Unified Quantum State Tomography and Hamiltonian Learning Using Transformer Models: A Language-Translation-Like Approach for Quantum Systems

• URL: http://arxiv.org/abs/2304.12010v1
• Date: Mon, 24 Apr 2023 11:20:44 GMT
• Title: Unified Quantum State Tomography and Hamiltonian Learning Using Transformer Models: A Language-Translation-Like Approach for Quantum Systems
• Authors: Zheng An, Jiahui Wu, Muchun Yang, D. L. Zhou, Bei Zeng
• Abstract summary: We introduce a new approach that employs the attention mechanism in transformer models to effectively merge quantum state tomography and Hamiltonian learning. We demonstrate the effectiveness of our approach across various quantum systems, ranging from simple 2-qubit cases to more involved 2D antiferromagnetic Heisenberg structures.
• Score: 0.47831562043724657
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Schr\"odinger's equation serves as a fundamental component in characterizing quantum systems, wherein both quantum state tomography and Hamiltonian learning are instrumental in comprehending and interpreting quantum systems. While numerous techniques exist for carrying out state tomography and learning Hamiltonians individually, no method has been developed to combine these two aspects. In this study, we introduce a new approach that employs the attention mechanism in transformer models to effectively merge quantum state tomography and Hamiltonian learning. By carefully choosing and preparing the training data, our method integrates both tasks without altering the model's architecture, allowing the model to effectively learn the intricate relationships between quantum states and Hamiltonian. We also demonstrate the effectiveness of our approach across various quantum systems, ranging from simple 2-qubit cases to more involved 2D antiferromagnetic Heisenberg structures. The data collection process is streamlined, as it only necessitates a one-way generation process beginning with state tomography. Furthermore, the scalability and few-shot learning capabilities of our method could potentially minimize the resources required for characterizing and optimizing quantum systems. Our research provides valuable insights into the relationship between Hamiltonian structure and quantum system behavior, fostering opportunities for additional studies on quantum systems and the advancement of quantum computation and associated technologies.

Related papers

• Hybrid Quantum-Classical Clustering for Preparing a Prior Distribution of Eigenspectrum [10.950807972899575]
  We consider preparing the prior distribution and circuits for the eigenspectrum of time-independent Hamiltonians. The proposed algorithm unfolds in three strategic steps: Hamiltonian transformation, parameter representation, and classical clustering. The algorithm is showcased through applications to the 1D Heisenberg system and the LiH molecular system.
  arXiv  Detail & Related papers  (2024-06-29T14:21:55Z)
• Dual-Capability Machine Learning Models for Quantum Hamiltonian Parameter Estimation and Dynamics Prediction [2.142387003055715]
  This study introduces a machine learning model with dual capabilities. It can deduce time-dependent Hamiltonian parameters from observed changes in local observables. It can predict the evolution of these observables based on Hamiltonian parameters.
  arXiv  Detail & Related papers  (2024-05-22T12:21:57Z)
• Quantum data learning for quantum simulations in high-energy physics [55.41644538483948]
  We explore the applicability of quantum-data learning to practical problems in high-energy physics. We make use of ansatz based on quantum convolutional neural networks and numerically show that it is capable of recognizing quantum phases of ground states. The observation of non-trivial learning properties demonstrated in these benchmarks will motivate further exploration of the quantum-data learning architecture in high-energy physics.
  arXiv  Detail & Related papers  (2023-06-29T18:00:01Z)
• 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)
• Modern applications of machine learning in quantum sciences [51.09906911582811]
  We cover the use of deep learning and kernel methods in supervised, unsupervised, and reinforcement learning algorithms. We discuss more specialized topics such as differentiable programming, generative models, statistical approach to machine learning, and quantum machine learning.
  arXiv  Detail & Related papers  (2022-04-08T17:48:59Z)
• Memory Complexity of Quantum Processes [0.0]
  Generic open quantum dynamics can be described by two seemingly very distinct approaches. The process tensor framework describes all the possible observations one could possibly make on a quantum system. The intimate connection between quantum processes and classical processes is drawn in the end.
  arXiv  Detail & Related papers  (2022-03-03T03:04:33Z)
• 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)
• Preparation of Many-body Ground States by Time Evolution with Variational Microscopic Magnetic Fields and Incomplete Interactions [1.6554452963165365]
  State preparation is of fundamental importance in quantum physics. We study the latter on quantum many-body systems by the time evolution with fixed couplings and variational magnetic fields. An optimization method is proposed to optimize the magnetic fields by "fine-graining" the discretization of time.
  arXiv  Detail & Related papers  (2021-06-03T12:04:36Z)
• Bidirectional information flow quantum state tomography [0.0]
  We propose a quantum state tomography method, which is based on Bidirectional Gated Recurrent Unit neural network (BiGRU) We are able to use fewer measurement samples in our method to reconstruct these quantum states and obtain high fidelity.
  arXiv  Detail & Related papers  (2021-03-31T02:57:27Z)
• Information Scrambling in Computationally Complex Quantum Circuits [56.22772134614514]
  We experimentally investigate the dynamics of quantum scrambling on a 53-qubit quantum processor. We show that while operator spreading is captured by an efficient classical model, operator entanglement requires exponentially scaled computational resources to simulate.
  arXiv  Detail & Related papers  (2021-01-21T22:18:49Z)
• 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.