Variational learning of quantum ground states on spiking neuromorphic hardware

Klassert, Robert; Baumbach, Andreas; Petrovici, Mihai A.; Gärttner, Martin

doi:10.1016/j.isci.2022.104707

Cited by 3 publications

(1 citation statement)

References 56 publications

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“…However, while classical ASS schemes have substantial practical value, they rely on the computational complexity of classical encryption, making them potentially vulnerable to adversaries with strong computational capabilities, especially the upcoming quantum computers and fast quantum algorithms. 12 , 13 Fortunately, QASS addresses this issue while ensuring information-theoretic security.…”

Section: Introductionmentioning

confidence: 99%

Enhanced quantum secret sharing protocol for anonymous secure communication utilizing W states

Li,

Cheng,

Wang

et al. 2024

iScience

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Section: Introductionmentioning

confidence: 99%

Enhanced quantum secret sharing protocol for anonymous secure communication utilizing W states

Li,

Cheng,

Wang

et al. 2024

iScience

View full text Add to dashboard Cite

Three learning stages and accuracy–efficiency tradeoff of restricted Boltzmann machines

Dabelow

Ueda

2022

Nat Commun

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Restricted Boltzmann Machines (RBMs) offer a versatile architecture for unsupervised machine learning that can in principle approximate any target probability distribution with arbitrary accuracy. However, the RBM model is usually not directly accessible due to its computational complexity, and Markov-chain sampling is invoked to analyze the learned probability distribution. For training and eventual applications, it is thus desirable to have a sampler that is both accurate and efficient. We highlight that these two goals generally compete with each other and cannot be achieved simultaneously. More specifically, we identify and quantitatively characterize three regimes of RBM learning: independent learning, where the accuracy improves without losing efficiency; correlation learning, where higher accuracy entails lower efficiency; and degradation, where both accuracy and efficiency no longer improve or even deteriorate. These findings are based on numerical experiments and heuristic arguments.

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From architectures to applications: a review of neural quantum states

Lange,

Van de Walle,

Abedinnia

et al. 2024

Quantum Sci. Technol.

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Due to the exponential growth of the Hilbert space dimension with system size, the simulation of quantum many-body systems has remained a persistent challenge until today. Here, we review a relatively new class of variational states for the simulation of such systems, namely neural quantum states (NQS), which overcome the exponential scaling by compressing the state in terms of the network parameters rather than storing all exponentially many coefficients needed for an exact parameterization of the state. We introduce the commonly used NQS architectures and their various applications for the simulation of ground and excited states, finite temperature and open system states as well as NQS approaches to simulate the dynamics of quantum states. Furthermore, we discuss NQS in the context of quantum state tomography.

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Variational learning of quantum ground states on spiking neuromorphic hardware

Cited by 3 publications

References 56 publications

Enhanced quantum secret sharing protocol for anonymous secure communication utilizing W states

Enhanced quantum secret sharing protocol for anonymous secure communication utilizing W states

Three learning stages and accuracy–efficiency tradeoff of restricted Boltzmann machines

From architectures to applications: a review of neural quantum states

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