Uncovering Quantum Many-body Scars with Quantum Machine Learning

• URL: http://arxiv.org/abs/2409.07405v1
• Date: Wed, 11 Sep 2024 16:49:07 GMT
• Title: Uncovering Quantum Many-body Scars with Quantum Machine Learning
• Authors: Jiajin Feng, Bingzhi Zhang, Zhi-Cheng Yang, Quntao Zhuang,
• Abstract summary: We employ tools from quantum machine learning -- specifically, quantum convolutional neural networks (QCNNs) -- to explore hidden non-thermal states in quantum many-body systems. Our simulations demonstrate that QCNNs achieve over 99% single-shot measurement accuracy in identifying all known scars. We successfully identify new non-thermal states in models such as the xorX model, the PXP model, and the far-coupling Su-Schrieffer-Heeger model.
• Score: 3.8822047197572975
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantum many-body scars are rare eigenstates hidden within the chaotic spectra of many-body systems, representing a weak violation of the eigenstate thermalization hypothesis (ETH). Identifying these scars, as well as other non-thermal states in complex quantum systems, remains a significant challenge. Besides exact scar states, the nature of other non-thermal states lacking simple analytical characterization remains an open question. In this study, we employ tools from quantum machine learning -- specifically, quantum convolutional neural networks (QCNNs), to explore hidden non-thermal states in chaotic many-body systems. Our simulations demonstrate that QCNNs achieve over 99% single-shot measurement accuracy in identifying all known scars. Furthermore, we successfully identify new non-thermal states in models such as the xorX model, the PXP model, and the far-coupling Su-Schrieffer-Heeger model. In the xorX model, some of these non-thermal states can be approximately described as spin-wave modes of specific quasiparticles. We further develop effective tight-binding Hamiltonians within the quasiparticle subspace to capture key features of these many-body eigenstates. Finally, we validate the performance of QCNNs on IBM quantum devices, achieving single-shot measurement accuracy exceeding 63% under real-world noise and errors, with the aid of error mitigation techniques. Our results underscore the potential of QCNNs to uncover hidden non-thermal states in quantum many-body systems.

Related papers

• Robust Finite-Temperature Many-Body Scarring on a Quantum Computer [0.0]
  Mechanisms for suppressing thermalization in disorder-free many-body systems have attracted much interest. We utilize IBM's Kolkata quantum processor to demonstrate an unexpected robustness of quantum many-body scars at finite temperatures.
  arXiv  Detail & Related papers  (2023-09-20T18:00:01Z)
• 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)
• Extracting Quantum Many-Body Scarred Eigenstates with Matrix Product States [8.02228625350421]
  Quantum many-body scarred systems host nonthermal excited eigenstates immersed in a sea of thermal ones. We propose a matrix-product-state (MPS) algorithm, dubbed DMRG-S, to extract such states at system sizes far beyond the scope of exact diagonalization. We find several new scarred eigenstates with exact MPS representations in kinetically constrained spin and clock models.
  arXiv  Detail & Related papers  (2022-11-09T19:00:02Z)
• Prominent quantum many-body scars in a truncated Schwinger model [0.0]
  We show that quantum many-body scars exist in a truncated version of the Schwinger model. Our conclusions can be readily tested in current cold-atom setups.
  arXiv  Detail & Related papers  (2022-04-04T18:00:01Z)
• Unsupervised detection of decoupled subspaces: many-body scars and beyond [0.0]
  We introduce a quantum-inspired machine learning platform based on a Quantum Variational Autoencoder (QVAE) We demonstrate that the autoencoder trained on a scar state is able to detect the whole family of scar states sharing common features with the input state. The possibility of an automatic detection of subspaces of scar states opens new pathways in studies of models with a weak breakdown of ergodicity.
  arXiv  Detail & Related papers  (2022-01-18T17:48:33Z)
• Extensive multipartite entanglement from su(2) quantum many-body scars [0.0]
  We numerically study signatures of multipartite entanglement in the PXP model of Rydberg atoms. Our results identify a rich multipartite correlation structure of scarred states with significant potential as a resource in quantum enhanced metrology.
  arXiv  Detail & Related papers  (2021-09-20T17:56:04Z)
• Hardware-Efficient, Fault-Tolerant Quantum Computation with Rydberg Atoms [55.41644538483948]
  We provide the first complete characterization of sources of error in a neutral-atom quantum computer. We develop a novel and distinctly efficient method to address the most important errors associated with the decay of atomic qubits to states outside of the computational subspace. Our protocols can be implemented in the near-term using state-of-the-art neutral atom platforms with qubits encoded in both alkali and alkaline-earth atoms.
  arXiv  Detail & Related papers  (2021-05-27T23:29:53Z)
• Towards understanding the power of quantum kernels in the NISQ era [79.8341515283403]
  We show that the advantage of quantum kernels is vanished for large size datasets, few number of measurements, and large system noise. Our work provides theoretical guidance of exploring advanced quantum kernels to attain quantum advantages on NISQ devices.
  arXiv  Detail & Related papers  (2021-03-31T02:41:36Z)
• Exact many-body scars and their stability in constrained quantum chains [55.41644538483948]
  Quantum scars are non-thermal eigenstates characterized by low entanglement entropy. We study the response of these exact quantum scars to perturbations by analysing the scaling of the fidelity susceptibility with system size.
  arXiv  Detail & Related papers  (2020-11-16T19:05:50Z)
• On the learnability of quantum neural networks [132.1981461292324]
  We consider the learnability of the quantum neural network (QNN) built on the variational hybrid quantum-classical scheme. We show that if a concept can be efficiently learned by QNN, then it can also be effectively learned by QNN even with gate noise.
  arXiv  Detail & Related papers  (2020-07-24T06:34:34Z)
• Entanglement Classification via Neural Network Quantum States [58.720142291102135]
  In this paper we combine machine-learning tools and the theory of quantum entanglement to perform entanglement classification for multipartite qubit systems in pure states. We use a parameterisation of quantum systems using artificial neural networks in a restricted Boltzmann machine (RBM) architecture, known as Neural Network Quantum States (NNS)
  arXiv  Detail & Related papers  (2019-12-31T07:40:23Z)

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.