Emmanuel Zambrini Cruzeiro, Christine De Mol, Serge Massar, and Stefano Pironio recently published an innovative study on quantum technologies in “Quantum Machine Intelligence” (Vol. 6, Issue 19, 2024).

According to Cruzeiro, the study introduces quantum-inspired algorithms for machine learning. He explains: "On the one hand, taking inspiration from neuroscience leads to the field of neuromorphic computing, which has already shown promising results. On the other hand, one can take inspiration from nature by observing the behavior of microscopic particles. This approach is known as quantum-inspired computation."

The study explores quantum-inspired machine learning algorithms that rival state-of-the-art classical methods. Cruzeiro highlights additional advantages: "These algorithms can run on quantum computers and handle both classical and quantum data."

Focusing on classification tasks, the researchers present algorithms inspired by the quantum state discrimination problem. While these algorithms are designed for quantum computers, the study emphasizes their classical implementation.

This open-access study bridges the gap between quantum state discrimination and quantum machine learning, offering insights into kernel methods for quantum-inspired approaches.

The full article is available here: