Quantum Circuit Synthesis and Compilation Optimization: Overview and Prospects
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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.
Builds on
53 of 232 references.
References · 53
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38 more
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50 most cited of 53 All 53 (JSON)
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Citing papers per year: 2024: 1; 2025: 4; 2026: 3.
8 papers.
- 2025Artificial intelligence for representing and characterizing quantum systemscited by 9
- 2024From Graphs to Qubits: A Critical Review of Quantum Graph Neural Networkscited by 6
- 2025Quantum computing and artificial intelligence: status and perspectivescited by 5
- 2025Pauli Network Circuit Synthesis with Reinforcement Learningcited by 2
- 2025Quantum Deep Sets and Sequencescited by 1
- 2026SCALAR: A Neurosymbolic Framework for Automated Conjecture and Reasoning in Quantum Circuit Analysiscited by 0
- 2026QAP-Router: Tackling Qubit Routing as Dynamic Quadratic Assignment with Reinforcement Learningcited by 0
- 2026AlphaClifford: Efficient Clifford Synthesis and Transpilation with Model-based RLcited by 0
- 2026AlphaClifford: Efficient Clifford Synthesis and Transpilation with Model-based RL19 Aug 2026
- 2026QAP-Router: Tackling Qubit Routing as Dynamic Quadratic Assignment with Reinforcement Learning12 May 2026
- 2026SCALAR: A Neurosymbolic Framework for Automated Conjecture and Reasoning in Quantum Circuit Analysis11 May 2026
- 2025Artificial intelligence for representing and characterizing quantum systems5 Sep 2025
- 2025Quantum computing and artificial intelligence: status and perspectives29 May 2025
- 2025Quantum Deep Sets and Sequences3 Apr 2025
- 2025Pauli Network Circuit Synthesis with Reinforcement Learning18 Mar 2025
- 2024From Graphs to Qubits: A Critical Review of Quantum Graph Neural Networks12 Aug 2024