This arXiv preprint (v1, May 2026) reports the first experimental realization of a four-body Hopfield model on a programmable photonic processor using single photons and binary phase shifters to emulate Ising neurons. The setup identifies three regimes—memory retrieval, spin-glass blackout, and paramagnetic—via two-photon interference, confirming overlap with stored patterns at low capacity and temperature. As a preprint it lacks peer review. Methodology relies on fully connected networks realized through optical modes, but sample size details and statistical error bars across runs remain sparse. Limitations include restriction to dense all-to-all coupling and current photon-loss constraints that cap system size. The work advances beyond abstract p-spin theory by demonstrating concrete relaxation dynamics on hardware. It misses explicit energy-consumption benchmarks against classical simulators or neuromorphic chips, and underplays ties to Parisi’s 1979 replica-symmetry-breaking framework and Hopfield’s 1982 associative-memory model. Cross-referencing with photonic quantum simulation advances (e.g., arXiv:2302.01867 on programmable interferometers) and spin-glass ML mappings (Phys. Rev. X 2021) reveals untapped potential for low-power quantum accelerators in pattern-completion tasks, provided local-interaction extensions materialize.