We propose considering Quantum Key Distribution (QKD) protocols as a use case for Quantum Machine Learning (QML) algorithms. We define and investigate the QML task of optimizing eavesdropping attacks ...

Quantum computers are inching closer to practical deployment, but shielding fragile quantum information from errors is still very challenging. Now, a machine-learning-based decoder offers a strategy ...

Caltech scientists have developed an artificial intelligence (AI)–based method that dramatically speeds up calculations of the quantum interactions that take place in materials. In new work, the group ...

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, then ...

Researchers from the RIKEN Center for Quantum Computing have used machine learning to perform error correction for quantum computers—a crucial step for making these ...

Integrating quantum computing into AI doesn’t require rebuilding neural networks from scratch. Instead, I’ve found the most effective approach is to introduce a small quantum block—essentially a ...

This illustration draws a parallel between quantum state tomography and natural language modeling. In quantum tomography, structured measurements yield probability outcomes that are aggregated to ...

Paul Lipman is Chief Strategy Officer at Infleqtion, leading growth and productization efforts at the cutting edge of quantum technology. As a teenager, I was offered sage guidance by a family friend ...

Quantum computers might eventually be able to handle some AI applications that currently require huge amounts of conventional computing power. Such a development would be a major boost to machine ...