English(EN)Enhanced Reinforcement Learning-based Process Synthesis via Quantum Computing
新研究探索人工智能和量子计算在生成模型和控制中的应用
作者PulseAugur 编辑部·[9 个来源]·
研究人员正在探索先进的机器学习技术来增强量子计算能力。一篇论文介绍了潜在条件参数化量子电路（LPQCs）作为量子态分布的通用逼近器，有望改进量子生成模型。另一项研究提出了一个用于鲁棒开放量子系统控制的多任务强化学习框架，在噪声环境中展示了高保真度。此外，一种名为CRiSP的新方法使用强化学习来优化变分量子算法的初始状态，优于现有方法。最后，量子强化学习被应用于过程合成问题，与经典方法相比显示出具有竞争力的可扩展性和效率。
AI
arXiv:2605.28690v1 Announce Type: cross Abstract: Many applications in quantum simulation, quantum chemistry, and quantum machine learning require not a single quantum state but an ensemble of states characterizing the heterogeneity of a target system. Preparing such ensembles st…
Many applications in quantum simulation, quantum chemistry, and quantum machine learning require not a single quantum state but an ensemble of states characterizing the heterogeneity of a target system. Preparing such ensembles state-by-state is prohibitive in both variational an…
arXiv cs.LG
TIER_1English(EN)·Haftu W. Fentaw, Steve Campbell, Simon Caton·
arXiv:2605.26925v1 Announce Type: cross Abstract: We present a Multi-task Soft Actor-Critic (SAC) Reinforcement Learning framework designed for open-system quantum control across diverse Hamiltonians, which learns optimal pulse sequences while simultaneously discovering problem-s…
We present a Multi-task Soft Actor-Critic (SAC) Reinforcement Learning framework designed for open-system quantum control across diverse Hamiltonians, which learns optimal pulse sequences while simultaneously discovering problem-specific evolution time T and number of control pul…
arXiv:2605.23138v1 Announce Type: cross Abstract: Variational Quantum Algorithms (VQAs) potentially offer a pathway to practical quantum advantage, but their optimization is heavily hindered by barren plateaus and numerous local minima. While classically simulable Clifford circui…
arXiv cs.AI
TIER_1English(EN)·Austin Braniff (Department of Chemical and Biomedical Engineering, West Virginia University), Fengqi You (R.F. Smith School of Chemical and Biomolecular Engineering, Cornell University), Yuhe Tian (Department of Chemical and Biomedical Engineering, West …·
arXiv:2605.21213v1 Announce Type: cross Abstract: In this work, we present quantum reinforcement learning (RL) as a solution strategy for process synthesis problems. Building on our prior work, we develop a generalized framework that formally poses process synthesis as a Markov d…
In this work, we present quantum reinforcement learning (RL) as a solution strategy for process synthesis problems. Building on our prior work, we develop a generalized framework that formally poses process synthesis as a Markov decision process and introduces quantum-enhanced RL…
In this work, we present quantum reinforcement learning (RL) as a solution strategy for process synthesis problems. Building on our prior work, we develop a generalized framework that formally poses process synthesis as a Markov decision process and introduces quantum-enhanced RL…
arXiv stat.ML
TIER_1English(EN)·Cristina Butucea, Jan Johannes, Henning Stein·
arXiv:2505.24587v3 Announce Type: replace-cross Abstract: Gentle measurements of quantum states do not entirely collapse the initial state. Instead, they provide a post-measurement state at a prescribed trace distance $\alpha$ from the initial state together with a random variabl…
