TL;DR
This work asks whether reinforcement learning (RL) can design circuits that reach a fixed accuracy target with fewer compiled CNOTs than strong hand-designed and greedy baselines than strong hand-designed and greedy baselines.
Numerical results show that the learned architectures recover known benchmark strategies, adapt to dephasing noise, and outperform fixed hardware-efficient ans\"atze while using fewer entangling gates, establishing AutoQSense as a resource-aware approach to adaptive and hardware-compatible quantum sensing.
This work examines when structured long-range connectivity provides a useful resource, focusing on sparse power-of-two (PWR2) coupling graphs, and identifies circuit geometry and qubit reconfigurability as task-dependent resources for variational algorithms.
AlphaClifford is introduced, a model-based Reinforcement Learning framework designed to efficiently synthesize Clifford circuits from the fundamental gate set composed of H, S, and CNOT, demonstrating the broad applicability of the framework on two additional tasks: hardware-constrained Clifford transpilation, where it...
In this work, a quantum architecture search framework for approximate quantum state preparation (QSP) is proposed, able to achieve approximation errors of $10^{-14}$.
RubriQ is introduced, a scalable framework that formulates circuit synthesis as a large language model (LLM) code-generation task, optimized via group relative policy optimization (GRPO), which establishes an automated, high-performance computing (HPC)-driven pipeline for generating hardware-ready, fault-tolerant quant...
A reinforcement learning framework that embeds a deterministic Commutation-and-Reduction (CR) algorithm directly into the training environment, enabling the agent to focus its learning capacity on the non-trivial optimizations where reinforcement learning adds real value.
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