Iterative quantum optimisation with a warm-started quantum state
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912231994687488 |
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| author | Yuan, Haomu Yang, Songqinghao Barnes, Crispin H. W. |
| author_facet | Yuan, Haomu Yang, Songqinghao Barnes, Crispin H. W. |
| contents | We provide a method to prepare a warm-started quantum state from measurements with an iterative framework to enhance the quantum approximate optimisation algorithm (QAOA). The numerical simulations show the method can effectively address the "stuck issue" of the standard QAOA using a single-string warm-started initial state described in [Cain et al., 2023]. When applied to the $3$-regular MaxCut problem, our approach achieves an improved approximation ratio, with a lower bound that iteratively converges toward the best classical algorithms for $p=1$ standard QAOA. Additionally, in the context of the discrete global minimal variance portfolio (DGMVP) model, simulations reveal a more favourable scaling of identifying the global minimal compared to the QAOA standalone, the single-string warm-started QAOA and a classical constrained sampling approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_09704 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Iterative quantum optimisation with a warm-started quantum state Yuan, Haomu Yang, Songqinghao Barnes, Crispin H. W. Quantum Physics Disordered Systems and Neural Networks Machine Learning Optimization and Control Computational Physics We provide a method to prepare a warm-started quantum state from measurements with an iterative framework to enhance the quantum approximate optimisation algorithm (QAOA). The numerical simulations show the method can effectively address the "stuck issue" of the standard QAOA using a single-string warm-started initial state described in [Cain et al., 2023]. When applied to the $3$-regular MaxCut problem, our approach achieves an improved approximation ratio, with a lower bound that iteratively converges toward the best classical algorithms for $p=1$ standard QAOA. Additionally, in the context of the discrete global minimal variance portfolio (DGMVP) model, simulations reveal a more favourable scaling of identifying the global minimal compared to the QAOA standalone, the single-string warm-started QAOA and a classical constrained sampling approach. |
| title | Iterative quantum optimisation with a warm-started quantum state |
| topic | Quantum Physics Disordered Systems and Neural Networks Machine Learning Optimization and Control Computational Physics |
| url | https://arxiv.org/abs/2502.09704 |