In conclusion, tensor networks offer a breakthrough in addressing the efficiency-interpretability challenge in artificial intelligence, especially in quantum-inspired machine learning.

According to Franco Nori, "Machine learning can play a pivotal role in addressing large-scale quantum computation and optimization challenges.

A device made from superconducting qubits could prove a powerful technology for enabling practical quantum computing or more ...

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 ...

What Makes Quantum Machine Learning Different QML isn’t just about running existing AI models on faster hardware. It’s about reimagining how learning itself happens.

Quantum machine learning (QML) combines quantum computing's power with ML's predictive capabilities to overcome classical algorithms' limitations.

This leads to improvements in complex-system simulations or tasks that involve multiple variables in quantum machine learning, making it ideal for applications across various scientific disciplines.

Traditional large-scale machine learning model training processes are often associated with massive energy consumption and carbon emissions, while quantum algorithms are expected to reduce energy ...

Vice President of AI & Quantum Computing, Paul Smith-Goodson gives his analysis of quantum machine learning models and IonQ's strategy to make it a reality.

Scientists in Australia have developed a quantum machine learning technique — a blend of artificial intelligence (AI) and quantum computing principles — that could change how microchips are made.