Recent developments in quantum machine learning have seen the introduction of several models to generalize the classical perceptron to the quantum regime. The capabilities of these quantum models need to be determined precisely in order to establish if a quantum advantage is achievable. Here we use a statistical physics approach to compute the pattern capacity of a particular model of quantum perceptron realized by means of a continuous variable quantum system
Quantum computing represents a promising paradigm for solving complex problems, such as large-number...
International audienceQuantum machine learning algorithms could provide significant speed-ups over t...
Machine learning algorithms based on parametrized quantum circuits are prime candidates for near-ter...
Recent developments in quantum machine learning have seen the introduction of several models to gene...
Recent developments in quantum machine learning have seen the introduction of several models to gene...
Recent developments in Quantum Machine Learning have seen the introduction of several models to gene...
Quantum machine learning algorithms could provide significant speed-ups over their classical counter...
Quantum neural networks form one pillar of the emergent field of quantum machine learning. Here quan...
Quantum neural networks form one pillar of the emergent field of quantum machine learning. Here quan...
Quantum computing represents a promising paradigm for solving complex problems, such as large-number...
International audienceQuantum machine learning algorithms could provide significant speed-ups over t...
International audienceQuantum machine learning algorithms could provide significant speed-ups over t...
The goal of generative machine learning is to model the probability distribution underlying a given ...
Quantum computing represents a promising paradigm for solving complex problems, such as large-number...
International audienceQuantum machine learning algorithms could provide significant speed-ups over t...
Quantum computing represents a promising paradigm for solving complex problems, such as large-number...
International audienceQuantum machine learning algorithms could provide significant speed-ups over t...
Machine learning algorithms based on parametrized quantum circuits are prime candidates for near-ter...
Recent developments in quantum machine learning have seen the introduction of several models to gene...
Recent developments in quantum machine learning have seen the introduction of several models to gene...
Recent developments in Quantum Machine Learning have seen the introduction of several models to gene...
Quantum machine learning algorithms could provide significant speed-ups over their classical counter...
Quantum neural networks form one pillar of the emergent field of quantum machine learning. Here quan...
Quantum neural networks form one pillar of the emergent field of quantum machine learning. Here quan...
Quantum computing represents a promising paradigm for solving complex problems, such as large-number...
International audienceQuantum machine learning algorithms could provide significant speed-ups over t...
International audienceQuantum machine learning algorithms could provide significant speed-ups over t...
The goal of generative machine learning is to model the probability distribution underlying a given ...
Quantum computing represents a promising paradigm for solving complex problems, such as large-number...
International audienceQuantum machine learning algorithms could provide significant speed-ups over t...
Quantum computing represents a promising paradigm for solving complex problems, such as large-number...
International audienceQuantum machine learning algorithms could provide significant speed-ups over t...
Machine learning algorithms based on parametrized quantum circuits are prime candidates for near-ter...
DOI
Add to ORKG