Operationally meaningful representations of physical systems in neural networks

Nautrup, Hendrik Poulsen; Metger, Tony; Iten, Raban; Jerbi, Sofiène; Trenkwalder, Lea M.; Wilming, Henrik; Briegel, Hans J.; Renner, Renato

doi:10.48550/arxiv.2001.00593

Cited by 6 publications

(17 citation statements)

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“…Representation learning is an area of machine learning devoted to turning high-dimensional data into more tractable representations [33][34][35]; however, limitations to these techniques make it difficult to turn the representations into meaningful physical parameters. Steps have been taken towards interpretability techniques that facilitate knowledge extraction about the physics of a given problem, using modified variational auto-encoders [36,37]; these methods have so far only been applied to problems with already known representations in physics.…”

Section: Introductionmentioning

confidence: 99%

Machine learning cosmological structure formation

Lucie-Smith

Peiris

Pontzen

et al. 2018

Monthly Notices of the Royal Astronomical Society

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We train a machine learning algorithm to learn cosmological structure formation from N-body simulations. The algorithm infers the relationship between the initial conditions and the final dark matter haloes, without the need to introduce approximate halo collapse models. We gain insights into the physics driving halo formation by evaluating the predictive performance of the algorithm when provided with different types of information about the local environment around dark matter particles. The algorithm learns to predict whether or not dark matter particles will end up in haloes of a given mass range, based on spherical overdensities. We show that the resulting predictions match those of spherical collapse approximations such as extended Press-Schechter theory. Additional information on the shape of the local gravitational potential is not able to improve halo collapse predictions; the linear density field contains sufficient information for the algorithm to also reproduce ellipsoidal collapse predictions based on the Sheth-Tormen model. We investigate the algorithm's performance in terms of halo mass and radial position and perform blind analyses on independent initial conditions realisations to demonstrate the generality of our results.

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Section: Introductionmentioning

confidence: 99%

Machine learning cosmological structure formation

Lucie-Smith

Peiris

Pontzen

et al. 2018

Monthly Notices of the Royal Astronomical Society

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“…The same concept could help to identify control unitaries for continuous variable quantum computation [31] or serve as an alternative to transfer learning in quantum neural network states [32]. The neural network finds patterns, which may be useful to identify phase transitions in quantum control [5], as well as identify physical concepts in the way the protocols create quantum states [33,34]. The resulting classification of protocols also opens up the potential of using reinforcement learning in identifying phase transitions in physical systems [35][36][37].…”

Section: Discussionmentioning

confidence: 99%

Classifying global state preparation via deep reinforcement learning

Haug,

Mok,

You

et al. 2020

Preprint

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Quantum information processing often requires the preparation of arbitrary quantum states, such as all the states on the Bloch sphere for two-level systems. While numerical optimization can prepare individual target states, they lack the ability to find general solutions that work for a large class of states in more complicated quantum systems. Here, we demonstrate global quantum control by preparing a continuous set of states with deep reinforcement learning. The protocols are represented using neural networks, which automatically groups the protocols into similar types, which could be useful for finding classes of protocols and extracting physical insights. As application, we generate arbitrary superposition states for the electron spin in complex multi-level nitrogen-vacancy centers, revealing classes of protocols characterized by specific preparation timescales. Our method could help improve control of near-term quantum computers, quantum sensing devices and quantum simulations.

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“…This could be very helpful for humans trying to understand and learn new concepts and design rules from the discovered solutions. One example could be interpretable neural networks [137,138] in the physical context [139][140][141] that are applied on deep generative models for complex scientific struc-tures such as functional molecules or quantum experiments [134].…”

Section: Where To Go From Here?mentioning

confidence: 99%

Computer-inspired Quantum Experiments

Krenn,

Erhard,

Zeilinger

2020

Preprint

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The design of new devices and experiments in science and engineering has historically relied on the intuitions of human experts. This credo, however, has changed. In many disciplines, computerinspired design processes, also known as inverse-design, have augmented the capability of scientists.Here we visit different fields of physics in which computer-inspired designs are applied. We will meet vastly diverse computational approaches based on topological optimization, evolutionary strategies, deep learning, reinforcement learning or automated reasoning. Then we draw our attention specifically on quantum physics. In the quest for designing new quantum experiments, we face two challenges: First, quantum phenomena are unintuitive. Second, the number of possible configurations of quantum experiments explodes combinatorially. To overcome these challenges, physicists began to use algorithms for computer-designed quantum experiments. We focus on the most mature and practical approaches that scientists used to find new complex quantum experiments, which experimentalists subsequently have realized in the laboratories. The underlying idea is a highlyefficient topological search, which allows for scientific interpretability. In that way, some of the computer-designs have led to the discovery of new scientific concepts and ideas -demonstrating how computer algorithm can genuinely contribute to science by providing unexpected inspirations. We discuss several extensions and alternatives based on optimization and machine learning techniques, with the potential of accelerating the discovery of practical computer-inspired experiments or concepts in the future. Finally, we discuss what we can learn from the different approaches in the fields of physics, and raise several fascinating possibilities for future research.

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Operationally meaningful representations of physical systems in neural networks

Cited by 6 publications

References 0 publications

Machine learning cosmological structure formation

Machine learning cosmological structure formation

Classifying global state preparation via deep reinforcement learning

Computer-inspired Quantum Experiments

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