mlsanityData to 14 Oct 2026

Enabling Multi-programming Mechanism for Quantum Computing in the NISQ Era

arXiv 2102.05321 ↗First posted 10 Feb 2021cs.AR quant-phcited by 22

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Abstract (arXiv)

NISQ devices have several physical limitations and unavoidable noisy quantum operations, and only small circuits can be executed on a quantum machine to get reliable results. This leads to the quantum hardware under-utilization issue. Here, we address this problem and improve the quantum hardware throughput by proposing a Quantum Multi-programming Compiler (QuMC) to execute multiple quantum circuits on quantum hardware simultaneously. This approach can also reduce the total runtime of circuits. We first introduce a parallelism manager to select an appropriate number of circuits to be executed at the same time. Second, we present two different qubit partitioning algorithms to allocate reliable partitions to multiple circuits - a greedy and a heuristic. Third, we use the Simultaneous Randomized Benchmarking protocol to characterize the crosstalk properties and consider them in the qubit partition process to avoid the crosstalk effect during simultaneous executions. Finally, we enhance the mapping transition algorithm to make circuits executable on hardware using a decreased number of inserted gates. We demonstrate the performance of our QuMC approach by executing circuits of different sizes on IBM quantum hardware simultaneously. We also investigate this method on VQE algorithm to reduce its overhead.

Key facts. Cited by 22 papers in the map (data through 14 Oct 2026). First posted on arXiv 10 Feb 2021.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 9; 2022: 5; 2023: 4; 2024: 2; 2025: 1; 2026: 1.

22 papers.

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  1. 2021The Inductive Bias of Quantum Kernelscited by 91
  2. 2021Parametrized quantum policies for reinforcement learningcited by 90
  3. 2021Reinforcement learning for optimization of variational quantum circuit architecturescited by 77
  4. 2021Towards understanding the power of quantum kernels in the NISQ eracited by 43
  5. 2021Exponentially Many Local Minima in Quantum Neural Networkscited by 22
  6. 2021The dilemma of quantum neural networkscited by 20
  7. 2022The Complexity of NISQcited by 13
  8. 2022Quantum Local Differential Privacy and Quantum Statistical Query Modelcited by 12
  9. 2022Monte Carlo Tree Search based Hybrid Optimization of Variational Quantum Circuitscited by 10
  10. 2023Splitting and Parallelizing of Quantum Convolutional Neural Networks for Learning Translationally Symmetric Datacited by 10
  11. 2023Analyzing Convergence in Quantum Neural Networks: Deviations from Neural Tangent Kernelscited by 8
  12. 2023ShadowNet for Data-Centric Quantum System Learningcited by 8
10 more
  1. 2024Quantum Circuit Synthesis and Compilation Optimization: Overview and Prospectscited by 8
  2. 2025Quantum-Informed Machine Learning for Predicting Spatiotemporal Chaos with Practical Quantum Advantagecited by 7
  3. 2021Improved FRQI on superconducting processors and its restrictions in the NISQ eracited by 6
  4. 2022Power of Quantum Generative Learningcited by 6
  5. 2021Probabilistic Graphical Models and Tensor Networks: A Hybrid Frameworkcited by 5
  6. 2021Quantum Topological Data Analysis with Linear Depth and Exponential Speedupcited by 4
  7. 2022Shuffle-QUDIO: accelerate distributed VQE with trainability enhancement and measurement reductioncited by 1
  8. 2023Error mitigation of entangled states using brainbox quantum autoencoderscited by 1
  9. 2024Estimating the Effect of Crosstalk Error on Circuit Fidelity Using Noisy Intermediate-Scale Quantum Devicescited by 0
  10. 2026Fidelity-Aware Scheduling of Quantum Circuits on Multi-QPU Systemscited by 0