Machine learning outperforms thermodynamics in measuring how well a many-body system learns a drive

Zhong, Weishun; Gold, Jacob; Marzen, Sarah; England, Jeremy L.; Halpern, Nicole Yunger

doi:10.1038/s41598-021-88311-7

Cited by 12 publications

(6 citation statements)

References 21 publications

(22 reference statements)

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“…Moreover, our observation of memory in a quasi-static system poses the challenge of how to extract this memory from measurements on the static particle configuration alone, e.g. by using machine-learning [20] [21].…”

Section: Discussionmentioning

confidence: 99%

Memory in 3D cyclically driven granular material

Benson,

Peshkov,

Richardson

et al. 2020

Preprint

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We perform experimental and numerical studies of a granular system under cyclic-compression to investigate reversibility and memory effects. We focus on the quasi-static forcing of dense systems, which is most relevant to a wide range of geophysical, industrial, and astrophysical problems. We find that soft-sphere simulations with proper stiffness and friction quantitatively reproduce both the translational and rotational displacements of the grains. We then utilize these simulations to demonstrate that such systems are capable of storing the history of previous compressions. While both mean translational and rotational displacements encode such memory, the response is fundamentally different for translations compared to rotations. For translational displacements, this memory of prior forcing depends on the coefficient of static inter-particle friction, but rotational memory is not altered by the level of friction.

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Section: Discussionmentioning

confidence: 99%

Memory in 3D cyclically driven granular material

Benson,

Peshkov,

Richardson

et al. 2020

Preprint

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“…Indeed, recent work in nonequilibrium thermodynamics and on physical and chemical microsystems has been using the language of top-down causation for quite simple systems -much simpler than biological ones-even adopting language that is usually reserved for biology, such as adaptation and the capacity for self-organization (England 2015;Perunov et al 2016;Horowitz and England 2017;Kachman et al 2017;Ropp et al 2018;Kedia et al 2019). Simple physical systems are said to exhibit life-like behavior (Colomer et al 2018;te Brinke et al 2018) and even to show some anticipatory predictive capacity (Zhong et al 2021; M. Jacob, J. M. Gold, and J. L. England unpublished data). In physics, the Prigogine conjecture is alive and well.…”

Section: The Physics Behind Prigogine's Trinomialmentioning

confidence: 99%

Applying the Prigogine view of dissipative systems to the major transitions in evolution

Castro¹,

McShea²

2022

Paleobiology

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Ilya Prigogine's trinomial concept is, he argued, applicable to many complex dissipative systems, from physics to biology and even to social systems. For Prigogine, this trinomial—functions, structure, fluctuations—was intended to capture the feedback-rich relations between upper and lower levels in these systems. The main novelty of his vision was his view of causation, in which the causal arrow runs downward from dissipative structures to their components or functions. Following this insight, some physicists and biophysicists are beginning to apply terms formerly used mainly in biology, such as evolution, adaptation, learning, and life-like behavior, to physical and chemical nonequilibrium systems. Here, instead, we apply Prigogine's view to biology, in particular to evolution, and especially the major transitions in evolution (MTE), arguing that at least the hierarchical transitions—the transitions in individuality—follow a trajectory anticipated by the trinomial. In this trajectory, formerly free-living organisms are transformed into “functions” within a larger organic “structure.” The Prigogine view also predicts that, consistent with available data, the increase in number of hierarchical levels in organisms should accelerate over time. Finally, it predicts that, on geological timescales, ecosystems and Gaia in particular will tend to “de-Darwinize” or “machinify” their component organisms.

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“…Pattern recognition has been implemented in a variety of analog classical systems ranging from molecular self-assembly to elastic networks [55][56][57][58][59][60][61][62] . It is interesting to ask whether quantum systems possesses similar power.…”

Section: A Pattern Recognitionmentioning

confidence: 99%

Many-body localized hidden Born machine

Zhong¹,

Gao²,

Yelin³

et al. 2022

Preprint

Self Cite

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Born Machines are quantum-inspired generative models that leverage the probabilistic nature of quantum states. Here, we present a new architecture called many-body localized (MBL) hidden Born machine that uses both MBL dynamics and hidden units as learning resources. We theoretically prove that MBL Born machines possess more expressive power than classical models, and the introduction of hidden units boosts its learning power. We numerically demonstrate that the MBL hidden Born machine is capable of learning a toy version of MNIST handwritten digits, quantum data obtained from quantum many-body states, and non-local parity data. In order to understand the mechanism behind learning, we track physical quantities such as von Neumann entanglement entropy and Hamming distance during learning, and compare the learning outcomes in the MBL, thermal, and Anderson localized phases. We show that the superior learning power of the MBL phase relies importantly on both localization and interaction. Our architecture and algorithm provide novel strategies of utilizing quantum many-body systems as learning resources, and reveal a powerful connection between disorder, interaction, and learning in quantum many-body systems.

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Machine learning outperforms thermodynamics in measuring how well a many-body system learns a drive

Cited by 12 publications

References 21 publications

Memory in 3D cyclically driven granular material

Memory in 3D cyclically driven granular material

Applying the Prigogine view of dissipative systems to the major transitions in evolution

Many-body localized hidden Born machine

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