How Parallel Circuit Execution Can Be Useful for NISQ Computing?
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Abstract (arXiv)
Quantum computing is performed on Noisy Intermediate-Scale Quantum (NISQ) hardware in the short term. Only small circuits can be executed reliably on a quantum machine due to the unavoidable noisy quantum operations on NISQ devices, leading to the under-utilization of hardware resources. With the growing demand to access quantum hardware, how to utilize it more efficiently while maintaining output fidelity is becoming a timely issue. A parallel circuit execution technique has been proposed to address this problem by executing multiple programs on hardware simultaneously. It can improve the hardware throughput and reduce the overall runtime. However, accumulative noises such as crosstalk can decrease the output fidelity in parallel workload execution. In this paper, we first give an in-depth overview of stateof-the-art parallel circuit execution methods. Second, we propose a Quantum Crosstalk-aware Parallel workload execution method (QuCP) without the overhead of crosstalk characterization. Third, we investigate the trade-off between hardware throughput and fidelity loss to explore the hardware limitation with parallel circuit execution. Finally, we apply parallel circuit execution to VQE and zero-noise extrapolation error mitigation method to showcase its various applications on advancing NISQ computing.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 1 Dec 2021.
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Citing papers per year: 2023: 1; 2024: 1; 2025: 1.
3 papers.
- 2023Splitting and Parallelizing of Quantum Convolutional Neural Networks for Learning Translationally Symmetric Datacited by 10
- 2024Reinforcement learning with learned gadgets to tackle hard quantum problems on real hardwarecited by 7
- 2025Computational Performance Bounds Prediction in Quantum Computing with Unstable Noisecited by 0
- 2025Computational Performance Bounds Prediction in Quantum Computing with Unstable Noise22 Jul 2025
- 2024Reinforcement learning with learned gadgets to tackle hard quantum problems on real hardware31 Oct 2024
- 2023Splitting and Parallelizing of Quantum Convolutional Neural Networks for Learning Translationally Symmetric Data12 Jun 2023