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

A review published in National Science Review highlights recent progress at the intersection of machine learning and quantum ...

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 ...

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

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 is an interdisciplinary field that harnesses the computational power of quantum systems to develop algorithms that can process and analyze data more efficiently than ...

Researchers have found a way to make the chip design and manufacturing process much easier — by tapping into a hybrid blend of artificial intelligence and quantum computing.

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.

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.

About a year and a half ago, quantum control startup Quantum Machines and Nvidia announced a deep partnership that would bring together Nvidia's DGX ...