Application of Large Language Models to Quantum State Simulation

• URL: http://arxiv.org/abs/2410.06629v2
• Date: Wed, 30 Oct 2024 07:46:35 GMT
• Title: Application of Large Language Models to Quantum State Simulation
• Authors: Shuangxiang Zhou, Ronghang Chen, Zheng An, Shi-Yao Hou,
• Abstract summary: Currently, various quantum simulators provide powerful tools for researchers, but simulating quantum evolution with these simulators often incurs high time costs. This paper details the process of constructing 1-qubit and 2-qubit quantum simulator models, extending to multiple qubits, and ultimately implementing a 3-qubit example. Our study demonstrates that LLMs can effectively learn and predict the evolution patterns among quantum bits, with minimal error compared to the theoretical output states.
• Score: 0.11666234644810894
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantum computers leverage the unique advantages of quantum mechanics to achieve acceleration over classical computers for certain problems. Currently, various quantum simulators provide powerful tools for researchers, but simulating quantum evolution with these simulators often incurs high time costs. Additionally, resource consumption grows exponentially as the number of quantum bits increases. To address this issue, our research aims to utilize Large Language Models (LLMs) to simulate quantum circuits. This paper details the process of constructing 1-qubit and 2-qubit quantum simulator models, extending to multiple qubits, and ultimately implementing a 3-qubit example. Our study demonstrates that LLMs can effectively learn and predict the evolution patterns among quantum bits, with minimal error compared to the theoretical output states. Even when dealing with quantum circuits comprising an exponential number of quantum gates, LLMs remain computationally efficient. Overall, our results highlight the potential of LLMs to predict the outputs of complex quantum dynamics, achieving speeds far surpassing those required to run the same process on a quantum computer. This finding provides new insights and tools for applying machine learning methods in the field of quantum computing.

Related papers

• Efficient Learning for Linear Properties of Bounded-Gate Quantum Circuits [63.733312560668274]
  Given a quantum circuit containing d tunable RZ gates and G-d Clifford gates, can a learner perform purely classical inference to efficiently predict its linear properties? We prove that the sample complexity scaling linearly in d is necessary and sufficient to achieve a small prediction error, while the corresponding computational complexity may scale exponentially in d. We devise a kernel-based learning model capable of trading off prediction error and computational complexity, transitioning from exponential to scaling in many practical settings.
  arXiv  Detail & Related papers  (2024-08-22T08:21:28Z)
• The curse of random quantum data [62.24825255497622]
  We quantify the performances of quantum machine learning in the landscape of quantum data. We find that the training efficiency and generalization capabilities in quantum machine learning will be exponentially suppressed with the increase in qubits. Our findings apply to both the quantum kernel method and the large-width limit of quantum neural networks.
  arXiv  Detail & Related papers  (2024-08-19T12:18:07Z)
• Quantum Digital Simulation of Cavity Quantum Electrodynamics: Insights from Superconducting and Trapped Ion Quantum Testbeds [0.016994625126740815]
  We present an early exploration of the potential for quantum computers to efficiently investigate open CQED physics. Our simulations make use of a recent quantum algorithm that maps the dynamics of a singly excited open Tavis-Cummings model containing $N$ atoms. By applying technology-specific transpilation and error mitigation techniques, we minimize the impact of gate errors, noise, and decoherence in each hardware platform.
  arXiv  Detail & Related papers  (2024-04-05T02:25:49Z)
• QuantumSEA: In-Time Sparse Exploration for Noise Adaptive Quantum Circuits [82.50620782471485]
  QuantumSEA is an in-time sparse exploration for noise-adaptive quantum circuits. It aims to achieve two key objectives: (1) implicit circuits capacity during training and (2) noise robustness. Our method establishes state-of-the-art results with only half the number of quantum gates and 2x time saving of circuit executions.
  arXiv  Detail & Related papers  (2024-01-10T22:33:00Z)
• Towards practical and massively parallel quantum computing emulation for quantum chemistry [10.095945254794906]
  Quantum computing is moving beyond its early stage and seeking for commercial applications in chemical and biomedical sciences. It is valuable to emulate quantum computing on classical computers for developing quantum algorithms and validating quantum hardware. Here we demonstrate a high-performance and massively parallel variational quantum eigensolver simulator based on matrix product states.
  arXiv  Detail & Related papers  (2023-03-07T06:44:18Z)
• Quantum Machine Learning: from physics to software engineering [58.720142291102135]
  We show how classical machine learning approach can help improve the facilities of quantum computers. We discuss how quantum algorithms and quantum computers may be useful for solving classical machine learning tasks.
  arXiv  Detail & Related papers  (2023-01-04T23:37:45Z)
• Scalable Simulation of Quantum Measurement Process with Quantum Computers [13.14263204660076]
  We propose qubit models to emulate the quantum measurement process. One model is motivated by single-photon detection and the other by spin measurement. We generate Schr"odinger cat-like state, and their corresponding quantum circuits are shown explicitly.
  arXiv  Detail & Related papers  (2022-06-28T14:21:43Z)
• Recompilation-enhanced simulation of electron-phonon dynamics on IBM Quantum computers [62.997667081978825]
  We consider the absolute resource cost for gate-based quantum simulation of small electron-phonon systems. We perform experiments on IBM quantum hardware for both weak and strong electron-phonon coupling. Despite significant device noise, through the use of approximate circuit recompilation we obtain electron-phonon dynamics on current quantum computers comparable to exact diagonalisation.
  arXiv  Detail & Related papers  (2022-02-16T19:00:00Z)
• QuantumSkynet: A High-Dimensional Quantum Computing Simulator [0.0]
  Current implementations of quantum computing simulators are limited to two-level quantum systems. Recent advances in high-dimensional quantum computing systems have demonstrated the viability of working with multi-level superposition and entanglement. We introduce QuantumSkynet, a novel high-dimensional cloud-based quantum computing simulator.
  arXiv  Detail & Related papers  (2021-06-30T06:28:18Z)
• On exploring the potential of quantum auto-encoder for learning quantum systems [60.909817434753315]
  We devise three effective QAE-based learning protocols to address three classically computational hard learning problems. Our work sheds new light on developing advanced quantum learning algorithms to accomplish hard quantum physics and quantum information processing tasks.
  arXiv  Detail & Related papers  (2021-06-29T14:01:40Z)
• Distributed Quantum Computing with QMPI [11.71212583708166]
  We introduce an extension of the Message Passing Interface (MPI) to enable high-performance implementations of distributed quantum algorithms. In addition to a prototype implementation of quantum MPI, we present a performance model for distributed quantum computing, SENDQ.
  arXiv  Detail & Related papers  (2021-05-03T18:30:43Z)

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.