Add to ORKGQuantum learning holds great promise for the field of machine intelli-gence. The most studied quantum learning algorithm is the quantum neural network. Many such models have been proposed, yet none has become a standard. In addition, these models usually leave out many details, often excluding how they intend to train their networks. This pa-per discusses one approach to the problem and what advantages it would have over classical networks.
A quantum computer that is useful in practice, is expected to be developed in the next few years. An...
A quantum computer that is useful in practice, is expected to be developed in the next few years. An...
State of the art and motivations Learning is the process of acquiring, modifying, and recognising kn...
Most proposals for quantum neural networks have skipped over the problem of how to train the networ...
Quantum computing (physically-based computation founded on quantum-theoretic concepts) is gaining pr...
Artificial neural networks are at the heart of modern deep learning algorithms. We describe how to e...
Graph structures are ubiquitous throughout the natural sciences. Here we develop an approach that ex...
Neural networks enjoy widespread success in both research and industry and, with the advent of quant...
It is shown by classical simulation and experimentation that quantum artificial neural networks (QUA...
Artificial neural networks have achieved great success in many fields ranging from image recognition...
A method is proposed for solving the two key problems facing quantum neural networks: introduction o...
Despite its undeniable success, classical machine learning remains a resource-intensive process. Pra...
Quantum computers are next-generation devices that hold promise to perform calculations beyond the r...
A quantum computer that is useful in practice, is expected to be developed in the next few years. An...
A quantum computer that is useful in practice, is expected to be developed in the next few years. An...
A quantum computer that is useful in practice, is expected to be developed in the next few years. An...
A quantum computer that is useful in practice, is expected to be developed in the next few years. An...
State of the art and motivations Learning is the process of acquiring, modifying, and recognising kn...
Most proposals for quantum neural networks have skipped over the problem of how to train the networ...
Quantum computing (physically-based computation founded on quantum-theoretic concepts) is gaining pr...
Artificial neural networks are at the heart of modern deep learning algorithms. We describe how to e...
Graph structures are ubiquitous throughout the natural sciences. Here we develop an approach that ex...
Neural networks enjoy widespread success in both research and industry and, with the advent of quant...
It is shown by classical simulation and experimentation that quantum artificial neural networks (QUA...
Artificial neural networks have achieved great success in many fields ranging from image recognition...
A method is proposed for solving the two key problems facing quantum neural networks: introduction o...
Despite its undeniable success, classical machine learning remains a resource-intensive process. Pra...
Quantum computers are next-generation devices that hold promise to perform calculations beyond the r...
A quantum computer that is useful in practice, is expected to be developed in the next few years. An...
A quantum computer that is useful in practice, is expected to be developed in the next few years. An...
A quantum computer that is useful in practice, is expected to be developed in the next few years. An...
A quantum computer that is useful in practice, is expected to be developed in the next few years. An...
State of the art and motivations Learning is the process of acquiring, modifying, and recognising kn...