# Feasibility-Aware Security-Constrained Unit Commitment via Hybrid Soft Actor-Critic with Quantum-Sampled Features

arXiv: https://arxiv.org/abs/2607.17031 · HTML: https://mlsanity.com/paper/2607.17031/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Quantum reinforcement learning, one of 15 directions in Deep reinforcement learning methods.
- Builds on 13 of its 35 references that are in the map.
- First posted on arXiv 19 Jul 2026.

## Placed in

- Deep reinforcement learning methods / [Quantum reinforcement learning](https://mlsanity.com/deeprl/quantum-reinforcement-learning/)

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Supervised learning with quantum enhanced feature spaces](https://mlsanity.com/paper/1804.11326/) (2018)
- [Learning to Solve Large-Scale Security-Constrained Unit Commitment Problems](https://mlsanity.com/paper/1902.01697/) (2019)
- [JuMP 1.0: Recent improvements to a modeling language for mathematical optimization](https://mlsanity.com/paper/2206.03866/) (2022)
- [Is learning for the unit commitment problem a low-hanging fruit?](https://mlsanity.com/paper/2106.11687/) (2021)
- [An Optimization Method-Assisted Ensemble Deep Reinforcement Learning Algorithm to Solve Unit Commitment Problems](https://mlsanity.com/paper/2206.04249/) (2022)
- [Qubit-Efficient Quantum Annealing for Stochastic Unit Commitment](https://mlsanity.com/paper/2502.15917/) (2025)
- [Leveraging Quantum Computing for Accelerated Classical Algorithms in Power Systems Optimization](https://mlsanity.com/paper/2503.19112/) (2025)
- [A New Hybrid Quantum-Classical Algorithm for Solving the Unit Commitment Problem](https://mlsanity.com/paper/2505.00145/) (2025)
- [D2-UC: A Distributed-Distributed Quantum-Classical Framework for Unit Commitment](https://mlsanity.com/paper/2511.03104/) (2025)
- [A Survey on Applications of Quantum Computing for Unit Commitment](https://mlsanity.com/paper/2601.01777/) (2026)
- [Structure-Aware Commitment Reduction for Network-Constrained Unit Commitment with Solver-Preserving Guarantees](https://mlsanity.com/paper/2604.02788/) (2026)

## Abstract (arXiv)

Security-constrained unit commitment (SCUC) couples binary commitment, economic dispatch, reserves, and network security over a multiperiod horizon, making an exact solution computationally expensive for realistic system sizes. This paper proposes a three-layer hybrid framework in which a Bernoulli hybrid soft actor-critic (HSAC) policy proposes hourly commitments, a quantum-sampled auxiliary channel augments the state, and a native SCUC mixed-integer linear program recovers dispatch and security variables after only a limited subset of commitment binaries is enforced. The method is therefore solver-compatible rather than an end-to-end replacement for exact optimization. We formalize the SCUC-to-reinforcement-learning interface, derive the temporal coverage induced by the fixed cap, and evaluate the 14- 57- and 118-bus benchmark cases. The results show stable, low-cost recovery in the 14-bus case, where the best recovered schedule attains the full-horizon optimum; a very low screen-rejection rate in the 57-bus case; and a clear coverage bottleneck in the 118-bus case once the enforcement cap no longer spans a complete commitment period. The study, therefore, identifies the amount of useful commitment information that reaches the recovery model, under an exploratory Bernoulli actor and a small enforcement cap, as the dominant limitation that governs scalability