JigSaw: Boosting Fidelity of NISQ Programs via Measurement Subsetting

Abstract

Near-term quantum computers contain noisy devices, which makes it difficult to infer the correct answer even if a program is run for thousands of trials. On current machines, qubit measurements tend to be the most error-prone operations (with an average error-rate of 4%) and often limit the size of quantum programs that can be run reliably on these systems. As quantum programs create and manipulate correlated states, all the program qubits are measured in each trial and thus, the severity of measurement errors increases with the program size. The fidelity of quantum programs can be improved by reducing the number of measurement operations. We present JigSaw, a framework that reduces the impact of measurement errors by running a program in two modes. First, running the entire program and measuring all the qubits for half of the trials to produce a global (albeit noisy) histogram. Second, running additional copies of the program and measuring only a subset of qubits in each copy, for the remaining trials, to produce localized (higher fidelity) histograms over the measured qubits. JigSaw then employs a Bayesian post-processing step, whereby the histograms produced by the subset measurements are used to update the global histogram. Our evaluations using three different IBM quantum computers with 27 and 65 qubits show that JigSaw improves the success rate on average by 3.6x and up-to 8.4x. Our analysis shows that the storage and time complexity of JigSaw scales linearly with the number of qubits and trials, making JigSaw applicable to programs with hundreds of qubits.

arXiv comment13 pages, 14 figures, 7 tables

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• HiSEP-Q: A Highly Scalable and Efficient Quantum Control Processor for Superconducting Qubits
• Red-QAOA: Efficient Variational Optimization through Circuit Reduction
• Qonductor: A Cloud Orchestrator for Quantum Computing
• Maximum Likelihood Quantum Error Mitigation for Algorithms with a Single Correct Output
• Q-Cluster: Quantum Error Mitigation Through Noise-Aware Unsupervised Learning