TY - JOUR

T1 - Integer factorization using stochastic magnetic tunnel junctions

AU - Borders, William A.

AU - Pervaiz, Ahmed Z.

AU - Fukami, Shunsuke

AU - Camsari, Kerem Y.

AU - Ohno, Hideo

AU - Datta, Supriyo

N1 - Funding Information:
Acknowledgements We thank H. Sato, M. Bersweiler, T. Hirata, H. Iwanuma, K. Goto, C. Igarashi, I. Morita, R. Ono and M. Musya for technical support. We thank O. Hassan and S. Chowdhury for their help with the Methods sections comparing CMOS alternatives and quantum computing, respectively. A portion of this work was supported by ImPACT Program of CSTI, JSPS KAKENHI grant numbers 17H06093 and 19J12206, Cooperative Research Projects of RIEC, and ASCENT, one of six centres in JUMP, an SRC program sponsored by DARPA. W.A.B. acknowledges JST-OPERA.
Publisher Copyright:
© 2019, The Author(s), under exclusive licence to Springer Nature Limited.

PY - 2019/9/19

Y1 - 2019/9/19

N2 - 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 efficiently1–3. However, decoherence and the current requirement for cryogenic operation4, as well as the limited many-body interactions that can be implemented, pose considerable challenges. Probabilistic computing1,5–7 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 p-bit)—a robust, classical entity fluctuating in time between 0 and 1, which interacts with other p-bits in the same system using principles inspired by neural networks8. Here we present a proof-of-concept experiment for probabilistic computing using spintronics technology, and demonstrate integer factorization, an illustrative example of the optimization class of problems addressed by adiabatic9 and gated2 quantum computing. Nanoscale magnetic tunnel junctions showing stochastic behaviour are developed by modifying market-ready magnetoresistive random-access memory technology10,11 and are used to implement three-terminal p-bits that operate at room temperature. The p-bits are electrically connected to form a functional asynchronous network, to which a modified adiabatic quantum computing algorithm that implements three- and four-body interactions is applied. Factorization of integers up to 945 is demonstrated with this rudimentary asynchronous probabilistic computer using eight correlated p-bits, and the results show good agreement with theoretical predictions, thus providing a potentially scalable hardware approach to the difficult problems of optimization and sampling.

AB - 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 efficiently1–3. However, decoherence and the current requirement for cryogenic operation4, as well as the limited many-body interactions that can be implemented, pose considerable challenges. Probabilistic computing1,5–7 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 p-bit)—a robust, classical entity fluctuating in time between 0 and 1, which interacts with other p-bits in the same system using principles inspired by neural networks8. Here we present a proof-of-concept experiment for probabilistic computing using spintronics technology, and demonstrate integer factorization, an illustrative example of the optimization class of problems addressed by adiabatic9 and gated2 quantum computing. Nanoscale magnetic tunnel junctions showing stochastic behaviour are developed by modifying market-ready magnetoresistive random-access memory technology10,11 and are used to implement three-terminal p-bits that operate at room temperature. The p-bits are electrically connected to form a functional asynchronous network, to which a modified adiabatic quantum computing algorithm that implements three- and four-body interactions is applied. Factorization of integers up to 945 is demonstrated with this rudimentary asynchronous probabilistic computer using eight correlated p-bits, and the results show good agreement with theoretical predictions, thus providing a potentially scalable hardware approach to the difficult problems of optimization and sampling.

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U2 - 10.1038/s41586-019-1557-9

DO - 10.1038/s41586-019-1557-9

M3 - Article

C2 - 31534247

AN - SCOPUS:85072379146

VL - 573

SP - 390

EP - 393

JO - Nature

JF - Nature

SN - 0028-0836

IS - 7774

ER -