Compiling quantum circuits to realistic hardware architectures using temporal planners
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Abstract (arXiv)
To run quantum algorithms on emerging gate-model quantum hardware, quantum circuits must be compiled to take into account constraints on the hardware. For near-term hardware, with only limited means to mitigate decoherence, it is critical to minimize the duration of the circuit. We investigate the application of temporal planners to the problem of compiling quantum circuits to newly emerging quantum hardware. While our approach is general, we focus on compiling to superconducting hardware architectures with nearest neighbor constraints. Our initial experiments focus on compiling Quantum Alternating Operator Ansatz (QAOA) circuits whose high number of commuting gates allow great flexibility in the order in which the gates can be applied. That freedom makes it more challenging to find optimal compilations but also means there is a greater potential win from more optimized compilation than for less flexible circuits. We map this quantum circuit compilation problem to a temporal planning problem, and generated a test suite of compilation problems for QAOA circuits of various sizes to a realistic hardware architecture. We report compilation results from several state-of-the-art temporal planners on this test set. This early empirical evaluation demonstrates that temporal planning is a viable approach to quantum circuit compilation.
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 24 May 2017.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 1; 2024: 1.
5 papers.
- 2021Generalization in quantum machine learning from few training datacited by 216
- 2021Theory of overparametrization in quantum neural networkscited by 100
- 2022Efficient and practical quantum compiler towards multi-qubit systems with deep reinforcement learningcited by 6
- 2024AI-Powered Algorithm-Centric Quantum Processor Topology Designcited by 1
- 2023Compilation of product-formula Hamiltonian simulation via reinforcement learningcited by 0
- 2024AI-Powered Algorithm-Centric Quantum Processor Topology Design18 Dec 2024
- 2023Compilation of product-formula Hamiltonian simulation via reinforcement learning7 Nov 2023
- 2022Efficient and practical quantum compiler towards multi-qubit systems with deep reinforcement learning14 Apr 2022
- 2021Generalization in quantum machine learning from few training data9 Nov 2021
- 2021Theory of overparametrization in quantum neural networks23 Sep 2021