Sequence-Model-Guided Measurement Selection for Quantum State Learning

• URL: http://arxiv.org/abs/2507.09891v1
• Date: Mon, 14 Jul 2025 03:50:42 GMT
• Title: Sequence-Model-Guided Measurement Selection for Quantum State Learning
• Authors: Jiaxin Huang, Yan Zhu, Giulio Chiribella, Ya-Dong Wu,
• Abstract summary: We introduce a deep neural network with a sequence model architecture that searches for efficient measurement choices in a data-driven, adaptive manner.<n>The model can be applied to a variety of tasks, including the prediction of linear and nonlinear properties of quantum states.<n>For topological quantum systems, our model tends to recommend measurements at the system's boundaries, even when the task is to predict bulk properties.
• Score: 15.098042082558544
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Characterization of quantum systems from experimental data is a central problem in quantum science and technology. But which measurements should be used to gather data in the first place? While optimal measurement choices can be worked out for small quantum systems, the optimization becomes intractable as the system size grows large. To address this problem, we introduce a deep neural network with a sequence model architecture that searches for efficient measurement choices in a data-driven, adaptive manner. The model can be applied to a variety of tasks, including the prediction of linear and nonlinear properties of quantum states, as well as state clustering and state tomography tasks. In all these tasks, we find that the measurement choices identified by our neural network consistently outperform the uniformly random choice. Intriguingly, for topological quantum systems, our model tends to recommend measurements at the system's boundaries, even when the task is to predict bulk properties. This behavior suggests that the neural network may have independently discovered a connection between boundaries and bulk, without having been provided any built-in knowledge of quantum physics.

Related papers

• Quantum Convolutional Neural Network with Flexible Stride [7.362858964229726]
  We propose a novel quantum convolutional neural network algorithm.<n>It can flexibly adjust the stride to accommodate different tasks.<n>It can achieve exponential acceleration of data scale in less memory compared with its classical counterpart.
  arXiv  Detail & Related papers  (2024-12-01T02:37:06Z)
• Learning quantum properties from short-range correlations using multi-task networks [3.7228085662092845]
  We introduce a neural network model that can predict various quantum properties of many-body quantum states with constant correlation length. The model is based on the technique of multi-task learning, which we show to offer several advantages over traditional single-task approaches.
  arXiv  Detail & Related papers  (2023-10-18T08:53:23Z)
• Learning the tensor network model of a quantum state using a few single-qubit measurements [0.0]
  The constantly increasing dimensionality of artificial quantum systems demands highly efficient methods for their characterization and benchmarking. Here we present a constructive and numerically efficient protocol which learns a tensor network model of an unknown quantum system.
  arXiv  Detail & Related papers  (2023-09-01T11:11:52Z)
• ShadowNet for Data-Centric Quantum System Learning [188.683909185536]
  We propose a data-centric learning paradigm combining the strength of neural-network protocols and classical shadows. Capitalizing on the generalization power of neural networks, this paradigm can be trained offline and excel at predicting previously unseen systems. We present the instantiation of our paradigm in quantum state tomography and direct fidelity estimation tasks and conduct numerical analysis up to 60 qubits.
  arXiv  Detail & Related papers  (2023-08-22T09:11:53Z)
• 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)
• Adaptive, Continuous Entanglement Generation for Quantum Networks [59.600944425468676]
  Quantum networks rely on entanglement between qubits at distant nodes to transmit information. We present an adaptive scheme that uses information from previous requests to better guide the choice of randomly generated quantum links. We also explore quantum memory allocation scenarios, where a difference in latency performance implies the necessity of optimal allocation of resources for quantum networks.
  arXiv  Detail & Related papers  (2022-12-17T05:40:09Z)
• A didactic approach to quantum machine learning with a single qubit [68.8204255655161]
  We focus on the case of learning with a single qubit, using data re-uploading techniques. We implement the different proposed formulations in toy and real-world datasets using the qiskit quantum computing SDK.
  arXiv  Detail & Related papers  (2022-11-23T18:25:32Z)
• Neural network enhanced measurement efficiency for molecular groundstates [63.36515347329037]
  We adapt common neural network models to learn complex groundstate wavefunctions for several molecular qubit Hamiltonians. We find that using a neural network model provides a robust improvement over using single-copy measurement outcomes alone to reconstruct observables.
  arXiv  Detail & Related papers  (2022-06-30T17:45:05Z)
• Adaptive Quantum State Tomography with Active Learning [0.0]
  We propose and implement an efficient scheme for quantum state tomography using active learning. We apply the scheme to reconstruct different multi-qubit states with varying degree of entanglement as well as to ground states of the XXZ model in 1D and a kinetically constrained spin chain. Our scheme is highly relevant to gain physical insights in quantum many-body systems as well as for benchmarking and characterizing quantum devices.
  arXiv  Detail & Related papers  (2022-03-29T16:23:10Z)
• Flexible learning of quantum states with generative query neural networks [4.540894342435848]
  We show that learning across multiple quantum states can be achieved by a generative query neural network. Our network can be trained offline with classically simulated data, and later be used to characterize unknown quantum states from real experimental data.
  arXiv  Detail & Related papers  (2022-02-14T15:48:27Z)
• Towards Quantum Graph Neural Networks: An Ego-Graph Learning Approach [47.19265172105025]
  We propose a novel hybrid quantum-classical algorithm for graph-structured data, which we refer to as the Ego-graph based Quantum Graph Neural Network (egoQGNN) egoQGNN implements the GNN theoretical framework using the tensor product and unity matrix representation, which greatly reduces the number of model parameters required. The architecture is based on a novel mapping from real-world data to Hilbert space.
  arXiv  Detail & Related papers  (2022-01-13T16:35:45Z)
• Quantum-tailored machine-learning characterization of a superconducting qubit [50.591267188664666]
  We develop an approach to characterize the dynamics of a quantum device and learn device parameters. This approach outperforms physics-agnostic recurrent neural networks trained on numerically generated and experimental data. This demonstration shows how leveraging domain knowledge improves the accuracy and efficiency of this characterization task.
  arXiv  Detail & Related papers  (2021-06-24T15:58:57Z)
• Random Sampling Neural Network for Quantum Many-Body Problems [0.0]
  We propose a general numerical method, Random Sampling Neural Networks (RSNN), to utilize the pattern recognition technique for the random sampling matrix elements of an interacting many-body system via a self-supervised learning approach. Several exactly solvable 1D models, including Ising model with transverse field, Fermi-Hubbard model, and spin-$1/2$ $XXZ$ model, are used to test the applicability of RSNN.
  arXiv  Detail & Related papers  (2020-11-10T15:52:44Z)

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.