Perspective: Probabilistic computing with p-bits.

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 a $0$ or a $1$. Here we make the case for a probabilistic computer based on \textit{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 \textit{p-computers} and 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.