Recurrent neural network wave functions

Hibat-Allah, Mohamed; Ganahl, Martin; Sierens, Lauren E. Hayward; Melko, Roger G.; Carrasquilla, Juan

doi:10.1103/physrevresearch.2.023358

Cited by 208 publications

(215 citation statements)

References 46 publications

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“…In this section, various HVAC devices are used to find the effectiveness of the proposed method that includes Heating and Cooling Split Systems, Hybrid Split System, Duct Free (Mini-Split), Packaged Heating and Air, Window Through-the-Wall Air Conditioner, Packaged Terminal Heat Pumps, Unit Heaters, Packaged Rooftop Unit, Packaged Rooftop Heat Pump and Humidifiers. The proposed method is compared with other existing methods that include RNN [43][44][45][46], ANN [39,46] and CNN [47][48][49][50] algorithms and with other communications protocols, including Li-Fi, Wi-Fi, Zigbee and Bluetooth. The datasets are collected from large size units, as in [51], and the simulations are conducted in a python anaconda complier with 16GB RAM usage requirements.…”

Section: Resultsmentioning

confidence: 99%

Forecasting of Energy Demands for Smart Home Applications

Bhatt

Danalakshmi²,

Hariharasudan

et al. 2021

Energies

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The utilization of energy is on the rise in current trends due to increasing consumptions by households. Smart buildings, on the other hand, aim to optimize energy, and hence, the aim of the study is to forecast the cost of energy consumption in smart buildings by effectively addressing the minimal energy consumption. However, smart buildings are restricted, with limited power access and capacity associated with Heating, Ventilation and Air Conditioning (HVAC) units. It further suffers from low communication capability due to device limitations. In this paper, a balanced deep learning architecture is used to offer solutions to address these constraints. The deep learning algorithm considers three constraints, such as a multi-objective optimization problem and a fitness function, to resolve the price management problem and high-level energy consumption in HVAC systems. The study analyzes and optimizes the consumption of power in smart buildings by the HVAC systems in terms of power loss, price management and reactive power. Experiments are conducted over various scenarios to check the integrity of the system over various smart buildings and in high-rise buildings. The results are compared in terms of various HVAC devices on various metrics and communication protocols, where the proposed system is considered more effective than other methods. The results of the Li-Fi communication protocols show improved results compared to the other communication protocols.

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Section: Resultsmentioning

confidence: 99%

Forecasting of Energy Demands for Smart Home Applications

Bhatt

Danalakshmi²,

Hariharasudan

et al. 2021

Energies

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“…Wavefunctions and other neural network representation of the quantum state based on autoregressive models allow for uncorrelated sampling from the wavefunction, unlike traditional variational Monte Carlo methods [159], where an expensive Markov chain introduces potential biases in the calculations of observables and during the optimization of the quantum state. Examples of this include a recurrent neural network representation of the quantum state based on generalized measurements [141], as well as neural autoregressive quantum states [160] and a recurrent neural network wavefunctions [161,162], both of which produce state-of-the-art approximations to the ground states of prototypical models in condensed matter physics.…”

Section: Hidden Layermentioning

confidence: 99%

Machine learning for quantum matter

Carrasquilla

2020

Advances in Physics: X

Self Cite

172

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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“…This is based on the introduction of ML methods to fulfill tasks beyond the scope for which they were originally designed. These include finding phase transitions [1][2][3][4][5][6][7][8][9][10][11][12], simulating quantum systems [13][14][15][16][17][18][19], and rediscovering physical concepts [20][21][22][23][24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Discovering symmetry invariants and conserved quantities by interpreting siamese neural networks

Wetzel

Melko

Scott

et al. 2020

Phys. Rev. Research

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We introduce interpretable siamese neural networks (SNNs) for similarity detection to the field of theoretical physics. More precisely, we apply SNNs to events in special relativity, the transformation of electromagnetic fields, and the motion of particles in a central potential. In these examples, SNNs learn to identify data points belonging to the same event, field configuration, or trajectory of motion. We demonstrate that in the process of learning which data points belong to the same event or field configuration, these SNNs also learn the relevant symmetry invariants and conserved quantities. Such SNNs are highly interpretable, which enables us to reveal the symmetry invariants and conserved quantities without prior knowledge.

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Recurrent neural network wave functions

Cited by 208 publications

References 46 publications

Forecasting of Energy Demands for Smart Home Applications

Forecasting of Energy Demands for Smart Home Applications

Machine learning for quantum matter

Discovering symmetry invariants and conserved quantities by interpreting siamese neural networks

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