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 ...

Artificial intelligence is now part of our daily lives, with the subsequent pressing need for larger, more complex models.

A neural network has learnt to correct the errors that arise during quantum computation, outperforming algorithms that were designed by humans. The strategy sets out a promising path towards ...

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 ...

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.

Given that neuromorphic computing is inspired by biological systems, deep neural networks (DNN) for machine learning is one of the application areas being targeted.