Self-correcting High-speed Opto-electronic Probabilistic Computer

TL;DR

This paper introduces a high-speed, self-correcting optoelectronic probabilistic computer architecture using quantum photonic p-bits, achieving significant improvements in speed and energy efficiency over existing systems, with robust error correction.

Contribution

It presents the first integration of source-device independent quantum photonic p-bits with electronic control for scalable probabilistic computing.

Findings

Achieves a flip rate of 2.7 billion flips per second.

Consumes only 4.9 nanojoules per flip.

Demonstrates nearly three orders of magnitude improvement over magnetic tunnel junction systems.

Abstract

We present a novel self-correcting, high-speed optoelectronic probabilistic computer architecture that leverages source-device independent (SDI) quantum photonic p-bits integrated with robust electronic control. Our approach combines the intrinsic randomness and high bandwidth of quantum photonics with the programmability and scal- ability of classical electronics, enabling efficient and flexible probabilistic computation. We detail the design and implementation of a prototype system based on photonic integrated circuits and FPGA-based control, capable of implementing and manipulating 64000 logical p-bits. Experimental results demonstrate that our architecture achieves a flip rate of 2.7 x 10^9 flips/s with an energy consumption of 4.9 nJ/flip, representing nearly three orders of magnitude improvement in speed and energy efficiency compared to state-of-the-art magnetic tunnel junc- tion (MTJ) based systems. Furthermore, the SDI protocol enables real-time self-certification and error correction, ensuring reliable operation across a wide range of conditions and solving the problem of hardware variability as the number of p-bits scale. Our results establish quantum photonic p-bits as a promising platform for scalable, high-performance probabilistic computing, with significant implications for combinatorial optimization, machine learning, and complex system modeling.