that determines the tolerable error rate of a given quantum algorithm computation, i.e. quantum circuits, and the success probability of the computation given a success criterion and a NISQ computer. ArsoNISQ is based on simulations of quantum circuits subject to errors according to the Pauli error model. ArsoNISQ was evaluated on a set of quantum algorithms that can incur a quantum speedup or are otherwise relevant to NISQ computing. Despite optimistic expectations in recent literature, we did not observe quantum algorithms with intrinsic robustness, i.e. algorithms that tolerate one error on average, in this evaluation. The evaluation demonstrated, however, that the quantum circuit size sets an upper bound for its tolerable error rate and quantified the difference in tolerate error rates for quantum circuits of similar sizes. Thus, the framework can assist quantum algorithm developers in improving their implementation and selecting a suitable NISQ computing platform. Extrapolating the results into the quantum advantage regime suggests that the error rate of larger quantum computers must decrease substantially or active quantum error correction will need to be deployed for most of the evaluated algorithms.

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