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Found 14 papers, 4 papers with code
Quantum Natural Gradient
A quantum generalization of Natural Gradient Descent is presented as part of a general-purpose optimization framework for variational quantum circuits.
Fisher-Rao Metric, Geometry, and Complexity of Neural Networks
We study the relationship between geometry and capacity measures for deep neural networks from an invariance viewpoint.
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.
Interaction Matters: A Note on Non-asymptotic Local Convergence of Generative Adversarial Networks
Motivated by the pursuit of a systematic computational and algorithmic understanding of Generative Adversarial Networks (GANs), we present a simple yet unified non-asymptotic local convergence theory for smooth two-player games, which subsumes several discrete-time gradient-based saddle point dynamics.
Probabilistic Modeling with Matrix Product States
Inspired by the possibility that generative models based on quantum circuits can provide a useful inductive bias for sequence modeling tasks, we propose an efficient training algorithm for a subset of classically simulable quantum circuit models.
Mean-field Analysis of Batch Normalization
Batch Normalization (BatchNorm) is an extremely useful component of modern neural network architectures, enabling optimization using higher learning rates and achieving faster convergence.
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.
Meta Variational Monte Carlo
An identification is found between meta-learning and the problem of determining the ground state of a randomly generated Hamiltonian drawn from a known ensemble.
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.
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.
Numerical and geometrical aspects of flow-based variational quantum Monte Carlo
This article aims to summarize recent and ongoing efforts to simulate continuous-variable quantum systems using flow-based variational quantum Monte Carlo techniques, focusing for pedagogical purposes on the example of bosons in the field amplitude (quadrature) basis.
Scalable neural quantum states architecture for quantum chemistry
Variational optimization of neural-network representations of quantum states has been successfully applied to solve interacting fermionic problems.
Gauge Equivariant Neural Networks for 2+1D U(1) Gauge Theory Simulations in Hamiltonian Formulation
Gauge Theory plays a crucial role in many areas in science, including high energy physics, condensed matter physics and quantum information science.
Toward Neural Network Simulation of Variational Quantum Algorithms
Variational quantum algorithms (VQAs) utilize a hybrid quantum-classical architecture to recast problems of high-dimensional linear algebra as ones of stochastic optimization.
