Evaluating spintronics-compatible implementations of Ising machines

Abstract

The commercial and industrial demand for the solution of hard combinatorial optimization problems push forward the development of efficient solvers. One of them is the Ising machine which can solve combinatorial problems mapped to Ising Hamiltonians. In particular, spintronic hardware implementations of Ising machines can be very efficient in terms of area and performance, and are relatively low-cost considering the potential to create hybrid CMOS-spintronic technology. Here, we perform a comparison of coherent and probabilistic paradigms of Ising machines on several hard Max-Cut instances, analyzing their scalability and performance at software level. We show that probabilistic Ising machines outperform coherent Ising machines in terms of the number of iterations required to achieve the problem s solution. Nevertheless, high frequency spintronic oscillators with sub-nanosecond synchronization times could be very promising as ultrafast Ising machines. In addition, considering that a coherent Ising machine acts better for Max-Cut problems because of the absence of the linear term in the Ising Hamiltonian, we introduce a procedure to encode Max-3SAT to Max-Cut. We foresee potential synergic interplays between the two paradigms.

26 pages, 6 Figures, submitted for publication in Phys. Rev. Applied (it will be presented at intermag 2023 in Japan)

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Cited by in corpus (6)

• Integrated probabilistic computer using voltage-controlled magnetic tunnel junctions as its entropy source
• ON-OFF Neuromorphic ISING Machines using Fowler-Nordheim Annealers
• General Oscillator-Based Ising Machine Models with Phase-Amplitude Dynamics and Polynomial Interactions
• Edge-of-chaos enhanced quantum-inspired algorithm for combinatorial optimization
• Lagrange Oscillatory Neural Networks for Constraint Satisfaction and Optimization
• Noise-based Local Learning using Stochastic Magnetic Tunnel Junctions