# Energy-Efficient p-Bit-Based Fully-Connected Quantum-Inspired Simulated Annealer with Dual BRAM Architecture

arXiv: https://arxiv.org/abs/2602.16143 · HTML: https://mlsanity.com/paper/2602.16143/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Feb 2026.

## Built on by (most cited first, 1 of 1)

- [Probabilistic Algorithms for Ising Machines from Optimization to Generative AI](https://mlsanity.com/paper/2610.03972/) (2026)

## Abstract (arXiv)

Probabilistic bits (p-bits) offer an energy-efficient hardware abstraction for stochastic optimization; however, existing p-bit-based simulated annealing accelerators suffer from poor scalability and limited support for fully connected graphs due to fan-out and memory overhead. This paper presents an energy-efficient FPGA architecture for stochastic simulated quantum annealing (SSQA) that addresses these challenges. The proposed design combines a spin-serial and replica-parallel update schedule with a dual-BRAM delay-line architecture, enabling scalable support for fully connected Ising models while eliminating fan-out growth in logic resources. By exploiting SSQA, the architecture achieves fast convergence using only final replica states, significantly reducing memory requirements compared to conventional p-bit-based annealers. Implemented on a Xilinx ZC706 FPGA, the proposed system solves an 800-node MAX-CUT benchmark and achieves up to 50% reduction in energy consumption and over 90% reduction in logic resources compared with prior FPGA-based p-bit annealing architectures. These results demonstrate the practicality of quantum-inspired, p-bit-based annealing hardware for large-scale combinatorial optimization under strict energy and resource constraints.