Estimating the Effect of Crosstalk Error on Circuit Fidelity Using Noisy Intermediate-Scale Quantum Devices
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Abstract (arXiv)
Current advancements in technology have focused the attention of the quantum computing community toward exploring the potential of near-term devices whose computing power surpasses that of classical computers in practical applications. An unresolved central question revolves around whether the inherent noise in these devices can be overcome or whether any potential quantum advantage would be limited. There is no doubt that crosstalk is one of the main sources of noise in noisy intermediate-scale quantum (NISQ) systems, and it poses a fundamental challenge to hardware designs. Crosstalk between parallel instructions can corrupt quantum states and cause incorrect program execution. In this study, we present a necessary analysis of the crosstalk error effect on NISQ devices. Our approach is extremely straightforward and practical to estimate the crosstalk error of various multi-qubit devices. In particular, we combine the randomized benchmarking (RB) and simultaneous randomized benchmarking (SRB) protocol to estimate the crosstalk error from the correlation controlled-NOT (CNOT) gate. We demonstrate this protocol experimentally on 5-, 7-, & 16-qubit devices. Our results demonstrate the crosstalk error model of three different IBM quantum devices over the experimental week and compare the error variation against the machine, number of qubits, quantum volume, processor, and topology. We then confirm the improvement in the circuit fidelity on different benchmarks by up to 3.06x via inserting an instruction barrier, as compared with an IBM quantum noisy device which offers near-optimal crosstalk mitigation in practice. Finally, we discuss the current system limitation, its tradeoff on fidelity and depth, noise beyond the NISQ system, and mitigation opportunities to ensure that the quantum operation can perform its quantum magic undisturbed.
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References · 6
- 2021Enabling Multi-programming Mechanism for Quantum Computing in the NISQ Eracited by 22
- 2021Learning the noise fingerprint of quantum devicescited by 12
- 2022Random Quantum Neural Networks (RQNN) for Noisy Image Recognitioncited by 4
- 2020A Bayesian Approach for Characterizing and Mitigating Gate and Measurement Errorscited by 3
- 2021Prime Factorization Using Quantum Variational Imaginary Time Evolutioncited by 2
- 2021Analyzing crosstalk error in the NISQ eracited by 1
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