mlsanityData to 14 Oct 2026

Near-Term Quantum Computing Techniques: Variational Quantum Algorithms, Error Mitigation, Circuit Compilation, Benchmarking and Classical Simulation

arXiv 2211.08737 ↗First posted 16 Nov 2022quant-ph cs.AI cs.LGcited by 13

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Abstract (arXiv)

Quantum computing is a game-changing technology for global academia, research centers and industries including computational science, mathematics, finance, pharmaceutical, materials science, chemistry and cryptography. Although it has seen a major boost in the last decade, we are still a long way from reaching the maturity of a full-fledged quantum computer. That said, we will be in the Noisy-Intermediate Scale Quantum (NISQ) era for a long time, working on dozens or even thousands of qubits quantum computing systems. An outstanding challenge, then, is to come up with an application that can reliably carry out a nontrivial task of interest on the near-term quantum devices with non-negligible quantum noise. To address this challenge, several near-term quantum computing techniques, including variational quantum algorithms, error mitigation, quantum circuit compilation and benchmarking protocols, have been proposed to characterize and mitigate errors, and to implement algorithms with a certain resistance to noise, so as to enhance the capabilities of near-term quantum devices and explore the boundaries of their ability to realize useful applications. Besides, the development of near-term quantum devices is inseparable from the efficient classical simulation, which plays a vital role in quantum algorithm design and verification, error-tolerant verification and other applications. This review will provide a thorough introduction of these near-term quantum computing techniques, report on their progress, and finally discuss the future prospect of these techniques, which we hope will motivate researchers to undertake additional studies in this field.

Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). Builds on 20 of its 272 references that are in the map. First posted on arXiv 16 Nov 2022.

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20 of 272 references.

References · 20

  1. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,498
  2. 2022Generative Adversarial Networkscited by 5,627
  3. 2020Variational Quantum Algorithmscited by 692
  4. 2018Supervised learning with quantum enhanced feature spacescited by 665
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  8. 2020Noise-Induced Barren Plateaus in Variational Quantum Algorithmscited by 193
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  11. 2019Stochastic gradient descent for hybrid quantum-classical optimizationcited by 59
  12. 2019Learning to Optimize Variational Quantum Circuits to Solve Combinatorial Problemscited by 31
8 more
  1. 2020Quantum versus Classical Generative Modelling in Financecited by 21
  2. 2020Natural Evolutionary Strategies for Variational Quantum Computationcited by 15
  3. 2018Experimental Implementation of a Quantum Autoencoder via Quantum Adderscited by 12
  4. 2020Towards a Distributed Quantum Computing Ecosystemcited by 11
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  6. 2019Optimal Space-Depth Trade-Off of CNOT Circuits in Quantum Logic Synthesiscited by 6
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  8. 2022Lifetime-based Optimization for Simulating Quantum Circuits on a New Sunway Supercomputercited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 3; 2024: 4; 2025: 4; 2026: 2.

13 papers.

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