Uncovering Latent Structures in Robust Pulse Sequences: A Model-Based Reinforcement Learning Approach for Adaptable Quantum Control

• URL: http://arxiv.org/abs/2606.24507v1
• Date: Tue, 23 Jun 2026 12:37:04 GMT
• Title: Uncovering Latent Structures in Robust Pulse Sequences: A Model-Based Reinforcement Learning Approach for Adaptable Quantum Control
• Abstract summary: Real-time adaptive control of quantum systems requires rapid generation of robust, high-fidelity pulses.<n>We present an approach to robust optimal quantum control based on model-based reinforcement learning.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Real-time adaptive control of quantum systems requires rapid generation of robust, high-fidelity pulses across a continuous range of operating conditions. Standard optimization algorithms such as gradient-ascent pulse engineering (GRAPE) solve each instance independently, discarding information between runs and requiring costly reinitialization when parameters change. We present an approach to robust optimal quantum control based on model-based reinforcement learning, in which a single neural network -- embedding the Hamiltonian directly into the training pipeline -- generates robust gates across an entire family of gate configurations, without pre-computed training data. Demonstrated on a single-spin (two-level) system, the trained networks produce pulses for arbitrary rotation angles over a range of pulse durations, detunings, and field inhomogeneities in milliseconds, at fidelities comparable to multi-seed GRAPE. The framework is inherently adaptable: any parameter entering the Hamiltonian can serve as a network input, extending the approach to different systems and control settings. Beyond speed, the network reveals structure in the control landscape: it discovers the same structured phase profiles that appear in GRAPE solutions -- made identifiable through fidelity-invariant symmetry transformations -- but more consistently than independent optimization. This consistency enables smooth interpolation across the entire trained parameter space.

Related papers

• Forked Physics-Informed Neural Networks for Non-Markovian Open Quantum Dynamics and Control [1.3975180218814856]
  Physics-informed neural networks (PINNs) provide a pathway to reunify the simulation and control of quantum systems.<n>We extend our previously proposed forked PINN (FPINN) by incorporating a dedicated control branch.<n>Our framework thus provides a unified, end-to-end differentiable paradigm for simulation and control of open quantum systems.
  arXiv  Detail & Related papers  (2026-07-14T09:38:03Z)
• From Open Loop to Closed Loop: A Test-Time Iterative Optimization Framework for Reference-Consistent Image Generation [108.02425846269234]
  controllable image generation has made significant strides by incorporating visual reference conditions.<n>Existing methods predominantly operate as open-loop systems.<n>We propose a novel test-time iterative optimization framework that reformulates reference-consistent generation as a closed-loop dynamic tracking problem.
  arXiv  Detail & Related papers  (2026-07-06T05:36:30Z)
• End-to-End Learning of Quantum Control on Latent Dynamical Manifold [13.119254069186034]
  Traditional quantum control relies on an iterative "simulate-then-optimize" paradigm.<n>We propose an end-to-end quantum control framework based on long short-term memory.
  arXiv  Detail & Related papers  (2026-06-26T09:57:12Z)
• Intelligent Optimal Control of Rydberg Gates with Incremental-Update Deep Reinforcement Learning [10.771205594997445]
  We show a DRL-based framework for realizing Rydberg controlled-NOT gates with high speed and high fidelity.<n>By introducing an incremental-update learning policy, our framework effectively regularizes the exploration of the control landscape.<n>Our protocol achieves a peak average fidelity of 0.9991, significantly outperforming conventional methods and surpassing the critical fault-tolerant threshold.
  arXiv  Detail & Related papers  (2026-05-06T08:15:59Z)
• Programming long-range interactions in analog quantum simulators [39.364243535886985]
  Long-range interactions are the source of many equilibrium and out-of-equilibrium quantum many-body phenomena.<n>We develop a hybrid classical-quantum toolbox that exploits this tunability to enhance many-body state preparation in analog simulators.<n>Our results establish programmable long-range interactions as a powerful resource for next-generation analog quantum simulators.
  arXiv  Detail & Related papers  (2026-04-24T12:06:38Z)
• Fidelity-informed neural pulse compilation of a continuous family of quantum gates with uncertainty-margin analysis [0.0]
  We develop a neural pulse-compilation framework for a family of single-qubit gates on a three-qubit liquid-state nuclear magnetic resonance (NMR) processor.<n>Instead of decomposing each target unitary into a sequence of calibrated basis gates, the method learns a direct map from the axis-angle parameters of an arbitrary U in SU(2) operation.<n>We show numerically that a single model generalizes across a continuous range of gate parameters and experimentally validate representative compiled pulses on a benchtop three-qubit NMR device.
  arXiv  Detail & Related papers  (2026-04-13T11:17:18Z)
• Optimal Control Meets Flow Matching: A Principled Route to Multi-Subject Fidelity [35.95129874095729]
  Text-to-image (T2I) models excel on single-entity prompts but struggle with multi-subject descriptions.<n>We introduce the first theoretical framework with principled optimizable objective for steering sampling dynamics toward multi-subject fidelity.
  arXiv  Detail & Related papers  (2025-10-02T17:59:58Z)
• Robust, Online, and Adaptive Decentralized Gaussian Processes [40.250871864353314]
  Decentralized random Fourier feature Gaussian processes (DRFGP) is an online and distributed algorithm that casts GPs in an information-filter form.<n>We extend DRFGP along two key directions: first, by introducing a robust-filtering update that downweights the impact of atypical observations; second, by incorporating a dynamic adaptation mechanism that adapts to time-varying functions.<n>We demonstrate its effectiveness on a large-scale Earth system application, underscoring its potential for in-situ modeling.
  arXiv  Detail & Related papers  (2025-09-22T16:49:49Z)
• Pulse-based optimization of quantum many-body states with Rydberg atoms in optical tweezer arrays [39.58317527488534]
  We explore a pulse-based variational quantum eigensolver algorithm for Rydberg atoms in optical tweezer arrays.<n>We numerically demonstrate that the ground states of the one-dimensional antiferromagnetic Heisenberg model and the mixed-field Ising model can be accurately prepared.
  arXiv  Detail & Related papers  (2025-07-25T10:51:49Z)
• Neural-network-based design and implementation of fast and robust quantum gates [0.0]
  We present a continuous-time, neural-network-based approach to optimal control in quantum systems, with a focus on pulse engineering for quantum gates.<n>We construct control fields as outputs of trainable neural networks, thereby eliminating the need for discrete parametrization or predefined bases.
  arXiv  Detail & Related papers  (2025-05-04T10:12:27Z)
• Foundation Neural-Networks Quantum States as a Unified Ansatz for Multiple Hamiltonians [39.47297975798672]
  Foundation Neural-Network Quantum States (FNQS) is an integrated paradigm for studying quantum many-body systems.<n>FNQS leverage key principles of foundation models to define variational wave functions based on a single, versatile architecture.<n>FNQS can generalize to physical Hamiltonians beyond those encountered during training.
  arXiv  Detail & Related papers  (2025-02-13T16:52:07Z)
• Quantum optimal control of superconducting qubits based on machine-learning characterization [39.58317527488534]
  We propose an experimentally simple approach to realize optimal quantum controls tailored to the device parameters and environment. We use physics-inspired machine learning to infer an accurate model of the dynamics from experimentally available data. We show the power and feasibility of this approach by optimizing arbitrary single-qubit operations on a superconducting transmon qubit.
  arXiv  Detail & Related papers  (2024-10-29T23:54:25Z)
• Message-Passing Neural Quantum States for the Homogeneous Electron Gas [41.94295877935867]
  We introduce a message-passing-neural-network-based wave function Ansatz to simulate extended, strongly interacting fermions in continuous space. We demonstrate its accuracy by simulating the ground state of the homogeneous electron gas in three spatial dimensions.
  arXiv  Detail & Related papers  (2023-05-12T04:12:04Z)
• Sample-efficient Model-based Reinforcement Learning for Quantum Control [0.2999888908665658]
  We propose a model-based reinforcement learning (RL) approach for noisy time-dependent gate optimization. We show an order of magnitude advantage in the sample complexity of our method over standard model-free RL. Our algorithm is well suited for controlling partially characterised one and two qubit systems.
  arXiv  Detail & Related papers  (2023-04-19T15:05:19Z)

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.