Forked Physics-Informed Neural Networks for Non-Markovian Open Quantum Dynamics and Control

• URL: http://arxiv.org/abs/2607.12567v1
• Date: Tue, 14 Jul 2026 09:38:03 GMT
• Title: Forked Physics-Informed Neural Networks for Non-Markovian Open Quantum Dynamics and Control
• Abstract summary: 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.
• Score: 1.3975180218814856
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Physics-informed neural networks (PINNs) provide a pathway to reunify the simulation and control of quantum systems, in which these two tasks are typically decoupled in traditional strategies. However, most work remains confined to Markovian environments. When applied to non-Markovian systems, standard PINN architectures fail to converge reliably due to multi-objective optimization conflicts arising from the coupled differential equations. To address this fundamental limitation, we extend our previously proposed forked PINN (FPINN) by incorporating a dedicated control branch. By decoupling the optimization objectives at the gradient level via selective gradient flow, our method turns a previously intractable multi-task optimization into a well-conditioned one, allowing simulation and control to be optimized jointly without compromise. Numerical simulations on a two-qubit Heisenberg XXX model confirm that our framework faithfully reproduces the features of non-Markovian dynamics, including decoherence and information backflow. Taking a state-preparation task on the same model as an example, our FPINN achieves higher fidelity than gradient ascent pulse engineering, chopped random basis, and standard PINNs, with the advantage becoming more pronounced as the environment becomes more dissipative and more Markovian. The generated pulses are also noticeably smoother, which is advantageous for experimental implementation. Our framework thus provides a unified, end-to-end differentiable paradigm for simulation and control of open quantum systems, with potential implications for quantum computing, simulation, and control.

Related papers

• Performance of Krotov, PRONTO and PINN for optimal control of quantum gates [0.0]
  We propose an enhanced PINN scheme for quantum optimal control (PINNQOC)<n>We benchmark our framework against two premier continuous control solvers.<n>Our advanced PINNQOC approach successfully suppresses population leakage while achieving gate fidelities exceeding 99.9$%$.
  arXiv  Detail & Related papers  (2026-07-25T16:21:48Z)
• FUSE: FK-Steered Multi-Modal Flow Matching for Efficient Simulation-Based Posterior Estimation [47.3675633392999]
  We introduce FUSE (Feynman-Kac steered mUlti-modal flow matching for efficient Simulation-based posterior Estimation)<n>Unlike prior work, FUSE employs a dual-track architecture that preserves the distinct features of multimodal inputs while facilitating dynamic interaction.<n>Our approach outperforms state-of-the-art baselines on standard SBI benchmarks, producing posteriors that closely match ground-truth MCMC.
  arXiv  Detail & Related papers  (2026-07-06T15:59:18Z)
• 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)
• 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)
• Forked Physics Informed Neural Networks for Coupled Systems of Differential equations [1.7158443622461192]
  We propose a Forked PINN framework designed for coupled systems of differential equations (DEs)<n>We demonstrate the effectiveness of FPINN in simulating non-Markovian open quantum dynamics governed by coupled DEs.<n>For the spin-boson and XXZ models, FPINN accurately captures non-Markovian features, such as quantum coherence revival and information backflow.
  arXiv  Detail & Related papers  (2026-02-16T08:32:59Z)
• 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)
• KITINet: Kinetics Theory Inspired Network Architectures with PDE Simulation Approaches [43.872190335490515]
  This paper introduces KITINet, a novel architecture that reinterprets feature propagation through the lens of non-equilibrium particle dynamics.<n>At its core, we propose a residual module that models update as the evolution of a particle system.<n>This formulation mimics particle collisions and energy exchange, enabling adaptive feature refinement via physics-informed interactions.
  arXiv  Detail & Related papers  (2025-05-23T13:58:29Z)
• Efficient Transformed Gaussian Process State-Space Models for Non-Stationary High-Dimensional Dynamical Systems [49.819436680336786]
  We propose an efficient transformed Gaussian process state-space model (ETGPSSM) for scalable and flexible modeling of high-dimensional, non-stationary dynamical systems.<n>Specifically, our ETGPSSM integrates a single shared GP with input-dependent normalizing flows, yielding an expressive implicit process prior that captures complex, non-stationary transition dynamics.<n>Our ETGPSSM outperforms existing GPSSMs and neural network-based SSMs in terms of computational efficiency and accuracy.
  arXiv  Detail & Related papers  (2025-03-24T03:19:45Z)
• Mitigating Barren Plateaus in Quantum Neural Networks via an AI-Driven Submartingale-Based Framework [3.0617189749929348]
  We propose AdaInit to mitigate barren plateaus (BPs) in quantum neural networks (QNNs)<n>AdaInit iteratively synthesizes initial parameters for QNNs that yield non-negligible gradient variance, thereby mitigating BPs.<n>We provide rigorous theoretical analyses of the submartingale-based process and empirically validate that AdaInit consistently outperforms existing methods in maintaining higher gradient variance across various QNN scales.
  arXiv  Detail & Related papers  (2025-02-17T05:57:15Z)
• Neural Projected Quantum Dynamics: a systematic study [0.0]
  We investigate the challenge of classical simulation of unitary quantum dynamics with variational Monte Carlo approaches.<n>We provide a thorough formalization and significant improvements to the projected time-dependent Monte Carlo (p-tVMC) method.<n>This work establishes p-tVMC as a powerful framework for simulating the dynamics of large-scale two-dimensional quantum systems.
  arXiv  Detail & Related papers  (2024-10-14T17:01:33Z)
• Compact Multi-Threshold Quantum Information Driven Ansatz For Strongly Interactive Lattice Spin Models [0.0]
  We introduce a systematic procedure for ansatz building based on approximate Quantum Mutual Information (QMI) Our approach generates a layered-structured ansatz, where each layer's qubit pairs are selected based on their QMI values, resulting in more efficient state preparation and optimization routines. Our results show that the Multi-QIDA method reduces the computational complexity while maintaining high precision, making it a promising tool for quantum simulations in lattice spin models.
  arXiv  Detail & Related papers  (2024-08-05T17:07:08Z)
• SupeRBNN: Randomized Binary Neural Network Using Adiabatic Superconductor Josephson Devices [44.440915387556544]
  AQFP devices serve as excellent carriers for binary neural network (BNN) computations. We propose SupeRBNN, an AQFP-based randomized BNN acceleration framework. We show that our design achieves an energy efficiency of approximately 7.8x104 times higher than that of the ReRAM-based BNN framework.
  arXiv  Detail & Related papers  (2023-09-21T16:14:42Z)
• U-FNO -- an enhanced Fourier neural operator based-deep learning model for multiphase flow [43.572675744374415]
  We present U-FNO, an enhanced Fourier neural operator for solving the multiphase flow problem. We show that the U-FNO architecture has the advantages of both traditional CNN and original FNO, providing significantly more accurate and efficient performance. The trained U-FNO provides gas saturation and pressure buildup predictions with a 10,000 times speedup compared to traditional numerical simulators.
  arXiv  Detail & Related papers  (2021-09-03T17:52:25Z)

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.