NbO_x Mott Memristor-Based Oscillatory P-trit for Ternary Potts Machine

Probabilistic computing is an emerging approach for solving combinatorial optimization and probabilistic inference by leveraging stochastic dynamics to explore complex energy landscapes. However, most hardware implementations remain constrained to binary probabilistic bits (p-bits), limiting efficiency for inherently multi-class problems. Here, we propose and experimentally demonstrate a ternary Potts machine (TPM) enabled by a NbO_x oscillator-based probabilistic ternary digit unit (i.e., p-trits). The p-trit exploits electro-thermal dynamics of a NbO_x threshold-switching memristor that exhibits two distinct negative differential resistance regimes, producing stochastic transitions among three discrete states (off/osc/on). A compact p-trit circuit digitizes the oscillatory signals into two-bit outputs, yielding a practical p-trit with an operation rate of 2.5 µs/bit, while the NbO_x oscillator, which is the primary source of energy consumption, consumes 2.4 nJ/bit. A microcontroller unit -integrated TPM prototype solves Max-3-Cut with ∼30% higher accuracy than p-bit-based computing while requiring ∼33% of its computational overhead. Simulations on the weighted ternary number partitioning problem confirm scalability and robustness, showing convergence of up to 99.96%, comparable to near-optimal solutions.