Multi-agent AI systems (such as drone swarms or robotic teams) normally rely on constant wireless communication. However, in ...

A new study by an international team of scientists has outlined the potential for a new class of robots that can match the capabilities of humans. These advances are driven by the convergence of ...

Patrick Kaczmarek, a junior at Carnegie Mellon University majoring in applied physics and mechanical engineering, was awarded a Summer Undergraduate Research Fellowship (SURF) to work on a project ...

The quantum-AI convergence represents the next fundamental shift in how we process information, solve problems and create ...

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 ...

Imagine a future where quantum computers supercharge machine learning—training models in seconds, extracting insights from ...

This diagram illustrates how the team reduces quantum circuit complexity in machine learning using three encoding methods—variational, genetic, and matrix product state algorithms. All methods ...

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 ...

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. When you purchase through ...

The quantum tangent kernel method is a mathematical approach used to understand how fast and how well quantum neural networks can learn. A quantum neural network is a machine learning model that runs ...