An unsupervised deep learning algorithm for single-site reconstruction in quantum gas microscopes

Impertro, Alexander; Wienand, Julian F.; Häfele, Sophie; Raven, Hendrik von; Hubele, Scott; Klostermann, Till; Cabrera, Cesar R.; Bloch, Immanuel; Aidelsburger, Monika

doi:10.1038/s42005-023-01287-w

Cited by 11 publications

(1 citation statement)

References 45 publications

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“…In strongly correlated systems, complex phases of matter can emerge in seemingly simple models -which, in many settings, still lack microscopic understanding [8,9]. With their powerful abstraction tools, neural networks have quickly opened a novel paradigm of analyzing many-body phases of matter, which may help to gain deeper understanding of appearing phases in strongly correlated systems [2,10,11], as well as act toward experimental image reconstruction [12], enhanced Monte Carlo sampling [13][14][15][16], and efficient representations of quantum states [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Fluctuation based interpretable analysis scheme for quantum many-body snapshots

Schlömer,

Bohrdt

2023

SciPost Phys.

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Microscopically understanding and classifying phases of matter is at the heart of strongly-correlated quantum physics. With quantum simulations, genuine projective measurements (snapshots) of the many-body state can be taken, which include the full information of correlations in the system. The rise of deep neural networks has made it possible to routinely solve abstract processing and classification tasks of large datasets, which can act as a guiding hand for quantum data analysis. However, though proven to be successful in differentiating between different phases of matter, conventional neural networks mostly lack interpretability on a physical footing. Here, we combine confusion learning [Nat. Phys. 13, 435 (2017)] with correlation convolutional neural networks [Nat. Commun. 12, 3905 (2021)], which yields fully interpretable phase detection in terms of correlation functions. In particular, we study thermodynamic properties of the 2D Heisenberg model, whereby the trained network is shown to pick up qualitative changes in the snapshots above and below a characteristic temperature where magnetic correlations become significantly long-range. We identify the full counting statistics of nearest neighbor spin correlations as the most important quantity for the decision process of the neural network, which go beyond averages of local observables. With access to the fluctuations of second-order correlations — which indirectly include contributions from higher order, long-range correlations — the network is able to detect changes of the specific heat and spin susceptibility, the latter being in analogy to magnetic properties of the pseudogap phase in high-temperature superconductors [Phys. Rev. Lett. 62, 957 (1989)]. By combining the confusion learning scheme with transformer neural networks, our work opens new directions in interpretable quantum image processing being sensible to long-range order.

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Section: Introductionmentioning

confidence: 99%

Fluctuation based interpretable analysis scheme for quantum many-body snapshots

Schlömer,

Bohrdt

2023

SciPost Phys.

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Dimensionality Reduction for Data Analysis With Quantum Feature Learning

Sihare

2024

WIREs Data Min & Knowl

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To improve data analysis and feature learning, this study compares the effectiveness of quantum dimensionality reduction (qDR) techniques to classical ones. In this study, we investigate several qDR techniques on a variety of datasets such as quantum Gaussian distribution adaptation (qGDA), quantum principal component analysis (qPCA), quantum linear discriminant analysis (qLDA), and quantum t‐SNE (qt‐SNE). The Olivetti Faces, Wine, Breast Cancer, Digits, and Iris are among the datasets used in this investigation. Through comparison evaluations against well‐established classical approaches, such as classical PCA (cPCA), classical LDA (cLDA), and classical GDA (cGDA), and using well‐established metrics like loss, fidelity, and processing time, the effectiveness of these techniques is assessed. The findings show that cPCA produced positive results with the lowest loss and highest fidelity when used on the Iris dataset. On the other hand, quantum uniform manifold approximation and projection (qUMAP) performs well and shows strong fidelity when tested against the Wine dataset, but ct‐SNE shows mediocre performance against the Digits dataset. Isomap and locally linear embedding (LLE) function differently depending on the dataset. Notably, LLE showed the largest loss and lowest fidelity on the Olivetti Faces dataset. The hypothesis testing findings showed that the qDR strategies did not significantly outperform the classical techniques in terms of maintaining pertinent information from quantum datasets. More specifically, the outcomes of paired t‐tests show that when it comes to the ability to capture complex patterns, there are no statistically significant differences between the cPCA and qPCA, the cLDA and qLDA, and the cGDA and qGDA. According to the findings of the assessments of mutual information (MI) and clustering accuracy, qPCA may be able to recognize patterns more clearly than standardized cPCA. Nevertheless, there is no discernible improvement between the qLDA and qGDA approaches and their classical counterparts.

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Emergence of fluctuating hydrodynamics in chaotic quantum systems

Wienand,

Karch,

Impertro

et al. 2024

Nat. Phys.

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A fundamental principle of chaotic quantum dynamics is that local subsystems eventually approach a thermal equilibrium state. The corresponding timescales increase with subsystem size as equilibration is limited by the hydrodynamic build-up of fluctuations on extended length scales. We perform large-scale quantum simulations that monitor particle-number fluctuations in tunable ladders of hard-core bosons and explore how the build-up of fluctuations changes as the system crosses over from integrable to fully chaotic dynamics. Our results indicate that the growth of large-scale fluctuations in chaotic, far-from-equilibrium systems is quantitatively determined by equilibrium transport coefficients, in agreement with the predictions of fluctuating hydrodynamics. This emergent hydrodynamic behaviour of subsystem fluctuations provides a test of fluctuation–dissipation relations far from equilibrium and allows the accurate determination of equilibrium transport coefficients using far-from-equilibrium quantum dynamics.

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An unsupervised deep learning algorithm for single-site reconstruction in quantum gas microscopes

Cited by 11 publications

References 45 publications

Fluctuation based interpretable analysis scheme for quantum many-body snapshots

Fluctuation based interpretable analysis scheme for quantum many-body snapshots

Dimensionality Reduction for Data Analysis With Quantum Feature Learning

Emergence of fluctuating hydrodynamics in chaotic quantum systems

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