Add to ORKGThe training of neural networks (NNs) is a computationally intensive task requiring significant time and resources. This paper presents a novel approach to NN training using Adiabatic Quantum Computing (AQC), a paradigm that leverages the principles of adiabatic evolution to solve optimisation problems. We propose a universal AQC method that can be implemented on gate quantum computers, allowing for a broad range of Hamiltonians and thus enabling the training of expressive neural networks. We apply this approach to various neural networks with continuous, discrete, and binary weights. Our results indicate that AQC can very efficiently find the global minimum of the loss function, offering a promising alternative to classical training method...
Neural Networks (NN) are known to be unversal approximators for any non-linear function. Training al...
The exponential run time of quantum simulators on classical machines and long queue depths and high ...
Accurate molecular force fields are of paramount importance for the efficient implementation of mole...
The training of neural networks (NNs) is a computationally intensive task requiring significant time...
The training of neural networks (NNs) is a computationally intensive task requiring significant time...
The universality of a quantum neural network refers to its ability to approximate arbitrary function...
Quantum machine learning techniques have been proposed as a way to potentially enhance performance i...
With a surge in popularity of machine learning as a whole, many researchers have sought optimization...
We introduce a general framework called neural network (NN) encoded variational quantum algorithms (...
Prime factorization is a difficult problem with classical computing, whose exponential hardness is t...
Quantum machine learning has become an area of growing interest but has certain theoretical and hard...
Neural networks enjoy widespread success in both research and industry and, with the advent of quant...
Artificial neural networks are at the heart of modern deep learning algorithms. We describe how to e...
The utility of classical neural networks as universal approximators suggests that their quantum anal...
In order to solve the problem of non-ideal training sets (i.e., the less-complete or over-complete s...
Neural Networks (NN) are known to be unversal approximators for any non-linear function. Training al...
The exponential run time of quantum simulators on classical machines and long queue depths and high ...
Accurate molecular force fields are of paramount importance for the efficient implementation of mole...
The training of neural networks (NNs) is a computationally intensive task requiring significant time...
The training of neural networks (NNs) is a computationally intensive task requiring significant time...
The universality of a quantum neural network refers to its ability to approximate arbitrary function...
Quantum machine learning techniques have been proposed as a way to potentially enhance performance i...
With a surge in popularity of machine learning as a whole, many researchers have sought optimization...
We introduce a general framework called neural network (NN) encoded variational quantum algorithms (...
Prime factorization is a difficult problem with classical computing, whose exponential hardness is t...
Quantum machine learning has become an area of growing interest but has certain theoretical and hard...
Neural networks enjoy widespread success in both research and industry and, with the advent of quant...
Artificial neural networks are at the heart of modern deep learning algorithms. We describe how to e...
The utility of classical neural networks as universal approximators suggests that their quantum anal...
In order to solve the problem of non-ideal training sets (i.e., the less-complete or over-complete s...
Neural Networks (NN) are known to be unversal approximators for any non-linear function. Training al...
The exponential run time of quantum simulators on classical machines and long queue depths and high ...
Accurate molecular force fields are of paramount importance for the efficient implementation of mole...