Year: 2025
Type: conference-paper
Abstract: A key strategy to enhance specialized hardware accelerators, such as GPUs and FPGA, is to reduce the numerical precision in arithmetic operations, which increases processing speed and lowers latency-both crucial for real-time AI applications. In this work, we consider NP-hard Quadratic Unconstrained Binary Optimization (QUBO) problems, which arise in machine learning and beyond. We show that these problems often require high numerical precision, posing challenges for hardware solvers. We introduce a principled Branch-andBound algorithm for reducing the precision requirements of QUBO problems by utilizing the dynamic range as measure of complexity. Experiments demonstrate that our method reduces the dynamic range in subset sum, clustering, and vector quantization problems, thereby increasing their solvability on actual quantum annealers and FPGA-based digital annealers.
Authors: Thore Gerlach, Nico Piatkowski
Institution: University of Bonn
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4416875152