Probabilistic computing with p-bits

TL;DR

This paper advocates for probabilistic computers built from p-bits, which are hardware-efficient, controllable probabilistic bits that can accelerate algorithms in Bayesian inference, optimization, and quantum simulation.

Contribution

It introduces the concept of p-bits, proposes a generic architecture for p-computers, and demonstrates their potential to speed up probabilistic algorithms through large-scale emulation.

Findings

P-bits can be implemented with specialized hardware.

P-computers significantly accelerate randomized algorithms.

Emulation shows potential for applications in Bayesian networks, optimization, and quantum Monte Carlo.

Abstract

Digital computers store information in the form of bits that can take on one of two values 0 and 1, while quantum computers are based on qubits that are described by a complex wavefunction, whose squared magnitude gives the probability of measuring either 0 or 1. Here, we make the case for a probabilistic computer based on p-bits, which take on values 0 and 1 with controlled probabilities and can be implemented with specialized compact energy-efficient hardware. We propose a generic architecture for such p-computers and emulate systems with thousands of p-bits to show that they can significantly accelerate randomized algorithms used in a wide variety of applications including but not limited to Bayesian networks, optimization, Ising models, and quantum Monte Carlo.