Neural Network Approach for Non-Markovian Dissipative Dynamics of Many-Body Open Quantum Systems

• URL: http://arxiv.org/abs/2404.11093v1
• Date: Wed, 17 Apr 2024 06:17:08 GMT
• Title: Neural Network Approach for Non-Markovian Dissipative Dynamics of Many-Body Open Quantum Systems
• Authors: Long Cao, Liwei Ge, Daochi Zhang, Xiang Li, Yao Wang, Rui-Xue Xu, YiJing Yan, Xiao Zheng,
• Abstract summary: We integrate the neural quantum states approach into the dissipaton-embedded quantum master equation in second quantization (DQME-SQ) Our approach compactly represents the reduced density tensor, explicitly encoding the combined effects of system-environment correlations and nonMarkovian memory. The novel RBM-based DQME-SQ approach paves the way for investigating non-Markovian open quantum dynamics in previously intractable regimes.
• Score: 9.775774445091516
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Simulating the dynamics of open quantum systems coupled to non-Markovian environments remains an outstanding challenge due to exponentially scaling computational costs. We present an artificial intelligence strategy to overcome this obstacle by integrating the neural quantum states approach into the dissipaton-embedded quantum master equation in second quantization (DQME-SQ). Our approach utilizes restricted Boltzmann machines (RBMs) to compactly represent the reduced density tensor, explicitly encoding the combined effects of system-environment correlations and nonMarkovian memory. Applied to model systems exhibiting prominent effects of system-environment correlation and non-Markovian memory, our approach achieves comparable accuracy to conventional hierarchical equations of motion, while requiring significantly fewer dynamical variables. The novel RBM-based DQME-SQ approach paves the way for investigating non-Markovian open quantum dynamics in previously intractable regimes, with implications spanning various frontiers of modern science.

