arXiv Machine Learning

Approximate Quantum State Preparation Through Proximal Policy Optimization

arXiv:2607. 21121v1 Announce Type: cross Abstract: In this work, a quantum architecture search framework for approximate quantum state preparation (QSP) is proposed.

arXiv Machine Learning
Jun 24

Reinforcement Learning to Disentangle Multiqubit Quantum States from Partial Observations

arXiv:2406. 07884v3 Announce Type: replace-cross Abstract: Using partial knowledge of a quantum state to control multiqubit entanglement is a largely unexplored paradigm in the emerging field of quantum interactive dynamics with the potential to address outstanding challenges in quantum state preparation and compression, quantum control, and quantum complexity.

By Pavel Tashev, Stefan Petrov, Matthew T. Diaz, Friederike Metz, Alaina M. Green, Norbert M. Linke, Marin Bukov
arXiv Machine Learning
Jul 24

Neural Guided Sampling for Quantum Circuit Optimization

arXiv:2510. 12430v2 Announce Type: replace-cross Abstract: Translating a general quantum circuit on a specific hardware topology with a reduced set of available gates, also known as transpilation, comes with a substantial increase in the length of the equivalent circuit.

By Bodo Rosenhahn, Tobias J. Osborne, Christoph Hirche
Hugging Face Trending Papers
Jul 2

One More Time: Revisiting Neural Quantum States from a Reinforcement Learning Perspective

Neural quantum states (NQS) provide a flexible and scalable framework for approximating quantum many-body wavefunctions. Among NQS parameterizations, autoregressive models are especially attractive because they enable exact, independent sampling from the Born distribution, avoiding the autocorrelation and mixing issues of Markov chain methods.

arXiv AI
Aug 3

DreamQAS: Learning a Decision-Useful World Model for VQE-Efficient Quantum Architecture Search

arXiv:2607. 29491v1 Announce Type: cross Abstract: Reinforcement-learning-based quantum architecture search (RL-QAS) repeatedly optimizes a variational quantum eigensolver (VQE) after extending a circuit, although circuit construction and action legality are deterministic and known.

By Jiayang Niu, Yan Wang, Jie Li, Ke Deng, Azadeh Alavi, Muhammad Usman, Yongli Ren
arXiv Machine Learning
Jul 13

Action-Factored Multi-Agent Reinforcement Learning for Scalable Quantum Device Tuning

arXiv:2607. 09422v1 Announce Type: new Abstract: Cooperative multi-agent reinforcement learning is well suited to problems with large parameter spaces and exploitable local structure, such as the tuning of electrostatically-defined quantum-dot arrays.

By Edwin De Nicolo, Rahul Marchand, Cornelius Carlsson, Pranav Vaidhyanathan, Natalia Ares
arXiv Machine Learning
Jun 9

Zero-shot Quantum Neural Architecture Search

arXiv:2605. 27410v2 Announce Type: replace-cross Abstract: Variational Quantum Algorithms (VQAs) are a leading approach to exploiting near-term quantum hardware, leveraging parameterized quantum circuits and classical optimization to achieve advantage.

By Tung Dao, Son N. Tran, Huynh Thi Thanh Binh