We present an algorithm of quantum restricted Boltzmann machine network based on quantum gates. The algorithm is used to initialize the procedure that adjusts the qubit and weights. After adjusting, the network forms an unsupervised generative model that gives better classification performance than other discriminative models. In addition, we show how the algorithm can be constructed with quantum circuit for quantum computer
A method is proposed for solving the two key problems facing quantum neural networks: introduction o...
A quantum-implementable neural network, namely quantum probability neural network (QPNN) model, is p...
Restricted Boltzmann machines are common machine learning models that can utilize quantum annealing ...
Quantum Computing is a novel technology that has wide applicability in the field of machine learning...
Quantum computers promise the ability to solve many types of difficult computational problems effici...
Inspired by the success of Boltzmann machines based on classical Boltzmann distribution, we propose ...
International audienceWe propose a neural-network variational quantum algorithm to simulate the time...
AbstractThis paper initiates the study of quantum computing within the constraints of using a polylo...
Quantum computers have become reality thanks to the effort of some majors in developing innovative t...
In this paper, we develop a novel classification algorithm that is based on the integration between ...
The goal of generative machine learning is to model the probability distribution underlying a given ...
The promise of quantum neural nets, which utilize quantum effects to model complex data sets, has ma...
Quantum machine learning has the potential to overcome problems that current classical machine learn...
Artificial neural netwokrs have proven to be a powerful tool with applications in various fields: fr...
Quantum machine learning has the potential to overcome problems that current classical machine learn...
A method is proposed for solving the two key problems facing quantum neural networks: introduction o...
A quantum-implementable neural network, namely quantum probability neural network (QPNN) model, is p...
Restricted Boltzmann machines are common machine learning models that can utilize quantum annealing ...
Quantum Computing is a novel technology that has wide applicability in the field of machine learning...
Quantum computers promise the ability to solve many types of difficult computational problems effici...
Inspired by the success of Boltzmann machines based on classical Boltzmann distribution, we propose ...
International audienceWe propose a neural-network variational quantum algorithm to simulate the time...
AbstractThis paper initiates the study of quantum computing within the constraints of using a polylo...
Quantum computers have become reality thanks to the effort of some majors in developing innovative t...
In this paper, we develop a novel classification algorithm that is based on the integration between ...
The goal of generative machine learning is to model the probability distribution underlying a given ...
The promise of quantum neural nets, which utilize quantum effects to model complex data sets, has ma...
Quantum machine learning has the potential to overcome problems that current classical machine learn...
Artificial neural netwokrs have proven to be a powerful tool with applications in various fields: fr...
Quantum machine learning has the potential to overcome problems that current classical machine learn...
A method is proposed for solving the two key problems facing quantum neural networks: introduction o...
A quantum-implementable neural network, namely quantum probability neural network (QPNN) model, is p...
Restricted Boltzmann machines are common machine learning models that can utilize quantum annealing ...