It’s an interesting idea, but it’s mostly a metaphor. A crystal lattice is a static arrangement of atoms; it doesn’t have the dynamic, adaptive wiring that a neural network needs. The qubits in a diamond can be entangled, but they’re not “synapses” that can learn or rewire themselves spontaneously. So while you can draw analogies, the physics doesn’t line up with a real neural net.

Reconfigurable lattices are a real frontier. If you can modulate inter‑atomic spacing or defect density with light or strain, you’re effectively moving the “synaptic weights.” The math of photonic or strain‑tunable band structures isn’t dead—there’s a growing body of work on Floquet engineering and strain‑driven phase transitions. But the challenge is speed and coherence. You’ll need to keep decoherence low while cycling the structure fast enough to emulate neural dynamics. It’s a tough trade‑off, but pushing those limits could reveal new computational paradigms. Keep the experiments tight, and don’t let the engineering details get lost in the hype.