Scalable Quantum Reinforcement Learning on NISQ Devices with Dynamic-Circuit Qubit Reuse and Grover Optimization
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866917426866683904 |
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| author | Su, Thet Htar Shresthamali, Shaswot Kondo, Masaaki |
| author_facet | Su, Thet Htar Shresthamali, Shaswot Kondo, Masaaki |
| contents | A scalable and resource-efficient quantum reinforcement learning framework is presented that eliminates the linear qubit-scaling barrier in multi-step quantum Markov decision processes (QMDPs). The proposed framework integrates a QMDP formulation, dynamic-circuit execution, and Grover-based amplitude amplification into a unified quantum-native architecture. Environment dynamics are encoded entirely within quantum Hilbert space, enabling coherent superposition over state-action sequences and a direct quantum agent-environment interface without intermediate quantum-to-classical conversion. The central contribution is a dynamic execution model for multi-step QMDPs that employs mid-circuit measurement and reset to recycle a fixed physical quantum register across sequential interactions. This approach preserves trajectory fidelity relative to a static unrolled QMDP, generating identical state-action sequences while reducing the physical qubit requirement from 7xT to a constant 7, independent of the interaction horizon T. Thus, the qubit complexity of multi-step QMDPs is transformed from O(T) to O(1) while maintaining functional equivalence at the level of trajectory generation. Trajectory returns are evaluated via quantum arithmetic, and high-return trajectories are marked and amplified using amplitude amplification to increase their sampling probability. Simulations confirm preservation of trajectory fidelity with a 66% qubit reduction compared to a static design. Experimental execution on an IBM Heron-class processor demonstrates feasibility on noisy intermediate-scale quantum hardware, establishing a scalable and resource-efficient foundation for large-scale quantum-native reinforcement learning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_16002 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Scalable Quantum Reinforcement Learning on NISQ Devices with Dynamic-Circuit Qubit Reuse and Grover Optimization Su, Thet Htar Shresthamali, Shaswot Kondo, Masaaki Quantum Physics Machine Learning A scalable and resource-efficient quantum reinforcement learning framework is presented that eliminates the linear qubit-scaling barrier in multi-step quantum Markov decision processes (QMDPs). The proposed framework integrates a QMDP formulation, dynamic-circuit execution, and Grover-based amplitude amplification into a unified quantum-native architecture. Environment dynamics are encoded entirely within quantum Hilbert space, enabling coherent superposition over state-action sequences and a direct quantum agent-environment interface without intermediate quantum-to-classical conversion. The central contribution is a dynamic execution model for multi-step QMDPs that employs mid-circuit measurement and reset to recycle a fixed physical quantum register across sequential interactions. This approach preserves trajectory fidelity relative to a static unrolled QMDP, generating identical state-action sequences while reducing the physical qubit requirement from 7xT to a constant 7, independent of the interaction horizon T. Thus, the qubit complexity of multi-step QMDPs is transformed from O(T) to O(1) while maintaining functional equivalence at the level of trajectory generation. Trajectory returns are evaluated via quantum arithmetic, and high-return trajectories are marked and amplified using amplitude amplification to increase their sampling probability. Simulations confirm preservation of trajectory fidelity with a 66% qubit reduction compared to a static design. Experimental execution on an IBM Heron-class processor demonstrates feasibility on noisy intermediate-scale quantum hardware, establishing a scalable and resource-efficient foundation for large-scale quantum-native reinforcement learning. |
| title | Scalable Quantum Reinforcement Learning on NISQ Devices with Dynamic-Circuit Qubit Reuse and Grover Optimization |
| topic | Quantum Physics Machine Learning |
| url | https://arxiv.org/abs/2509.16002 |