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Found 25 papers, 13 papers with code
Could ChatGPT get an Engineering Degree? Evaluating Higher Education Vulnerability to AI Assistants
We investigate the potential scale of this vulnerability by measuring the degree to which AI assistants can complete assessment questions in standard university-level STEM courses.
Ab-initio variational wave functions for the time-dependent many-electron Schrödinger equation
Understanding the real-time evolution of many-electron quantum systems is essential for studying dynamical properties in condensed matter, quantum chemistry, and complex materials, yet it poses a significant theoretical and computational challenge.
Hybrid Ground-State Quantum Algorithms based on Neural Schrödinger Forging
Entanglement forging based variational algorithms leverage the bi-partition of quantum systems for addressing ground state problems.
Empirical Sample Complexity of Neural Network Mixed State Reconstruction
Quantum state reconstruction using Neural Quantum States has been proposed as a viable tool to reduce quantum shot complexity in practical applications, and its advantage over competing techniques has been shown in numerical experiments focusing mainly on the noiseless case.
Learning ground states of gapped quantum Hamiltonians with Kernel Methods
Neural network approaches to approximate the ground state of quantum hamiltonians require the numerical solution of a highly nonlinear optimization problem.
Ab-initio quantum chemistry with neural-network wavefunctions
Machine learning and specifically deep-learning methods have outperformed human capabilities in many pattern recognition and data processing problems, in game playing, and now also play an increasingly important role in scientific discovery.
Positive-definite parametrization of mixed quantum states with deep neural networks
We introduce the Gram-Hadamard Density Operator (GHDO), a new deep neural-network architecture that can encode positive semi-definite density operators of exponential rank with polynomial resources.
From Tensor Network Quantum States to Tensorial Recurrent Neural Networks
We show that any matrix product state (MPS) can be exactly represented by a recurrent neural network (RNN) with a linear memory update.
NetKet 3: Machine Learning Toolbox for Many-Body Quantum Systems
We introduce version 3 of NetKet, the machine learning toolbox for many-body quantum physics.
Continuous-variable neural-network quantum states and the quantum rotor model
We initiate the study of neural-network quantum state algorithms for analyzing continuous-variable lattice quantum systems in first quantization.
Unbiased Monte Carlo Cluster Updates with Autoregressive Neural Networks
Efficient sampling of complex high-dimensional probability distributions is a central task in computational science.
Neural tensor contractions and the expressive power of deep neural quantum states
We establish a direct connection between general tensor networks and deep feed-forward artificial neural networks.
An efficient quantum algorithm for the time evolution of parameterized circuits
Our approach is efficient in the sense that it exhibits an optimal linear scaling with the total number of variational parameters.
Quantum Physics Other Condensed Matter Computational Physics
Gauge equivariant neural networks for quantum lattice gauge theories
Gauge symmetries play a key role in physics appearing in areas such as quantum field theories of the fundamental particles and emergent degrees of freedom in quantum materials.
Phases of two-dimensional spinless lattice fermions with first-quantized deep neural-network quantum states
First-quantized deep neural network techniques are developed for analyzing strongly coupled fermionic systems on the lattice.
Natural evolution strategies and variational Monte Carlo
A notion of quantum natural evolution strategies is introduced, which provides a geometric synthesis of a number of known quantum/classical algorithms for performing classical black-box optimization.
Precise measurement of quantum observables with neural-network estimators
The measurement precision of modern quantum simulators is intrinsically constrained by the limited set of measurements that can be efficiently implemented on hardware.
Quantum Physics Disordered Systems and Neural Networks Strongly Correlated Electrons
Fermionic neural-network states for ab-initio electronic structure
Neural-network quantum states have been successfully used to study a variety of lattice and continuous-space problems.
Computational Physics Disordered Systems and Neural Networks Strongly Correlated Electrons Quantum Physics
Quantum Natural Gradient
A quantum generalization of Natural Gradient Descent is presented as part of a general-purpose optimization framework for variational quantum circuits.
NetKet: A Machine Learning Toolkit for Many-Body Quantum Systems
We introduce NetKet, a comprehensive open source framework for the study of many-body quantum systems using machine learning techniques.
Quantum Physics Disordered Systems and Neural Networks Strongly Correlated Electrons Computational Physics Data Analysis, Statistics and Probability
Machine learning and the physical sciences
Machine learning encompasses a broad range of algorithms and modeling tools used for a vast array of data processing tasks, which has entered most scientific disciplines in recent years.
Computational Physics Cosmology and Nongalactic Astrophysics Disordered Systems and Neural Networks High Energy Physics - Theory Quantum Physics
Deep autoregressive models for the efficient variational simulation of many-body quantum systems
Artificial Neural Networks were recently shown to be an efficient representation of highly-entangled many-body quantum states.
Constructing exact representations of quantum many-body systems with deep neural networks
It is based on the deep Boltzmann machine architecture, in which two layers of hidden neurons mediate quantum correlations among physical degrees of freedom in the visible layer.
Disordered Systems and Neural Networks Statistical Mechanics Strongly Correlated Electrons Computational Physics Quantum Physics
Learning hard quantum distributions with variational autoencoders
This suggests that the probability distributions associated to hard quantum states might have a compositional structure that can be exploited by layered neural networks.
Solving the Quantum Many-Body Problem with Artificial Neural Networks
The challenge posed by the many-body problem in quantum physics originates from the difficulty of describing the non-trivial correlations encoded in the exponential complexity of the many-body wave function.
Disordered Systems and Neural Networks Quantum Gases Quantum Physics
