Generation of ice states through deep reinforcement learning

Zhao, Kai-Wen; Kao, Wen-Han; Wu, Kai-Hsin; Kao, Ying-Jer

doi:10.1103/physreve.99.062106

Cited by 13 publications

(8 citation statements)

References 46 publications

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“…While an supervised approach is immediately successful at distinguishing the high and low temperature phases [14], unsupervised approaches did not succeed without an explicit recipe what type of restriction to look at. There has been significant progress in this direction, but a fully general approach is yet to be found [17][18][19][20][21][22][23][24]. While methods like principal component analysis, clustering and variational auto-encoders have proven to be successful to determine the phase transitions in spin models possessing an order parameter [25], systems without order parameters still represent a challenge.…”

Section: The Ising Gauge Theorymentioning

confidence: 99%

“…In figure 2 we show how the difference between the true and predicted inverse temperatures β pred −β label behaves as a function of the true β label for seven different system sizes N=4, 8,12,16,20,24,28 (the total number of spins is 2N 2 ). We see that the behavior of the prediction is not uniform for all inputs and, in fact, we observe that for all systems sizes there exists a different finiteb above which the network has difficulties to identify the correct β label .…”

Section: The Ising Gauge Theorymentioning

confidence: 99%

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Unsupervised identification of topological phase transitions using predictive models

et al. 2020

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Machine-learning driven models have proven to be powerful tools for the identification of phases of matter. In particular, unsupervised methods hold the promise to help discover new phases of matter without the need for any prior theoretical knowledge. While for phases characterized by a broken symmetry, the use of unsupervised methods has proven to be successful, topological phases without a local order parameter seem to be much harder to identify without supervision. Here, we use an unsupervised approach to identify boundaries of the topological phases. We train artificial neural nets to relate configurational data or measurement outcomes to quantities like temperature or tuning parameters in the Hamiltonian. The accuracy of these predictive models can then serve as an indicator for phase transitions. We successfully illustrate this approach on both the classical Ising gauge theory as well as on the quantum ground state of a generalized toric code.

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Section: The Ising Gauge Theorymentioning

confidence: 99%

Section: The Ising Gauge Theorymentioning

confidence: 99%

Unsupervised identification of topological phase transitions using predictive models

et al. 2020

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“…Deep reinforcement learning has also been used in conjunction with MC simulations [186]. Zhao et al develop a deep reinforcement learning framework where a machine agent is trained to search for a policy to generate ground states for the square ice model [187], which belongs to the family of ice models used to describe the statistical properties of the hydrogen atoms in water ice.…”

Section: Machine Learning Acceleration Of Monte Carlo Simulationsmentioning

confidence: 99%

Machine learning for quantum matter

Carrasquilla

2020

Advances in Physics: X

178

111

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Quantum matter, the research field studying phases of matter whose properties are intrinsically quantum mechanical, draws from areas as diverse as hard condensed matter physics, materials science, statistical mechanics, quantum information, quantum gravity, and large-scale numerical simulations. Recently, researchers interested in quantum matter and strongly correlated quantum systems have turned their attention to the algorithms underlying modern machine learning with an eye on making progress in their fields. Here we provide a short review on the recent development and adaptation of machine learning ideas for the purpose advancing research in quantum matter, including ideas ranging from algorithms that recognize conventional and topological states of matter in synthetic experimental data, to representations of quantum states in terms of neural networks and their applications to the simulation and control of quantum systems. We discuss the outlook for future developments in areas at the intersection between machine learning and quantum many-body physics.

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“…Another approach is 'self-learning Monte Carlo' [18][19][20][21] that, in principle, works for any generic system and applies machinelearning-based approaches on top of MCMC to speed up the simulations and to reduce the increase in auto-correlation time near the critical temperature. Other approaches which apply machine-learning techniques as a supplement or alternative to MCMC are based on normalizing flow [22], Boltzmann machines [23][24][25][26], on reinforcement learning [27], on generative adversarial networks (GANs) [28][29][30][31][32][33], autoencoders [34][35][36], and on variational autoregressive networks [37][38][39][40].…”

Section: Introductionmentioning

confidence: 99%

Conditional generative models for sampling and phase transition indication in spin systems

2021

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In this work, we study generative adversarial networks (GANs) as a tool to learn the distribution of spin configurations and to generate samples, conditioned on external tuning parameters or other quantities associated with individual configurations. For concreteness, we focus on two examples of conditional variables---the temperature of the system and the energy of the samples. We show that temperature-conditioned models can not only be used to generate samples across thermal phase transitions, but also be employed as unsupervised indicators of transitions. To this end, we introduce a GAN-fidelity measure that captures the model’s susceptibility to external changes of parameters. The proposed energy-conditioned models are integrated with Monte Carlo simulations to perform over-relaxation steps, which break the Markov chain and reduce auto-correlations. We propose ways of efficiently representing the physical states in our network architectures, e.g., by exploiting symmetries, and to minimize the correlations between generated samples. A detailed evaluation, using the two-dimensional XY model as an example, shows that these incorporations bring in considerable improvements over standard machine-learning approaches. We further study the performance of our architectures when no training data is provided near the critical region.

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Generation of ice states through deep reinforcement learning

Cited by 13 publications

References 46 publications

Unsupervised identification of topological phase transitions using predictive models

Unsupervised identification of topological phase transitions using predictive models

Machine learning for quantum matter

Conditional generative models for sampling and phase transition indication in spin systems

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