22,639 research outputs found
Finding Quantum Critical Points with Neural-Network Quantum States
Finding the precise location of quantum critical points is of particular
importance to characterise quantum many-body systems at zero temperature.
However, quantum many-body systems are notoriously hard to study because the
dimension of their Hilbert space increases exponentially with their size.
Recently, machine learning tools known as neural-network quantum states have
been shown to effectively and efficiently simulate quantum many-body systems.
We present an approach to finding the quantum critical points of the quantum
Ising model using neural-network quantum states, analytically constructed
innate restricted Boltzmann machines, transfer learning and unsupervised
learning. We validate the approach and evaluate its efficiency and
effectiveness in comparison with other traditional approaches.Comment: 19 pages, 12 figures, extended version of an accepted paper at the
24th European Conference on Artificial Intelligence (ECAI 2020
Revealing quantum chaos with machine learning
Understanding properties of quantum matter is an outstanding challenge in
science. In this paper, we demonstrate how machine-learning methods can be
successfully applied for the classification of various regimes in
single-particle and many-body systems. We realize neural network algorithms
that perform a classification between regular and chaotic behavior in quantum
billiard models with remarkably high accuracy. We use the variational
autoencoder for autosupervised classification of regular/chaotic wave
functions, as well as demonstrating that variational autoencoders could be used
as a tool for detection of anomalous quantum states, such as quantum scars. By
taking this method further, we show that machine learning techniques allow us
to pin down the transition from integrability to many-body quantum chaos in
Heisenberg XXZ spin chains. For both cases, we confirm the existence of
universal W shapes that characterize the transition. Our results pave the way
for exploring the power of machine learning tools for revealing exotic
phenomena in quantum many-body systems.Comment: 12 pages, 12 figure
Discriminative Cooperative Networks for Detecting Phase Transitions
The classification of states of matter and their corresponding phase
transitions is a special kind of machine-learning task, where physical data
allow for the analysis of new algorithms, which have not been considered in the
general computer-science setting so far. Here we introduce an unsupervised
machine-learning scheme for detecting phase transitions with a pair of
discriminative cooperative networks (DCN). In this scheme, a guesser network
and a learner network cooperate to detect phase transitions from fully
unlabeled data. The new scheme is efficient enough for dealing with phase
diagrams in two-dimensional parameter spaces, where we can utilize an active
contour model -- the snake -- from computer vision to host the two networks.
The snake, with a DCN "brain", moves and learns actively in the parameter
space, and locates phase boundaries automatically
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