Polychromatic optical scattering and nonlinear chromatic mixing in passive optical media can serve as a physical reservoir computer with no digital-to-analog conversion overhead. Single-wavelength optical reservoir computing has already demonstrated digit classification using scattering media (frosted glass, multimode fibre) as the reservoir. The novel extension: using multiple simultaneous wavelengths (colours) as independent high-dimensional input channels, exploiting chromatic dispersion and nonlinear optical mixing as the physical reservoir dynamics.

This discovery has a Claude-generated validation package with a full experimental design.

Precise Hypothesis

Spine & Adversarial ReadReady for validation

“This hypothesis tests whether nonlinear cross-wavelength mixing in a passive polychromatic scattering medium provides reservoir-computing capacity beyond what is achievable by simply concatenating independent single-wavelength optical reservoirs of equal total dimensionality and photon budget.”

• highAt typical bench-top laser powers (mW range) used with passive media like frosted glass, third-order nonlinearities (four-wave mixing, cross-phase modulation) are extremely weak (chi^3 effects require high intensity or long interaction lengths), so any observed 'chromatic mixing advantage' may actually be linear wavelength-dependent scattering diversity, not true nonlinear mixing — mislabeling the mechanism.
  The protocol explicitly includes a spectroscopic four-wave-mixing confirmation step (Methodology step 3) and a power-ablation control (step 10) designed to isolate nonlinear contribution from linear multiplexing gain; however, if nonlinearity is confirmed negligible, the EVP as written would need to re-scope the hypothesis to 'linear polychromatic multiplexing advantage,' which is a materially narrower and less novel claim. This gap is acknowledged, not fully resolved in advance.
• mediumWhy MNIST/NARMA-10 and ridge regression specifically, rather than more diagnostic reservoir-computing metrics or harder benchmark tasks — the methodology choice (standard shallow benchmarks) may not stress-test whether cross-wavelength mixing contributes meaningfully to a nontrivial task, and could produce ceiling effects (both conditions near 100%) that hide true differences.
  MNIST and NARMA-10 are chosen for direct comparability with prior published single-wavelength optical reservoir results, enabling apples-to-apples baseline validation (Methodology step 1). The protocol mitigates ceiling effects by using reduced-resolution MNIST (8x8/14x14) and by including memory-capacity/nonlinear-Volterra metrics as a secondary, more sensitive diagnostic (step 7) rather than relying on classification accuracy alone; this is a partial justification but the choice of these specific benchmarks over harder alternatives (e.g., chaotic Mackey-Glass, Santa Fe laser series) is not independently defended and remains a methodological weakness.
• highThe 'independent-channel control' (condition C) computationally concatenates sequentially-acquired single-wavelength readouts, which is not physically identical to true simultaneous multi-wavelength illumination in terms of noise correlation structure, detector integration time, and total energy delivered to the sample — any performance difference could reflect these confounds rather than genuine nonlinear mixing.
  Not fully resolved in the current design; the dimensionality-matched control (step 8) partially addresses total-information confounds, but temporal/thermal/noise-correlation differences between simultaneous vs. sequential acquisition are not explicitly controlled for (e.g., no proposed simultaneous-but-independently-modulated control using orthogonal spatial regions of the same medium). This is a genuine methodological gap that should be addressed by adding a fourth control condition (simultaneous illumination, spatially or temporally multiplexed to prevent mixing) before drawing causal conclusions.

Experimental Protocol

ROI Projection

Implementation Sketch

# Optical setup control loop
for condition in [MONO, POLY, INDEPENDENT_CONCAT]:
    for sample in dataset:
        encode_input_to_DMD(sample, condition)
        if condition == POLY:
            fire_lasers_simultaneously([405,532,650])
        elif condition == MONO:
            fire_laser(650)
        elif condition == INDEPENDENT_CONCAT:
            for wl in [405,532,650]:
                fire_laser(wl); capture_frame(wl)
        frame = capture_readout(camera, condition)
        features[condition].append(flatten(frame))

# Readout training
for condition in features:
    X_train, X_test = split(features[condition])
    ridge = RidgeClassifier(alpha=tuned_via_CV)
    ridge.fit(X_train, y_train)
    acc[condition] = ridge.score(X_test, y_test)

# Statistical comparison
bootstrap_test(acc[POLY], acc[INDEPENDENT_CONCAT], n_resamples=10000)

# Ablation
attenuate_power(to_linear_regime)
rerun(condition=POLY, label="POLY_ablated")
compare(acc[POLY] - acc[INDEPENDENT_CONCAT], acc[POLY_ablated] - acc[INDEPENDENT_CONCAT])