Niloy Deb Roy Mishu, Lei Jiang, Mohsen Heidari, Ruizhe Zhang, Fan Chen
原文摘要
Sample-based quantum diagonalization (SQD) avoids iterative variational circuit optimization, but its accuracy and cost depend on selecting a compact determinant subspace that captures the ground state. Existing machine-learning selectors use task-specific physical cues and typically retrain for each Hamiltonian. We introduce TGen-SQD, a physics-informed generator jointly trained across multiple Hamiltonians. It learns from configuration-interaction-weighted data and uses orbital- and system-level descriptors to guide generation. After training, its weights remain frozen for inference on seen and zero-shot geometries. On a 20-qubit H10 chain, TGen-SQD achieves mean energy errors of 40.18 +/- 1.52 and 43.97 +/- 1.20 mHa on seen and unseen geometries, respectively, 12.7% and 11.7% lower than the state-of-the-art PIGen-SQD baseline under matched shot and proposal budgets.
Quantum reinforcement learning (QRL) has emerged as a promising approach for controlling quantum systems and accelerating learning tasks with quantum resources. However, most existing QRL formulations still retain a classical reinforcement learning interface: states and actions are often encoded in computational bases and rewards are extracted through measurements. This interface is restrictive for genuinely quantum environments: choosing a measurement can discard entanglement information, while repeated intermediate measurements collapse the evolving state and require costly re-preparation. This motivates a fully QRL framework in which the agent, environment, rewards, and return accumulation are all quantum. In this paper, we take a step toward this goal and introduce a new QRL framework in which both the policy and the environment are modeled by conditional quantum channels. We then propose a quantum policy-gradient algorithm for parameterized conditional quantum channels with a trainable control basis. We also derive finite- and infinite-horizon gradient estimators using a new parameter-shift rule. This rule is needed because policy parameters are shared across repeated interaction blocks and the infinite-horizon objective is not a finite circuit; rather than shifting every occurrence and evaluating the whole trajectory, our estimator shifts only one sampled policy block. Sampling this block from a geometric distribution yields an unbiased gradient estimator under bounded rewards. Finally, we establish convergence of the resulting stochastic gradient-ascent procedure to stationary points under standard smoothness and step-size assumptions.