  The growth of artificial intelligence and IoT has created a significant
computational load for solving non-deterministic polynomial-time (NP)-hard
problems, which are difficult to solve using conventional computers. The Ising
computer, based on the Ising model and annealing process, has been highly
sought for finding approximate solutions to NP-hard problems by observing the
convergence of dynamic spin states. However, it faces several challenges,
including high power consumption due to artificial spins and randomness
emulated by complex circuits, as well as low scalability caused by the rapidly
growing connectivity when considering large-scale problems. Here, we present an
experimental Ising annealing computer based on superparamagnetic tunnel
junctions (SMTJs) with all-to-all connections, which successfully solves a
70-city travelling salesman problem (4761-node Ising problem). By taking
advantage of the intrinsic randomness of SMTJs, implementing a proper global
annealing scheme, and using an efficient algorithm, our SMTJ-based Ising
annealer shows superior performance in terms of power consumption and energy
efficiency compared to other Ising schemes. Additionally, our approach provides
a promising way to solve complex problems with limited hardware resources.
Moreover, we propose a crossbar array architecture for scalable integration
using conventional magnetic random access memories. Our results demonstrate
that the SMTJ-based Ising annealing computer with high energy efficiency,
speed, and scalability is a strong candidate for future unconventional
computing schemes.
