Quantum machine learning is a highly promising application for quantum computing. The hybrid quantum-classical convolutional neural networks (QCCNN) employs parameter quantum circuit to enhance ...

Quantum resources for artificial intelligence Memristors are thought to be valuable in neural networks, which typically require large amounts of training data to operate effectively. An architecture ...

Neural networks revolutionized machine learning for classical computers: self-driving cars, language translation and even artificial intelligence software were all made possible. It is no wonder ...

To build a large-scale quantum computer that works, scientists and engineers need to overcome the spontaneous errors that quantum bits, or qubits, create as they operate.

We introduce a variational representation of quantum states based on artificial neural networks with a variable number of hidden neurons. A reinforcement-learning scheme we demonstrate is capable of ...

For example, when using the quantum processor to reconstruct lightning data, they found it did a better job at lower altitudes but was generally comparable to the classical neural network.

The practical assessment of quantum computing by tech leaders requires knowledge of how quantum computing differs from classical computing systems.

However, in new research I have used a phenomenon called “quantum tunnelling” to design a neural network that can “see” optical illusions in much the same way humans do.