Researchers have achieved a significant breakthrough in quantum computing by developing a technique that enables machine learning algorithms to outperform classical computers. Published in Nature Photonics, the study describes using a quantum photonic circuit combined with a custom machine learning algorithm, effectively leveraging just two photons. This novel approach demonstrated enhanced speed, accuracy, and efficiency in processing data compared to traditional methods, particularly benefiting "kernel-based machine learning," which has seen renewed interest recently.
The researchers utilized a femtosecond laser to imprint data onto borosilicate glass, injecting photons in various configurations analyzed through a hybrid quantum-binary system. Results showed that quantum processing was faster and more energy-efficient than classical techniques. Importantly, this approach does not rely on entangled gates and is scalable, suggesting potential advancements as photon or qubit numbers increase. The findings open pathways for integrating photonic processors with existing machine learning systems, thereby addressing energy consumption challenges in current electronic models.
