Relaxed Peephole Optimization: A Novel Compiler Optimization for Quantum Circuits
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Abstract (arXiv)
In this paper, we propose a novel quantum compiler optimization, named relaxed peephole optimization (RPO) for quantum computers. RPO leverages the single-qubit state information that can be determined statically by the compiler. We define that a qubit is in a basis state when, at a given point in time, its state is either in the X-, Y-, or Z-basis. When basis qubits are used as inputs to quantum gates, there exist opportunities for strength reduction, which replaces quantum operations with equivalent but less expensive ones. Compared to the existing peephole optimization for quantum programs, the difference is that our proposed optimization does not require an identical unitary matrix, thereby named `relaxed' peephole optimization. We also extend our approach to optimize the quantum gates when some input qubits are in known pure states. Both optimizations, namely the Quantum Basis-state Optimization (QBO) and the Quantum Pure-state Optimization (QPO), are implemented in the IBM's Qiskit transpiler. Our experimental results show that our proposed optimization pass is fast and effective. The circuits optimized with our compiler optimizations obtain up to 18.0% (11.7% on average) fewer CNOT gates and up to 8.2% (7.1% on average) lower transpilation time than that of the most aggressive optimization level in the Qiskit compiler. When running on real quantum computers, the success rates of 3-qubit quantum phase estimation algorithm improve by 2.30X due to the reduced gate counts.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Dec 2020.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 2; 2025: 2.
6 papers.
- 2024'Eliv'agar: Efficient Quantum Circuit Search for Classificationcited by 12
- 2024Quantum Circuit Synthesis and Compilation Optimization: Overview and Prospectscited by 8
- 2021Representation of binary classification trees with binary features by quantum circuitscited by 7
- 2022AutoQC: Automated Synthesis of Quantum Circuits Using Neural Networkcited by 2
- 2025Pauli Network Circuit Synthesis with Reinforcement Learningcited by 2
- 2025Three Birds with One Stone: Improving Performance, Convergence, and System Throughput with Nestcited by 0
- 2025Three Birds with One Stone: Improving Performance, Convergence, and System Throughput with Nest10 Oct 2025
- 2025Pauli Network Circuit Synthesis with Reinforcement Learning18 Mar 2025
- 2024Quantum Circuit Synthesis and Compilation Optimization: Overview and Prospects30 Jun 2024
- 2024'Eliv'agar: Efficient Quantum Circuit Search for Classification17 Jan 2024
- 2022AutoQC: Automated Synthesis of Quantum Circuits Using Neural Network6 Oct 2022
- 2021Representation of binary classification trees with binary features by quantum circuits30 Aug 2021