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The Journal of Chemical Physics

Simulating many-body open quantum systems by harnessing the power of artificial intelligence and quantum computing

Lyuzhou Ye, Yao Wang, Xiao Hong Zheng

Published Mar 24, 2025• 7 citations
Publisher

Abstract

Simulating many-body open quantum systems (OQSs) is challenging due to the intricate interplay between the system and its environment, resulting in strong quantum correlations in both space and time. This Perspective presents an overview of recently developed theoretical methods using artificial intelligence (AI) and quantum computing (QC) to simulate the dynamics of these systems. We briefly introduce the dissipaton-embedded quantum master equation in second quantization, which provides a single master equation suitable for representation by neural quantum states or quantum circuits. The promising performance of AI- and QC-based approaches is demonstrated through preliminary research on simulating the quantum dissipative dynamics of many-body OQSs. We also discuss the limitations and future developments of these methods, which hold promise for overcoming the computational challenges associated with many-body OQS dynamics.

DOI
10.1063/5.0242648

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Simulating many-body open quantum systems (OQSs) is challenging due to the intricate interplay between the system and its environment, resulting in strong quantum correlations in both space and time. This Perspective presents an overview of recently developed theoretical methods using artificial intelligence (AI) and quantum computing (QC) to simulate the dynamics of these systems. We briefly introduce the dissipaton-embedded quantum master equation in second quantization, which provides a single master equation suitable for representation by neural quantum states or quantum circuits. The promising performance of AI- and QC-based approaches is demonstrated through preliminary research on simulating the quantum dissipative dynamics of many-body OQSs. We also discuss the limitations and future developments of these methods, which hold promise for overcoming the computational challenges associated with many-body OQS dynamics.
Reducing computational scaling for simulating non-Markovian dissipative dynamics using artificial neural networks is both a major focus and formidable challenge in open quantum systems. To enable neural quantum states (NQSs), we encode environmental memory in dissipatons (quasiparticles with characteristic lifetimes), yielding the dissipaton-embedded quantum master equation (DQME). The resulting NQS-DQME framework achieves compact representation of many-body correlations and non-Markovian memory. Benchmarking against numerically exact hierarchical equations of motion confirms NQS-DQME maintains comparable accuracy while enhancing scalability and interpretability. This methodology opens new paths to explore non-Markovian open quantum dynamics in previously intractable systems.
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Numerous variational methods have been proposed for solving quantum many-body systems, but they often face exponentially increasing computational complexity as the Hilbert space dimension grows. To address this, we introduce a novel approach using quantum neural networks to simulate the dissipative dynamics of many-body open quantum systems. This method combines neural-network quantum state representation with the time-dependent variational principle, both implemented via quantum algorithms. This results in accurate open quantum dynamics described by the Lindblad quantum master equation, exemplified by the spin-boson and transverse field Ising models. Our approach avoids the computational expense of classical algorithms and demonstrates the potential advantages of quantum computing for many-body simulations. To reduce measurement errors, we introduce a projection reset procedure, which could benefit other quantum simulations. In addition, our approach can be extended to simulate non-Markovian quantum dynamics.
The intersection of artificial intelligence (AI) and quantum physics has sparked significant advancements in both fields. This paper first reviews the application of AI techniques, particularly neural networks, to quantum simulation. We discuss key developments in representing quantum states, efficient sampling methods, and optimization techniques for ground states and dynamics. The review also explores how quantum principles enhances AI algorithms, including quantum-inspired neural architectures and physics-based optimization methods. We highlight challenges and open questions in this rapidly evolving interdisciplinary field, emphasizing the potential for future innovations that combine AI and quantum approaches to address complex problems in both domains.
In the twenty-first century, humanity is compelled to face global challenges. Such challenges involve complex systems. However, science has some cognitive and predictive limits in dealing with complex systems. Some of these limits are related to computational complexity and the recognition of variable patterns. To overcome these limits, artificial intelligence (AI) and quantum computing (QC) appear to be helpful. Even more promising is quantum AI (QAI), which emerged from the combination of AI and QC. The combination of AI and QC produces reciprocal, synergistic effects. This work describes some of these effects. It shows that QC offers new materials for implementing AI and innovative algorithms for solving optimisation problems and enhancing machine learning algorithms. Additionally, it demonstrates how AI algorithms can help overcome many of the experimental challenges associated with implementing QC. It also outlines several perspectives for the future development of quantum artificial intelligence.
This study is based on Artificial Intelligence (AI) and Quantum Technology which represent two transformative frontiers in modern science. Their integration promises exponential computational enhancements, novel algorithmic models, and robust problem-solving frameworks for intractable problems. This paper explores the synergy between AI and quantum technologies, particularly focusing on how AI algorithms optimize quantum processes and vice versa. We present implementation methodologies, practical results from simulations, and discuss the mutual reinforcement of these technologies for future applications. The integration of Artificial Intelligence (AI) with Quantum Technology (QT) promises to revolutionize computational paradigms. This paper explores how AI models can enhance quantum error correction, optimize quantum circuit design, and facilitate quantum system control. We detail the methodology used to implement AI algorithms in quantum simulations and report results that highlight performance improvements in system efficiency, accuracy, and learning rates. The discussion covers implications for quantum computing, communications, and AI-driven quantum research.