Abstract
Conventional computers operate deterministically using strings of zeros and ones called bits to represent information in binary code. Despite the evolution of conventional computers into sophisticated machines, there are many classes of problems that they cannot efficiently address, including inference, invertible logic, sampling and optimization, leading to considerable interest in alternative computing schemes. Quantum computing, which uses qubits to represent a superposition of 0 and 1, is expected to perform these tasks efficiently^{1–3}. However, decoherence and the current requirement for cryogenic operation^{4}, as well as the limited manybody interactions that can be implemented, pose considerable challenges. Probabilistic computing^{1,5–7} is another unconventional computation scheme that shares similar concepts with quantum computing but is not limited by the above challenges. The key role is played by a probabilistic bit (a pbit)—a robust, classical entity fluctuating in time between 0 and 1, which interacts with other pbits in the same system using principles inspired by neural networks^{8}. Here we present a proofofconcept experiment for probabilistic computing using spintronics technology, and demonstrate integer factorization, an illustrative example of the optimization class of problems addressed by adiabatic^{9} and gated^{2} quantum computing. Nanoscale magnetic tunnel junctions showing stochastic behaviour are developed by modifying marketready magnetoresistive randomaccess memory technology^{10,11} and are used to implement threeterminal pbits that operate at room temperature. The pbits are electrically connected to form a functional asynchronous network, to which a modified adiabatic quantum computing algorithm that implements three and fourbody interactions is applied. Factorization of integers up to 945 is demonstrated with this rudimentary asynchronous probabilistic computer using eight correlated pbits, and the results show good agreement with theoretical predictions, thus providing a potentially scalable hardware approach to the difficult problems of optimization and sampling.
Original language  English 

Pages (fromto)  390393 
Number of pages  4 
Journal  Nature 
Volume  573 
Issue number  7774 
DOIs  
Publication status  Published  2019 Sept 19 
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