Related papers

• Stochastic Quantum Spiking Neural Networks with Quantum Memory and Local Learning [32.56953949580735]
  Neuromorphic and quantum computing have emerged as promising paradigms for advancing artificial intelligence.<n>Here we propose a quantum spiking (SQS) neuron model that addresses these challenges.<n>The SQS neuron uses multi-qubit quantum circuits to realize a spiking unit with internal quantum memory.<n>The proposed SQSNN model fuses the time-series efficiency of neuromorphic computing with the exponentially large inner state space of quantum computing.
  arXiv  Detail & Related papers  (2025-06-26T14:39:14Z)
• Unitary Scrambling and Collapse: A Quantum Diffusion Framework for Generative Modeling [5.258882634977828]
  We propose QSC-Diffusion, the first fully quantum diffusion-based framework for image generation.<n>We employ parameterized quantum circuits with measurement-induced collapse for reverse denoising.<n>Remarkably, QSC-Diffusion achieves competitive FID scores across multiple datasets.
  arXiv  Detail & Related papers  (2025-06-12T11:00:21Z)
• VQC-MLPNet: An Unconventional Hybrid Quantum-Classical Architecture for Scalable and Robust Quantum Machine Learning [60.996803677584424]
  Variational Quantum Circuits (VQCs) offer a novel pathway for quantum machine learning.<n>Their practical application is hindered by inherent limitations such as constrained linear expressivity, optimization challenges, and acute sensitivity to quantum hardware noise.<n>This work introduces VQC-MLPNet, a scalable and robust hybrid quantum-classical architecture designed to overcome these obstacles.
  arXiv  Detail & Related papers  (2025-06-12T01:38:15Z)
• Minimal Quantum Reservoirs with Hamiltonian Encoding [72.27323884094953]
  We investigate a minimal architecture for quantum reservoir computing based on Hamiltonian encoding.<n>This approach circumvents many of the experimental overheads typically associated with quantum machine learning.
  arXiv  Detail & Related papers  (2025-05-28T16:50:05Z)
• Characterizing Non-Markovian Dynamics of Open Quantum Systems [0.0]
  We develop a structure-preserving approach to characterizing non-Markovian evolution using the time-convolutionless (TCL) master equation. We demonstrate our methodology using experimental data from a superconducting qubit at the Quantum Device Integration Testbed (QuDIT) at Lawrence Livermore National Laboratory. These findings provide valuable insights into efficient modeling strategies for open quantum systems, with implications for quantum control and error mitigation in near-term quantum processors.
  arXiv  Detail & Related papers  (2025-03-28T04:43:24Z)
• 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. 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. 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)
• qHEOM: A Quantum Algorithm for Simulating Non-Markovian Quantum Dynamics Using the Hierarchical Equations of Motion [0.0]
  We introduce a quantum algorithm designed to simulate non-Markovian dynamics of open quantum systems. Our approach enables the implementation of arbitrary quantum master equations on noisy intermediate-scale quantum computers.
  arXiv  Detail & Related papers  (2024-11-18T20:41:10Z)
• Leveraging Pre-Trained Neural Networks to Enhance Machine Learning with Variational Quantum Circuits [48.33631905972908]
  We introduce an innovative approach that utilizes pre-trained neural networks to enhance Variational Quantum Circuits (VQC) This technique effectively separates approximation error from qubit count and removes the need for restrictive conditions. Our results extend to applications such as human genome analysis, demonstrating the broad applicability of our approach.
  arXiv  Detail & Related papers  (2024-11-13T12:03:39Z)
• Efficiency of Dynamical Decoupling for (Almost) Any Spin-Boson Model [44.99833362998488]
  We analytically study the dynamical decoupling of a two-level system coupled with a structured bosonic environment. We find sufficient conditions under which dynamical decoupling works for such systems. Our bounds reproduce the correct scaling in various relevant system parameters.
  arXiv  Detail & Related papers  (2024-09-24T04:58:28Z)
• Fourier Neural Operators for Learning Dynamics in Quantum Spin Systems [77.88054335119074]
  We use FNOs to model the evolution of random quantum spin systems. We apply FNOs to a compact set of Hamiltonian observables instead of the entire $2n$ quantum wavefunction.
  arXiv  Detail & Related papers  (2024-09-05T07:18:09Z)
• Quantum Simulation of Open Quantum Dynamics via Non-Markovian Quantum State Diffusion [2.9413085575648235]
  Quantum simulation of non-Markovian open quantum dynamics is essential but challenging for standard quantum computers. We introduce a hybrid quantum-classical algorithm designed for simulating dissipative dynamics in system with non-Markovian environment.
  arXiv  Detail & Related papers  (2024-04-16T15:31:25Z)
• Projective Quantum Eigensolver via Adiabatically Decoupled Subsystem Evolution: a Resource Efficient Approach to Molecular Energetics in Noisy Quantum Computers [0.0]
  We develop a projective formalism that aims to compute ground-state energies of molecular systems accurately using Noisy Intermediate Scale Quantum (NISQ) hardware. We demonstrate the method's superior performance under noise while concurrently ensuring requisite accuracy in future fault-tolerant systems.
  arXiv  Detail & Related papers  (2024-03-13T13:27:40Z)
• Towards Neural Variational Monte Carlo That Scales Linearly with System Size [67.09349921751341]
  Quantum many-body problems are central to demystifying some exotic quantum phenomena, e.g., high-temperature superconductors. The combination of neural networks (NN) for representing quantum states, and the Variational Monte Carlo (VMC) algorithm, has been shown to be a promising method for solving such problems. We propose a NN architecture called Vector-Quantized Neural Quantum States (VQ-NQS) that utilizes vector-quantization techniques to leverage redundancies in the local-energy calculations of the VMC algorithm.
  arXiv  Detail & Related papers  (2022-12-21T19:00:04Z)
• Dynamics with autoregressive neural quantum states: application to critical quench dynamics [41.94295877935867]
  We present an alternative general scheme that enables one to capture long-time dynamics of quantum systems in a stable fashion. We apply the scheme to time-dependent quench dynamics by investigating the Kibble-Zurek mechanism in the two-dimensional quantum Ising model.
  arXiv  Detail & Related papers  (2022-09-07T15:50:00Z)
• Decimation technique for open quantum systems: a case study with driven-dissipative bosonic chains [62.997667081978825]
  Unavoidable coupling of quantum systems to external degrees of freedom leads to dissipative (non-unitary) dynamics. We introduce a method to deal with these systems based on the calculation of (dissipative) lattice Green's function. We illustrate the power of this method with several examples of driven-dissipative bosonic chains of increasing complexity.
  arXiv  Detail & Related papers  (2022-02-15T19:00:09Z)
• Simulating the Mott transition on a noisy digital quantum computer via Cartan-based fast-forwarding circuits [62.73367618671969]
  Dynamical mean-field theory (DMFT) maps the local Green's function of the Hubbard model to that of the Anderson impurity model. Quantum and hybrid quantum-classical algorithms have been proposed to efficiently solve impurity models. This work presents the first computation of the Mott phase transition using noisy digital quantum hardware.
  arXiv  Detail & Related papers  (2021-12-10T17:32:15Z)
• Quantum algorithms for quantum dynamics: A performance study on the spin-boson model [68.8204255655161]
  Quantum algorithms for quantum dynamics simulations are traditionally based on implementing a Trotter-approximation of the time-evolution operator. variational quantum algorithms have become an indispensable alternative, enabling small-scale simulations on present-day hardware. We show that, despite providing a clear reduction of quantum gate cost, the variational method in its current implementation is unlikely to lead to a quantum advantage.
  arXiv  Detail & Related papers  (2021-08-09T18:00:05Z)
• Efficient Quantum Simulation of Open Quantum System Dynamics on Noisy Quantum Computers [0.0]
  We show that quantum dissipative dynamics can be simulated efficiently across coherent-to-incoherent regimes. This work provides a new direction for quantum advantage in the NISQ era.
  arXiv  Detail & Related papers  (2021-06-24T10:37:37Z)
• Probing non-Markovian quantum dynamics with data-driven analysis: Beyond "black-box" machine learning models [0.0]
  We propose a data-driven approach to the analysis of the non-Markovian dynamics of open quantum systems. Our method allows, on the one hand, capturing the effective dimension of the environment and the spectrum of the joint system-environment quantum dynamics. We demonstrate the performance of the proposed approach with various models of open quantum systems.
  arXiv  Detail & Related papers  (2021-03-26T14:27:33Z)
• 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)
• Quantum Non-equilibrium Many-Body Spin-Photon Systems [91.3755431537592]
  dissertation concerns the quantum dynamics of strongly-correlated quantum systems in out-of-equilibrium states. Our main results can be summarized in three parts: Signature of Critical Dynamics, Driven Dicke Model as a Test-bed of Ultra-Strong Coupling, and Beyond the Kibble-Zurek Mechanism.
  arXiv  Detail & Related papers  (2020-07-23T19:05:56Z)
• Capturing Non-Markovian Dynamics on Near-Term Quantum Computers [0.0]
  quantum algorithms for the treatment of open quantum systems (OQSs) have remained under-explored. We present and validate a new quantum algorithm to treat non-Markovian dynamics in OQSs built on the Ensemble of Lindblad's Trajectories approach.
  arXiv  Detail & Related papers  (2020-04-30T18:02:15Z)

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.