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Quantum & photonic applications

SWC is a retained-state feedback runtime for wave hardware. On quantum and photonic substrates it has a precise fit — strong in some regimes, structurally wrong in others. We map both so you can tell in a minute whether it is for you.

Photonics — the strongest fit

Mesh phase stabilization. Programmable MZI meshes drift with temperature and suffer thermal crosstalk; a small phase error destroys the intended interference. SWC holds each tap at its setpoint at one measurement per round, model-free, with no per-chip gain tuning. It natively handles the cos² transfer whose slope sign varies across the range — the runtime measures each tap’s drive direction at the start of the session, so no calibration step is required.

The mesh as an optimizer. For QUBO / diagonal objectives encoded in the optical output, the mesh physically realizes the landscape and SWC drives it toward a good configuration under a tight measurement deadline — the regime where a classical solver has no time to search. See QUBO & diagonal optimization.

Retained-state memory. When you track a stream of related target transforms, the carried phase configuration gives a re-convergence advantage that grows with how related the targets are and vanishes when they are unrelated — the signature that the effect is memory, not tuning. This is what makes a photonic mesh adaptive rather than merely stabilized.

Example: hold an MZI mesh locked under drift

One phase tap per channel, one detector read per round. The runtime measures each tap’s sign-varying response at the start, then regulates each tap to its target power as the chip drifts.

copyfrom swc import SWCOptimizer

N = 16                                  # mesh phase taps
target_power = measure_reference()      # desired per-tap optical power

opt = SWCOptimizer(license_key="EVAL-...", n=N,
                   mode="regulation", target=list(target_power))
phi = opt.start(current_phases())

while running:                          # holds lock as the chip drifts
    power = read_detectors(phi)         # one detector pass
    phi   = opt.step(list(power), target=list(target_power))
    apply_phases(phi)                   # write back to the heaters
opt.end()

Quantum — control, not variational solving

Where it fits: calibration and stabilization. Holding a single-rotation observable — a gate angle, a readout discrimination point — at setpoint as the device drifts, one measurement per round. This is regulation, and it is the regime SWC is built for.

Where it does NOT fit: variational energy minimization (VQE / QAOA). We tested this thoroughly and it does not work — marginal steering cannot see energy that lives in two-qubit correlations, and on degenerate or unreachable targets the loop does not descend. We do not sell SWC as a VQA optimizer. If someone tells you a marginal-feedback runtime minimizes VQE energy, be skeptical — we were, and the experiments settled it.

Photonic stabilization, the mesh-as-QUBO-optimizer, and the memory signature are established in controlled benchmarks against fairly-tuned baselines; on-chip validation is in progress and a hardware pilot is the decisive next step. The one physical retained-vs-reset result we have is a quantum mechanism ablation.