mlsanityData to 7 Oct 2026

Quantum Circuit Synthesis and Compilation Optimization: Overview and Prospects

arXiv 2407.00736 ↗First posted 30 Jun 2024quant-ph cs.ET cs.LGcited by 8

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Abstract (arXiv)

Quantum computing is a promising paradigm that may overcome the current computational power bottlenecks. The increasing maturity of quantum processors provides more possibilities for the development and implementation of quantum algorithms. As the crucial stages for quantum algorithm implementation, the logic circuit design and quantum compiling have also received significant attention, which covers key technologies, e.g., quantum logic circuit synthesis (also widely known as quantum architecture search) and optimization, as well as qubit mapping and routing. Recent studies suggest that the scale and precision of related algorithms are steadily increasing, especially with the integration of artificial intelligence methods. In this survey, we systematically review and summarize a vast body of literature, exploring the feasibility of an integrated design and optimization scheme that spans from the algorithmic level to quantum hardware, combining the steps of logic circuit design and compilation optimization. Leveraging the exceptional cognitive and learning capabilities of AI algorithms, it becomes more possible to reduce manual design costs, enhance the precision and efficiency of execution, and facilitate the implementation and validation of the superiority of quantum algorithms on hardware.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 53 of its 232 references that are in the map. First posted on arXiv 30 Jun 2024.

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53 of 232 references.

References · 53

  1. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  2. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  3. 2016Categorical Reparameterization with Gumbel-Softmaxcited by 2,708
  4. 2013Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computationcited by 2,031
  5. 2018DARTS: Differentiable Architecture Searchcited by 1,782
  6. 2020Variational Quantum Algorithmscited by 687
  7. 2015Recent Advances in Convolutional Neural Networkscited by 360
  8. 2021Quantum advantage in learning from experimentscited by 186
  9. 2021QuantumNAS: Noise-Adaptive Search for Robust Quantum Circuitscited by 97
  10. 2020Quantum circuit architecture search for variational quantum algorithmscited by 81
  11. 2020Recurrent Quantum Neural Networkscited by 79
  12. 2021Reinforcement learning for optimization of variational quantum circuit architecturescited by 76
38 more
  1. 2018Towards Quantum Machine Learning with Tensor Networkscited by 74
  2. 2021Quantum Architecture Search via Deep Reinforcement Learningcited by 58
  3. 2024Curriculum reinforcement learning for quantum architecture search under hardware errorscited by 37
  4. 2024Quantum Circuit Optimization with AlphaTensorcited by 36
  5. 2024KANQAS: Kolmogorov-Arnold Network for Quantum Architecture Searchcited by 36
  6. 2023Quantum circuit synthesis with diffusion modelscited by 33
  7. 2018Optimizing Quantum Error Correction Codes with Reinforcement Learningcited by 28
  8. 2019Noise-Adaptive Compiler Mappings for Noisy Intermediate-Scale Quantum Computerscited by 22
  9. 2021Enabling Multi-programming Mechanism for Quantum Computing in the NISQ Eracited by 22
  10. 2021Quantum Architecture Search via Continual Reinforcement Learningcited by 22
  11. 2019Graph-theoretic Simplification of Quantum Circuits with the ZX-calculuscited by 20
  12. 2017Quantum autoencoders via quantum adders with genetic algorithmscited by 19
  13. 2022Quantum Neural Architecture Search with Quantum Circuits Metric and Bayesian Optimizationcited by 16
  14. 2022Near-Term Quantum Computing Techniques: Variational Quantum Algorithms, Error Mitigation, Circuit Compilation, Benchmarking and Classical Simulationcited by 13
  15. 2019On the qubit routing problemcited by 11
  16. 2023Enhancing variational quantum state diagonalization using reinforcement learning techniquescited by 11
  17. 2023Quarl: A Learning-Based Quantum Circuit Optimizercited by 10
  18. 2021Qubit Routing using Graph Neural Network aided Monte Carlo Tree Searchcited by 9
  19. 2021Nonlinear transformation of complex amplitudes via quantum singular value transformationcited by 9
  20. 2022Qubit Mapping and Routing via MaxSATcited by 8
  21. 2022Distributed Quantum Computing: a Surveycited by 8
  22. 2019A Verified Optimizer for Quantum Circuitscited by 7
  23. 2021Optimal qubit assignment and routing via integer programmingcited by 7
  24. 2022EAutoDet: Efficient Architecture Search for Object Detectioncited by 7
  25. 2019Optimal Space-Depth Trade-Off of CNOT Circuits in Quantum Logic Synthesiscited by 6
  26. 2020Relaxed Peephole Optimization: A Novel Compiler Optimization for Quantum Circuitscited by 6
  27. 2020A Hardware-Aware Heuristic for the Qubit Mapping Problem in the NISQ Eracited by 5
  28. 2020Diagrammatic Design and Study of Ans"atze for Quantum Machine Learningcited by 5
  29. 2022Synthesizing Quantum-Circuit Optimizerscited by 5
  30. 2019Exact and practical pattern matching for quantum circuit optimizationcited by 4
  31. 2021Quanto: Optimizing Quantum Circuits with Automatic Generation of Circuit Identitiescited by 4
  32. 2016Homomorphic Encryption Experiments on IBM's Cloud Quantum Computing Platformcited by 3
  33. 2019Optimising Clifford Circuits with Quantomaticcited by 3
  34. 2021Optimizing Ansatz Design in QAOA for Max-cutcited by 3
  35. 2022Not All SWAPs Have the Same Cost: A Case for Optimization-Aware Qubit Routingcited by 3
  36. 2023Reducing 2-QuBit Gate Count for ZX-Calculus based Quantum Circuit Optimizationcited by 3
  37. 2024AltGraph: Redesigning Quantum Circuits Using Generative Graph Models for Efficient Optimizationcited by 3
  38. 2015Quantomatic: A Proof Assistant for Diagrammatic Reasoningcited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 4; 2026: 3.

8 papers.