Unsupervised detection of decoupled subspaces: Many-body scars and beyond

Szołdra, Tomasz; Sierant, Piotr; Lewenstein, Maciej; Zakrzewski, Jakub

doi:10.1103/physrevb.105.224205

Cited by 10 publications

(2 citation statements)

References 66 publications

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“…Let us mention also that quench dynamics and Rabi oscillations resulting from the excitations of two or more localized integrals of motion in the context of many-body localization have recently been studied [20,21]. The localized, almost decoupled family of states may not be easy to identify, one may try to identify it e.g., by adiabatic following from some analytic limit [22] or via purely numerical approaches including artificial intelligence [23].…”

Section: Quantum Scarsmentioning

confidence: 99%

Dynamical Quantum Phase Transitions from Quantum Optics Perspective

Zakrzewski

2023

Acta Phys. Pol. A

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In this work dedicated to Professor Iwo Białynicki-Birula on the occasion of his 90th birthday, I attempts to show that dynamical quantum phase transitions observed as singularities in the Loschmidt rate dynamics bear a close resemblance to the standard Rabi oscillations known from the dynamics of twolevel systems. For some many-body systems, this analogy may go even further, and the behaviour observed for example transverse Ising chain can be directly mapped to such simple dynamics. A simple link between Loschmidt echo singularities and quantum scars is also suggested. topics: phase transitions, Loschmidt echo singularities, Rabi oscillations, quantum scars

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Section: Quantum Scarsmentioning

confidence: 99%

Dynamical Quantum Phase Transitions from Quantum Optics Perspective

Zakrzewski

2023

Acta Phys. Pol. A

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“…The graphical illustrations in figure 1 demonstrate the pipeline for our approach, which uses sampled data from the PXP model to both embed in a low-dimensional space and obtain an ID estimate. We note that there has been some recent interest in the detection of quantum scars using neural network approaches [25,26]. These, however, are not truly unsupervised methods and require partial training of the neural networks using existing knowledge of some scar states, which may not always be accessible.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised learning of quantum many-body scars using intrinsic dimension

Cao,

Angelakis,

Leykam

2024

Mach. Learn.: Sci. Technol.

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Quantum many-body scarred systems contain both thermal and non-thermal scar eigenstates in their spectra. When these systems are quenched from special initial states which share high overlap with scar eigenstates, the system undergoes dynamics with atypically slow relaxation and periodic revival. This scarring phenomenon poses a potential avenue for circumventing decoherence in various quantum engineering applications. Given access to an unknown scar system, current approaches for identification of special states leading to non-thermal dynamics rely on costly measures such as entanglement entropy. In this work, we show how two dimensionality reduction techniques, multidimensional scaling and intrinsic dimension estimation, can be used to learn structural properties of dynamics in the PXP model and distinguish between thermal and scar initial states. The latter method is shown to be robust against limited sample sizes and experimental measurement errors.

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Quantum self-supervised learning

Jaderberg

Anderson

Xie

et al. 2022

Quantum Sci. Technol.

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The resurgence of self-supervised learning, whereby a deep learning model generates its own supervisory signal from the data, promises a scalable way to tackle the dramatically increasing size of real-world data sets without human annotation. However, the staggering computational complexity of these methods is such that for state-of-the-art performance, classical hardware requirements represent a signiﬁcant bottleneck to further progress. Here we take the ﬁrst steps to understanding whether quantum neural networks could meet the demand for more powerful architectures and test its eﬀectiveness in proof-of-principle hybrid experiments. Interestingly, we observe a numerical advantage for the learning of visual representations using small-scale quantum neural networks over equivalently structured classical networks, even when the quantum circuits are sampled with only 100 shots. Furthermore, we apply our best quantum model to classify unseen images on the ibmq_paris quantum computer and find that current noisy devices can already achieve equal accuracy to the equivalent classical model on downstream tasks.

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Unsupervised detection of decoupled subspaces: Many-body scars and beyond

Cited by 10 publications

References 66 publications

Dynamical Quantum Phase Transitions from Quantum Optics Perspective

Dynamical Quantum Phase Transitions from Quantum Optics Perspective

Unsupervised learning of quantum many-body scars using intrinsic dimension

Quantum self-supervised learning

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