Quantum-assisted finite-element design optimization

用于区分振幅阻尼动力学的纠缠辅助定时优化

Entanglement-Assisted Timing Optimization for Discriminating Amplitude-Damping Dynamics

分析单用途场景下由相位协变振幅阻尼林德布拉德算符生成的两个量子比特动力学过程的最小错误辨别，优化输入探针及询问时间，给出无辅助和纠缠辅助探针的相关结果。

QUAV：面向无人机导航避障的量子辅助路径规划与优化

QUAV: Quantum-Assisted Path Planning and Optimization for UAV Navigation with Obstacle Avoidance

本文提出基于QAOA的量子辅助无人机路径规划框架QUAV，将避障与UTM坐标约束纳入量子优化，并通过仿真和ibm_kyiv真机验证其可行性、鲁棒性及电路深度线性扩展优势。

Shape optimizations for body-assisted light-matter interactions

We implement a shape optimization algorithm for body-assisted light-matter interactions described by the formalism of macroscopic quantum electrodynamics. The approach uses the level-set method to represent and incrementally evolve dielectric environments. Utilizing finite-difference time-domain techniques we demonstrate the ability of the algorithm by optimizing the rate of resonance energy transfer in two dimensions. The resulting geometries enhance the transfer rate by several orders of magnitude.

Feedback-assisted ponderomotive squeezing

We analyze how the radiation pressure interaction between a mechanical element and an intensely driven optical cavity mode can be exploited for generating squeezed light. We study in particular how the performance of the optomechanical device can be improved when a homodyne-based feedback loop is added to control the motion of the mechanical element of the system. We show that, when driving the cavity at resonance, an appropriate proportional feedback control is able to improve the generation of ponderomotive squeezing, which should be detectable with state-of-the-art apparatuses.

Reinforcement Learning assisted Quantum Optimization

We propose a reinforcement learning (RL) scheme for feedback quantum control within the quan-tum approximate optimization algorithm (QAOA). QAOA requires a variational minimization for states constructed by applying a sequence of unitary operators, depending on parameters living ina highly dimensional space. We reformulate such a minimum search as a learning task, where a RL agent chooses the control parameters for the unitaries, given partial information on the system. We show that our RL scheme finds a policy converging to the optimal adiabatic solution for QAOA found by Mbeng et al. arXiv:1…

量子信息辅助的完全活性空间优化（QICAS）

Quantum Information-Assisted Complete Active Space Optimization (QICAS)

该研究提出量子信息辅助的完全活性空间优化（QICAS）方案，利用量子信息度量选择活性空间并优化轨道，使CASCI能量达到化学精度，并为铬二聚体等复杂体系提供良好起点，验证了低纠缠非活性空间能量最优的猜想。