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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.00145 |
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| _version_ | 1866917531932950528 |
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| author | DiBrita, Nicholas S. Han, Jason Bhatia, Krishna Cho, Younghyun Luo, Hengrui Patel, Tirthak |
| author_facet | DiBrita, Nicholas S. Han, Jason Bhatia, Krishna Cho, Younghyun Luo, Hengrui Patel, Tirthak |
| contents | We study the problem of probability distribution matching and sampling on near-term quantum computers, aiming to construct parameterized circuits that generate samples from a target distribution while minimizing resource overhead. This task arises naturally in hybrid quantum-classical workflows, where measurement-driven objectives replace full state reconstruction, and is central to applications in generative modeling and variational inference. However, it remains challenging due to hardware noise, limited circuit depth, and a high-dimensional, non-convex parameter space.
We propose CircuitTree, a surrogate-guided optimization framework based on Bayesian Optimization with tree-based models for scalable, domain-aware distribution matching. Our approach introduces a structured, layerwise decomposition aligned with the variational circuit architecture, enabling distributed and sample-efficient optimization within hybrid loops with theoretical convergence guarantees. Across representative distribution-matching tasks, CircuitTree achieves up to 2-3 times lower total variation distance while using 40-60% fewer gates than prior approaches. These results demonstrate its effectiveness as a practical building block for end-to-end hybrid quantum sampling. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_00145 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Domain-Aware Probability Sampling for Hybrid Quantum Systems using Bayesian Optimization DiBrita, Nicholas S. Han, Jason Bhatia, Krishna Cho, Younghyun Luo, Hengrui Patel, Tirthak Quantum Physics We study the problem of probability distribution matching and sampling on near-term quantum computers, aiming to construct parameterized circuits that generate samples from a target distribution while minimizing resource overhead. This task arises naturally in hybrid quantum-classical workflows, where measurement-driven objectives replace full state reconstruction, and is central to applications in generative modeling and variational inference. However, it remains challenging due to hardware noise, limited circuit depth, and a high-dimensional, non-convex parameter space. We propose CircuitTree, a surrogate-guided optimization framework based on Bayesian Optimization with tree-based models for scalable, domain-aware distribution matching. Our approach introduces a structured, layerwise decomposition aligned with the variational circuit architecture, enabling distributed and sample-efficient optimization within hybrid loops with theoretical convergence guarantees. Across representative distribution-matching tasks, CircuitTree achieves up to 2-3 times lower total variation distance while using 40-60% fewer gates than prior approaches. These results demonstrate its effectiveness as a practical building block for end-to-end hybrid quantum sampling. |
| title | Domain-Aware Probability Sampling for Hybrid Quantum Systems using Bayesian Optimization |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2510.00145 |