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This study introduces probabilistic computing using spintronics, demonstrating integer factorization with a novel p-bit network. This approach offers a scalable hardware solution for complex optimization and sampling problems at room temperature.

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Area of Science:

  • Explores unconventional computing paradigms beyond classical and quantum approaches.
  • Focuses on spintronics technology for novel computational hardware development.

Background:

  • Classical computers struggle with complex problems like optimization and sampling.
  • Quantum computing offers potential but faces challenges such as decoherence and cryogenic requirements.
  • Probabilistic computing emerges as an alternative, leveraging principles from neural networks.

Purpose of the Study:

  • To present a proof-of-concept experiment for probabilistic computing using spintronics.
  • To demonstrate the capability of this new computing scheme for solving optimization problems, specifically integer factorization.

Main Methods:

  • Developed nanoscale magnetic tunnel junctions from magnetoresistive random-access memory technology to create probabilistic bits (p-bits).
  • Implemented a functional asynchronous network of three-terminal p-bits operating at room temperature.
  • Applied a modified adiabatic quantum computing algorithm with three- and four-body interactions to the p-bit network.

Main Results:

  • Successfully demonstrated integer factorization up to 945 using eight correlated p-bits.
  • Achieved good agreement between experimental results and theoretical predictions.
  • Showcased a rudimentary asynchronous probabilistic computer capable of tackling optimization tasks.

Conclusions:

  • Probabilistic computing using spintronics offers a robust, room-temperature alternative for complex computations.
  • The developed p-bit network provides a potentially scalable hardware approach for optimization and sampling problems.
  • This work validates spintronics as a viable platform for building next-generation probabilistic computers.