Scalable quantum state tomography with artificial neural networks

Schmale, Tobias; Reh, Moritz; Gärttner, Martin

doi:10.48550/arxiv.2109.13776

Cited by 2 publications

(4 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…more possible reference bases in the first step. Our active learning scheme can furthermore be generalized to state representations other than the restricted Boltzmann machines considered here, such as variational autoencoders [21], recurrent [22] and convolutional neural networks [23], generative adversarial networks [24], and transformer architectures [25].…”

Section: Discussionmentioning

confidence: 99%

“…Here, we use the implementation of RBM quantum state tomography by Beach et al [20] in the form of a python package called QuCumber. However, the idea of our AL scheme is independent of the specific implementation of the RBMs and can in principle be applied in combination with any other quantum state tomography scheme, such as other neural network architectures [21][22][23][24][25] as well as matrix-product state based state reconstruction [26,27]. Here, the target many-body quantum state is represented in terms of an artificial neu-ral network (RBM) wave function…”

Section: Restricted Boltzmann Machinesmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive Quantum State Tomography with Active Learning

Lange¹,

Kebrič²,

Buser³

et al. 2022

Preprint

View full text Add to dashboard Cite

Recently, tremendous progress has been made in the field of quantum science and technologies: different platforms for quantum simulation as well as quantum computing, ranging from superconducting qubits to neutral atoms, are starting to reach unprecedentedly large systems. In order to benchmark these systems and gain physical insights, the need for efficient tools to characterize quantum states arises. The exponential growth of the Hilbert space with system size renders a full reconstruction of the quantum state prohibitively demanding in terms of the number of necessary measurements. Here we propose and implement an efficient scheme for quantum state tomography using active learning. Based on a few initial measurements, the active learning protocol proposes the next measurement basis, designed to yield the maximum information gain. For a fixed total number of measurements and basis configurations, our algorithm maximizes the information one can obtain about the quantum state under consideration. We apply the active learning quantum state tomography scheme to reconstruct different multi-qubit states with varying degree of entanglement as well as to ground states of a kinetically constrained spin chain. In all cases, we obtain a significantly improved reconstruction as compared to a reconstruction based on the exact same number of measurements, but with randomly chosen basis configurations. Our scheme is highly relevant to gain physical insights in quantum many-body systems as well as for the characterization of quantum devices, and paves the way for benchmarking and probing large quantum systems.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Restricted Boltzmann Machinesmentioning

confidence: 99%

Adaptive Quantum State Tomography with Active Learning

Lange¹,

Kebrič²,

Buser³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…For instance, neural networks (NNs) have been used with reasonable success as variational wavefunctions of quantum many-body systems [2][3][4][5][6][7][8][9][10]. Irrespective of the type of NN employed as variational state, the vast majority of methods to train NNs rely on Markov chain Monte Carlo (MCMC) sampling and gradient descent [1].…”

mentioning

confidence: 99%

“…This is called quantum state tomography (QST) [13,14]. While traditional, bruteforce methods require tens of thousands of measurements to reconstruct even small quantum states [15], recent advancements in machine learning methods have greatly improved the efficiency of such a task, making it feasible to perform QST on states with tens or even hundreds of qubits [5,7,9,16]. Yet, as we will show below such methods are still inefficient when the quantum states showcase strongly non-local features.…”

mentioning

confidence: 99%

Efficient Quantum State Tomography with Mode-assisted Training

Zhang¹,

Ventra²

2021

Preprint

View full text Add to dashboard Cite

Neural networks (NNs) representing quantum states are typically trained using Markov chain Monte Carlo sampling and gradient-based methods. However, such approaches cannot easily extract the global (long-range) structure of quantum states, which is essential for certain problems. Here, we propose to use a mode-assisted training that provides global information via the modes of the NN distribution. Applied to quantum state tomography using restricted Boltzmann machines, this method improves the quality of reconstructed quantum states by orders of magnitude. The method is applicable to other types of NNs and may efficiently tackle problems previously unmanageable.

show abstract

Scalable quantum state tomography with artificial neural networks

Cited by 2 publications

References 38 publications

Adaptive Quantum State Tomography with Active Learning

Adaptive Quantum State Tomography with Active Learning

Efficient Quantum State Tomography with Mode-assisted Training

Contact Info

Product

Resources

About