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

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.

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

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

My guests are Mario Krenn – who heads the Artificial Scientist Lab at Germany’s Max Planck Institute for the Science of Light ...

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.

In Optica Quantum, Okinawa Institute of Science and Technology (OIST) researchers propose the first practical application of boson sampling for image identification, which is critical in a variety ...