Year: 2024
Type: conference-paper
Abstract: Simulated Adiabatic Bifurcation (aSB) is a quantum-inspired algorithm that provides approximate solutions for large-scale optimization problems using the Ising model. It emulates the quantum adiabatic evolution of a network of non-linear Kerr oscillators on classical platforms. These oscillators undergo bifurcation, where each branch corresponds to a spin state, with the network creating an energy imbalance to deter-mine the optimal solution. This approach is highly parallelizable, making it suitable for implementation on GPUs and FPGAs. However, classical emulation introduces analog errors, potentially compromising performance. To address this, alternative methods like ballistic (bSB) and discrete (dSB) evolutions were developed, with the ballistic approach further enhanced by thermal fluctuations (HbSB). Our comprehensive analysis of bSB, dSB, and HbSB using benchmark Max-Cut and knapsack problems aimed to identify the best balance of speed, area, and accuracy. We found that fixed-point number representation allows more algorithm iterations within the same timeframe as floating-point representation. The proposed FPGA architecture implements d$S$B and its heated version, leveraging high parallelizability and efficient memory or-ganization. This design supports larger coefficients and improves problem-solving efficiency without multipliers for matrix-vector evaluation. Challenges include mitigating data dependency between matrix-vector multiplication and time evolution, and developing a preconditioning method for adapting problem coefficients, making the architecture suitable for real-world applications and potential ASIC release.
Authors: Fabrizio Orlando, Deborah Volpe, Giacomo Orlandi, Fabrizio Riente, Marco Vacca, Mariagrazia Graziano
Institution: Politecnico di Torino
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4406261938