Figure illustrating the architecture of the quantum convolutional neural networks developed by the researchers. Credit: Cong, Choi & Lukin.

WiMi's quantum algorithm provides exponential speedup in both stages, enabling neural networks to achieve convergence in significantly less time.

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 ...

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 ...

MicroAlgo's Quantum Neural Network-based intelligent search system follows a sophisticated process framework, ensuring effective data filtering and efficient processing.

AlphaQubit addresses this issue using transformer-based neural networks, a model architecture that powers advanced AI systems like large language models.

This simply means that neural networks running on quantum systems could, potentially, be exponentially more robust than those running on classical systems.