Quantum Circuit Design using a Progressive Widening Enhanced Monte Carlo Tree Search
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866917461549383680 |
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| author | Lipardi, Vincenzo Dibenedetto, Domenica Stamoulis, Georgios Winands, Mark H. M. |
| author_facet | Lipardi, Vincenzo Dibenedetto, Domenica Stamoulis, Georgios Winands, Mark H. M. |
| contents | The performance of Variational Quantum Algorithms (VQAs) strongly depends on the choice of the parameterized quantum circuit to optimize. One of the biggest challenges in VQAs is designing quantum circuits tailored to the particular problem. This article proposes a gradient-free Monte Carlo Tree Search (MCTS) technique to automate the process of quantum circuit design. Our proposed technique introduces a novel formulation of the action space based on a sampling scheme and a progressive widening technique to explore the space dynamically. When testing our MCTS approach on the domain of random quantum circuits, MCTS approximates unstructured circuits under different values of stabilizer Rényi entropy. It turns out that MCTS manages to approximate the benchmark quantum states independently from their degree of nonstabilizerness. Next, our technique exhibits robustness across various application domains, including quantum chemistry and systems of linear equations. Compared to previous MCTS research, our technique reduces the number of quantum circuit evaluations by a factor of 10 up to 100 while achieving equal or better results. In addition, the resulting quantum circuits exhibit up to three times fewer CNOT gates, which is important for implementation on noisy quantum hardware. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_03962 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Quantum Circuit Design using a Progressive Widening Enhanced Monte Carlo Tree Search Lipardi, Vincenzo Dibenedetto, Domenica Stamoulis, Georgios Winands, Mark H. M. Quantum Physics Artificial Intelligence Emerging Technologies The performance of Variational Quantum Algorithms (VQAs) strongly depends on the choice of the parameterized quantum circuit to optimize. One of the biggest challenges in VQAs is designing quantum circuits tailored to the particular problem. This article proposes a gradient-free Monte Carlo Tree Search (MCTS) technique to automate the process of quantum circuit design. Our proposed technique introduces a novel formulation of the action space based on a sampling scheme and a progressive widening technique to explore the space dynamically. When testing our MCTS approach on the domain of random quantum circuits, MCTS approximates unstructured circuits under different values of stabilizer Rényi entropy. It turns out that MCTS manages to approximate the benchmark quantum states independently from their degree of nonstabilizerness. Next, our technique exhibits robustness across various application domains, including quantum chemistry and systems of linear equations. Compared to previous MCTS research, our technique reduces the number of quantum circuit evaluations by a factor of 10 up to 100 while achieving equal or better results. In addition, the resulting quantum circuits exhibit up to three times fewer CNOT gates, which is important for implementation on noisy quantum hardware. |
| title | Quantum Circuit Design using a Progressive Widening Enhanced Monte Carlo Tree Search |
| topic | Quantum Physics Artificial Intelligence Emerging Technologies |
| url | https://arxiv.org/abs/2502.03962 |