Probabilistic computing with p-bits

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.

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• Current-induced deterministic switching of van der Waals ferromagnet at room temperature
• Biasing the quantum vacuum to control macroscopic probability distributions
• Experimental demonstration of an integrated on-chip p-bit core utilizing stochastic Magnetic Tunnel Junctions and 2D-MoS2 FETs
• Efficient Probabilistic Computing with Stochastic Perovskite Nickelates
• Accelerated Quantum Monte Carlo with Probabilistic Computers
• Integrated probabilistic computer using voltage-controlled magnetic tunnel junctions as its entropy source
• Ultrafast spontaneous spin switching in an antiferromagnet
• Tunneling current-controlled spin states in few-layer van der Waals magnets
• Lanthanide molecular nanomagnets as probabilistic bits
• Noise-augmented Chaotic Ising Machines for Combinatorial Optimization and Sampling
• Stochastic thermodynamic bounds on logical circuit operation
• Superior probabilistic computing using operationally stable probabilistic-bit constructed by manganite nanowire
• pc-COP: An Efficient and Configurable 2048-p-Bit Fully-Connected Probabilistic Computing Accelerator for Combinatorial Optimization
• Electrically Tunable Picosecond-scale Octupole Fluctuations in Chiral Antiferromagnets
• Superparamagnetic and Stochastic-Write Magnetic Tunnel Junctions for High-Speed True Random Number Generation in Advanced Computing
• Entropy Computing, A Paradigm for Optimization in Open Photonic Systems
• Creating currents of electric bubbles
• Ground-State Probabilistic Logic with the Simplest Binary Energy Landscape for Probabilistic Computing
• In-plane dominant anisotropy stochastic magnetic tunnel junction for probabilistic computing: A Fokker-Planck study