New research explores AI and quantum computing for generative models and control
ByPulseAugur Editorial·[9 sources]·
Researchers are exploring advanced machine learning techniques to enhance quantum computing capabilities. One paper introduces latent-conditioned parameterized quantum circuits (LPQCs) as a universal approximator for quantum state distributions, potentially improving quantum generative modeling. Another study presents a multi-task reinforcement learning framework for robust open quantum system control, demonstrating high fidelities in noisy environments. Additionally, a new approach called CRiSP uses reinforcement learning to optimize initial states for variational quantum algorithms, outperforming existing methods. Finally, quantum reinforcement learning is being applied to process synthesis problems, showing competitive scalability and efficiency compared to classical methods.
AI
IMPACT
These advancements could lead to more powerful quantum computers and novel applications in fields like chemistry and materials science.
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Multiple arXiv papers detailing novel research in quantum computing and 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…
