Ultrafast artificial intelligence: Machine learning with atomic-scale quantum systems

TL;DR

This paper proposes a quantum atomic system as a ultrafast machine learning platform for image recognition, demonstrating initial success and scalability potential for atomic-scale quantum systems to outperform existing AI hardware.

Contribution

It introduces a novel quantum atomic system approach for machine learning, utilizing intense light-matter interactions for ultrafast image recognition tasks.

Findings

Achieved about 40% success rate in digit recognition

Demonstrated scalability to larger molecular systems

Operates on timescales down to tens of femtoseconds

Abstract

We train a model atom to recognize hand-written digits between 0 and 9, employing intense light--matter interaction as a computational resource. For training, individual images of hand-written digits in the range 0-9 are converted into shaped laser pulses (data input pulses). Simultaneously with an input pulse, another shaped pulse (program pulse), polarized in the orthogonal direction, is applied to the atom and the system evolves quantum mechanically according to the time-dependent Schr\"odinger equation. The purpose of the optimal program pulse is to direct the system into specific atomic final states that correspond to the input digits. A success rate of about 40\% is demonstrated here for a basic optimization scheme, so far limited by the computational power to find the optimal program pulse in a high-dimensional search space. This atomic-intelligence image-recognition scheme is scalable towards larger (e.g. molecular) systems, is readily reprogrammable towards other learning/classification tasks and operates on time scales down to tens of femtoseconds. It has the potential to outpace other currently implemented machine-learning approaches, including the fastest optical on-chip neuromorphic systems and optical accelerators, by orders of magnitude.