Toward General Quantum Control with Physics-Informed Large Language Models

• URL: http://arxiv.org/abs/2605.26021v1
• Date: Mon, 25 May 2026 16:41:41 GMT
• Title: Toward General Quantum Control with Physics-Informed Large Language Models
• Abstract summary: We introduce VF-QCTRL, a physics-informed large language model framework for general quantum control.<n>Our results establish VF-QCTRL as a promising paradigm for accurate, efficient, interpretable, and training-free quantum control protocol design.
• Score: 14.759081005076082
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantum control is essential for quantum information science and technology, yet designing high-fidelity control protocols remains challenging due to complex optimization landscapes, hardware noise, and long pulse sequences. Existing numerical solvers often require problem-specific engineering and produce opaque control amplitudes, while naive large language models (LLMs) lack the physical consistency and long-horizon precision for reliable quantum control synthesis. Here we introduce VF-QCTRL, a physics-informed large language model framework for general quantum control that combines symbolic reasoning with optimization to propose analytic control ansätze and coherently refine their parameters through feedback. To systematically evaluate LLM-driven quantum control, we develop QCTRL-BENCH, a benchmark spanning sixteen tasks across single- and multi-qubit systems, closed and open quantum dynamics, noiseless and noisy settings, and both analytic and numerical protocols. Across the benchmark, VF-QCTRL demonstrates strong universality, accuracy, efficiency, and interpretability: it applies to generic quantum control systems without task-specific training, achieves performance competitive with or exceeding state-of-the-art conventional solvers in both noiseless and noisy regimes with query efficiency, exhibits favorable inference-time scaling and pulse resolution scaling, and derives physically interpretable analytical protocols directly from prompts. Our results establish physics-informed LLM-based quantum control as a promising paradigm for accurate, efficient, interpretable, and training-free quantum control protocol design across a broad range of quantum systems.

Related papers

• Analytic Approach to Quantum Control Using Quantum Signal Processing [4.52716230518765]
  Quantum algorithms offer rigorous error guarantees for implementing unitary and non-unitary transformations.<n>QSP-Control is an analytical framework for quantum control of qubit-oscillator dynamics.
  arXiv  Detail & Related papers  (2026-06-24T17:56:12Z)
• Software Between Quantum and Machine Learning -- And Down to Pulses [0.9569512179866463]
  We present a software framework that extends quantum machine learning (QML) methodologies to encompass pulse-level modelling.<n>The framework provides a comprehensive suite of modelling and analytical capabilities.<n>All performance-critical components are implemented in a high-performance environment using JAX and supported by a dedicated quantum simulator.
  arXiv  Detail & Related papers  (2026-05-20T15:20:07Z)
• Continual Quantum Architecture Search with Tensor-Train Encoding: Theory and Applications to Signal Processing [68.35481158940401]
  CL-QAS is a continual quantum architecture search framework.<n>It mitigates challenges of costly encoding amplitude and forgetting in variational quantum circuits.<n>It achieves controllable robustness expressivity, sample-efficient generalization, and smooth convergence without barren plateaus.
  arXiv  Detail & Related papers  (2026-01-10T02:36:03Z)
• The Quantum Control Hierarchy: When Physics-Informed Design Meets Machine Learning [0.6053648545114841]
  We address a wide spectrum of quantum control strategies, including various open-loop protocols and advanced adaptive methods.<n>For entanglement preservation, a physics-informed hybrid Quantum Error Correction and Dynamical Decoupling protocol provides the most stable and effective solution.<n>For dynamic tasks requiring the discovery of non-trivial control sequences, such as DD, Floquet engineering, and rapid entanglement generation or coherent transport, the model-free Reinforcement Learning agents consistently find superior solutions.
  arXiv  Detail & Related papers  (2025-09-16T08:56:11Z)
• Reinforcement Learning for Quantum Network Control with Application-Driven Objectives [53.03367590211247]
  Dynamic programming and reinforcement learning offer promising tools for optimizing control strategies.<n>We propose a novel RL framework that directly optimize non-linear, differentiable objective functions.<n>Our work comprises the first step towards non-linear objective function optimization in quantum networks with RL, opening a path towards more advanced use cases.
  arXiv  Detail & Related papers  (2025-09-12T18:41:10Z)
• VQC-MLPNet: An Unconventional Hybrid Quantum-Classical Architecture for Scalable and Robust Quantum Machine Learning [50.95799256262098]
  Variational quantum circuits (VQCs) hold promise for quantum machine learning but face challenges in expressivity, trainability, and noise resilience.<n>We propose VQC-MLPNet, a hybrid architecture where a VQC generates the first-layer weights of a classical multilayer perceptron during training, while inference is performed entirely classically.
  arXiv  Detail & Related papers  (2025-06-12T01:38:15Z)
• QCircuitBench: A Large-Scale Dataset for Benchmarking Quantum Algorithm Design [63.02824918725805]
  Quantum computing is recognized for the significant speedup it offers over classical computing through quantum algorithms.<n>QCircuitBench is the first benchmark dataset designed to evaluate AI's capability in designing and implementing quantum algorithms.
  arXiv  Detail & Related papers  (2024-10-10T14:24:30Z)
• Quantum control by the environment: Turing uncomputability, Optimization over Stiefel manifolds, Reachable sets, and Incoherent GRAPE [56.47577824219207]
  In many practical situations, the controlled quantum systems are open, interacting with the environment. In this note, we briefly review some results on control of open quantum systems using environment as a resource.
  arXiv  Detail & Related papers  (2024-03-20T10:09:13Z)
• Self-Correcting Quantum Many-Body Control using Reinforcement Learning with Tensor Networks [0.0]
  We present a novel framework for efficiently controlling quantum many-body systems based on reinforcement learning (RL) We show that RL agents are capable of finding universal controls, of learning how to optimally steer previously unseen many-body states, and of adapting control protocols on-thefly when the quantum dynamics is subject to perturbations.
  arXiv  Detail & Related papers  (2022-01-27T20:14:09Z)
• Model predictive control for robust quantum state preparation [4.069849286089743]
  We introduce model predictive control (MPC) for quantum control applications. MPC inherits a natural degree of disturbance rejection by incorporating measurement feedback. We show how MPC can be used to generate practical optimized control sequences.
  arXiv  Detail & Related papers  (2022-01-14T00:55:41Z)
• Quantum circuit architecture search for variational quantum algorithms [88.71725630554758]
  We propose a resource and runtime efficient scheme termed quantum architecture search (QAS) QAS automatically seeks a near-optimal ansatz to balance benefits and side-effects brought by adding more noisy quantum gates. We implement QAS on both the numerical simulator and real quantum hardware, via the IBM cloud, to accomplish data classification and quantum chemistry tasks.
  arXiv  Detail & Related papers  (2020-10-20T12:06:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.