The QuantLearn project develops an optically linked computing ecosystem for quantum machine learning (QML). We develop optical communication between conventional computers and cryogenic classical single flux quantum quantum (SFQ) computers. Here, SFQ serves as an interface for quantum computing, but it is also a promising, high-speed and energy-efficient alternative for supporting conventional high-performance computing (HPC). We also develop QML software where classical HPC takes care of most of the computing, for example, runs the necessary computing loops, and the HPC sends specific computing tasks to a QC. We utilize and develop QML algorithms which can be exponentially faster than corresponding classical machine learning algorithms. Despite significant promise, QML in general is suffering from the problem of transmitting classical data between HPC and QC. We aim to solve this problem with our data bus.

2022

2024