A Noise-Aware Scalable Subspace Classical Optimizer for the Quantum Approximate Optimization Algorithm
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866914176731971584 |
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| author | Dzahini, Kwassi Joseph Larson, Jeffrey M. Menickelly, Matt Wild, Stefan M. |
| author_facet | Dzahini, Kwassi Joseph Larson, Jeffrey M. Menickelly, Matt Wild, Stefan M. |
| contents | We introduce ANASTAARS, a noise-aware scalable classical optimizer for variational quantum algorithms such as the quantum approximate optimization algorithm (QAOA). ANASTAARS leverages adaptive random subspace strategies to efficiently optimize the ansatz parameters of a QAOA circuit, in an effort to address challenges posed by a potentially large number of QAOA layers. ANASTAARS iteratively constructs random interpolation models within low-dimensional affine subspaces defined via Johnson--Lindenstrauss transforms. This adaptive strategy allows the selective reuse of previously acquired measurements, significantly reducing computational costs associated with shot acquisition. Furthermore, to robustly handle noisy measurements, ANASTAARS incorporates noise-aware optimization techniques by estimating noise magnitude and adjusts trust-region steps accordingly. Numerical experiments demonstrate the practical scalability of the proposed method for near-term quantum computing applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_10992 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Noise-Aware Scalable Subspace Classical Optimizer for the Quantum Approximate Optimization Algorithm Dzahini, Kwassi Joseph Larson, Jeffrey M. Menickelly, Matt Wild, Stefan M. Quantum Physics Optimization and Control We introduce ANASTAARS, a noise-aware scalable classical optimizer for variational quantum algorithms such as the quantum approximate optimization algorithm (QAOA). ANASTAARS leverages adaptive random subspace strategies to efficiently optimize the ansatz parameters of a QAOA circuit, in an effort to address challenges posed by a potentially large number of QAOA layers. ANASTAARS iteratively constructs random interpolation models within low-dimensional affine subspaces defined via Johnson--Lindenstrauss transforms. This adaptive strategy allows the selective reuse of previously acquired measurements, significantly reducing computational costs associated with shot acquisition. Furthermore, to robustly handle noisy measurements, ANASTAARS incorporates noise-aware optimization techniques by estimating noise magnitude and adjusts trust-region steps accordingly. Numerical experiments demonstrate the practical scalability of the proposed method for near-term quantum computing applications. |
| title | A Noise-Aware Scalable Subspace Classical Optimizer for the Quantum Approximate Optimization Algorithm |
| topic | Quantum Physics Optimization and Control |
| url | https://arxiv.org/abs/2507.10992 |