The computational demands of today’s AI systems are starting to outpace what classical hardware can deliver. How can we fix this? One possible solution is quantum machine learning (QML). QML ...

This research paper delves into the realm of quantum machine learning (QML) by conducting a comprehensive study on time-series data. The primary objective is to compare the results and time complexity ...

When a quantum computer processes data, it must translate it into understandable quantum data. Algorithms that carry out this 'quantum compilation' typically optimize one target at a time. However, a ...

Quantum computing and machine learning convergence enable powerful new approaches for optimizing mobile edge computing (MEC) networks. This paper uses Lyapunov optimization theory to propose a novel ...

One of the current hot research topics is the combination of two of the most recent technological breakthroughs: machine learning and quantum computing. An experimental study shows that already ...

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 ...

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 ...

Microsoft and Atom Computing have announced what they claim is a significant step forward in reliable quantum computing, unveiling a commercial quantum machine built with 24 entangled logical qubits.

In the world of quantum physics, the rules that govern reality behave in ways that challenge what we think we know. Particles can be in two places at once. Actions on one particle can instantly affect ...

A partnership between Microsoft and Atom Computing has leveraged high-performance computing to successfully process 24 logical qubits, or quantum bits, marking a milestone in the quest to bring ...