Abstract

In hybrid quantum-classical (HQC) computing, the coherent execution of algorithms on quantum and classical resources is a critical challenge. Most HQC algorithms involve iterative exchanges of data between quantum and classical processors, causing system bottlenecks and leading to high latency in applications. We present a novel framework that unifies quantum and reconfigurable resources for HQC algorithms, to mitigate system bottlenecks and latency. The proposed framework integrates Field-Programmable Gate Arrays (FPGAs) with quantum processing units (QPUs) for deploying HQC algorithms. Classical subroutines of the algorithms are accelerated on FPGA fabric using a high-throughput processing pipeline, while quantum subroutines are executed on QPUs. High-level software is used to seamlessly facilitate workload distribution and data exchange between classical and quantum processors. To evaluate the proposed framework, an HQC algorithm, namely variational quantum classification and MNIST dataset are used as a test case. The results demonstrate that the FPGA pipeline achieves up to 8× improvement in runtime compared to the baseline of a state-of-the-art quantum software framework running on a server-grade CPU.

About the Speaker

Dr. Mahmud is currently an Assistant Professor at Florida Tech. He teaches Computer Architecture and Programmable Gate Arrays. His research is focused on future and emerging computing architectures such as quantum computing and reconfigurable computing. Specifically, his work is on optimizing quantum algorithms and circuits for efficient implementation on near-term quantum devices, developing applications for quantum computing and investigating hybrid quantum-classical architectures and FPGA-based hardware acceleration.