Neural network enhanced hybrid quantum many-body dynamical distributions

Koch, Rouven; Lado, José L.

doi:10.1103/physrevresearch.3.033102

Cited by 8 publications

(5 citation statements)

References 65 publications

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“…Thus, we conclude that the quantum oscillator model can reproduce the predictions of the neural network model provided that the outputs of the single and double parabolic potential well oscillators are combined together, which can be achieved, for example, by coupling them into a chain oscillator. While the discussion of an implementation of this approach is beyond the scope of this paper, the similarity of the outputs of the neural network model and the quantum oscillator model has a clear physical meaning: both models are dynamical systems that operate according to the fundamental laws of quantum mechanics [99][100][101].…”

Section: Neural Network Model Versus Quantum Oscillator Modelmentioning

confidence: 99%

Quantum-Inspired Neural Network Model of Optical Illusions

Maksymov

2024

Algorithms

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Ambiguous optical illusions have been a paradigmatic object of fascination, research and inspiration in arts, psychology and video games. However, accurate computational models of perception of ambiguous figures have been elusive. In this paper, we design and train a deep neural network model to simulate human perception of the Necker cube, an ambiguous drawing with several alternating possible interpretations. Defining the weights of the neural network connection using a quantum generator of truly random numbers, in agreement with the emerging concepts of quantum artificial intelligence and quantum cognition, we reveal that the actual perceptual state of the Necker cube is a qubit-like superposition of the two fundamental perceptual states predicted by classical theories. Our results finds applications in video games and virtual reality systems employed for training of astronauts and operators of unmanned aerial vehicles. They are also useful for researchers working in the fields of machine learning and vision, psychology of perception and quantum–mechanical models of human mind and decision making.

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Section: Neural Network Model Versus Quantum Oscillator Modelmentioning

confidence: 99%

Quantum-Inspired Neural Network Model of Optical Illusions

Maksymov

2024

Algorithms

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“…Besides being proposed as ground state estimators for variational calculations [44,[47][48][49], their representation power has been instrumental to a number of other applications, such as the calculation of spectral functions [50][51][52].…”

Section: Restricted Boltzmann Machinesmentioning

confidence: 99%

Neural network representation for minimally entangled typical thermal states

Hendry¹,

Chen²,

Feiguin³

2022

Preprint

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Minimally entangled typical thermal states (METTS) are a construction that allows one to to solve for the imaginary time evolution of quantum many body systems. By using wave functions that are weakly entangled, one can take advantage of efficient representations in the form of matrix product states. We generalize these ideas to arbitrary variational wave functions and we focus, as illustration, on the particular case of restricted Boltzmann machines. The imaginary time evolution is carried out using stochastic reconfiguration (natural gradient descent), combined with Monte Carlo sampling. Since the time evolution takes place on the tangent space, deviations between the actual path in the Hilbert space and the trajectory on the variational manifold can be important, depending on the internal structure and expressivity of the variational states. We show how these differences translate into a rescaled temperature and demonstrate the application of the method to quantum spin systems in one and two spatial dimensions.

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“…This spectral function corresponds to the dynamical spin structure factor for a spin system and the electronic many-body density of states for an electronic system. The dynamical correlator is computed using the tensor-network kernel polynomial algorithm [43][44][45][46][47][48][49][50]. The many-body states and Hamiltonians are represented in terms of a tensor-network, using the matrix-product state formalism [51][52][53], the ground state is computed with the density-matrix renormalization group algorithm [4], and the Hamiltonian is scaled to the interval (−1, 1) to perform the Chebyshev expansion [43].…”

Section: B Dynamical Correlators With Tensor-networkmentioning

confidence: 99%

Designing quantum many-body matter with conditional generative adversarial networks

Koch¹,

Lado²

2022

Preprint

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The computation of dynamical correlators of quantum many-body systems represents an open critical challenge in condensed matter physics. While powerful methodologies have risen in recent years, covering the full parameter space remains unfeasible for most many-body systems with a complex configuration space. Here we demonstrate that conditional Generative Adversarial Networks (GANs) allow simulating the full parameter space of several many-body systems, accounting both for controlled parameters, and stochastic disorder effects. After training with a restricted set of noisy many-body calculations, the conditional GAN algorithm provides the whole dynamical excitation spectra for a Hamiltonian instantly and with an accuracy analogous to the exact calculation. We further demonstrate how the trained conditional GAN automatically provides a powerful method for Hamiltonian learning from its dynamical excitations, and to flag non-physical systems via outlier detection. Our methodology puts forward generative adversarial learning as a powerful technique to explore complex many-body phenomena, providing a starting point to design large-scale quantum many-body matter.

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Neural network enhanced hybrid quantum many-body dynamical distributions

Cited by 8 publications

References 65 publications

Quantum-Inspired Neural Network Model of Optical Illusions

Quantum-Inspired Neural Network Model of Optical Illusions

Neural network representation for minimally entangled typical thermal states

Designing quantum many-body matter with conditional generative adversarial networks

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