QPC Noise Reducer

Partial but significant noise reduction at the QPC software layer. Readout mitigation, multi-run aggregation, constraint projection. Tested on IBM Fez—col3 improved from −0.72 to −0.88 with readout mitigation; the −1.0 col3 value in the table is reached only after post-hoc constraint projection on mitigated expectations, not as a raw unprocessed hardware readout.

What the Reducer Does

Quantum Polycontextural Computing (QPC) enables multiple logical contexts to coexist and interact within quantum systems. We developed a universal QPC noise reducer—a software module that applies post-processing strategies aligned with QPC architecture.

Strategies

Readout mitigation — Corrects measurement bit-flip bias using backend calibration (local assignment-matrix model, with Tikhonov regularization).
Multi-run aggregation — Merges or averages results over multiple independent runs to reduce statistical noise.
Majority decode — For redundant block encoding (e.g. kenogram-style): majority vote per block to recover logical bits.
Expectation extrapolation — Optional Richardson/linear extrapolation to zero noise from scaled-circuit runs (ZNE-style).
Constraint projection (KS) — Optional post-hoc classical projection of context products onto the ideal Kochen–Specker sign manifold after mitigation (not a hardware re-run).

The module is designed for all QPC tasks: KS (Kochen–Specker), CSI, SWR, redundant kenogram, holographic memory, and others.

Measured Results on IBM Fez

We tested the reducer on the Mermin–Peres Kochen–Specker contextuality experiment. The key metric is the product of three Pauli expectations in column 3 (col3), which should equal −1 for ideal quantum contextuality.

Verdict: col3 improved from −0.72 to −0.88 with readout mitigation and regularization—a significant gain toward the ideal −1; that is the best single number tied directly to mitigated measurement data before manifold projection. Applying KS constraint projection afterward aligns products to the ideal row/column signs (col3 = −1.0); that step is classical post-processing, not a second noiseless QPU execution.

Configuration	col3 product	Ideal	Contextuality sign check
No mitigation	−0.72	−1	✓
Readout mitigation, no regularization	−0.87	−1	✗
Readout mitigation + regularization (1e−4)	−0.88	−1	✓
Full pipeline (+ post-hoc constraint projection)	−1.0 (projected)	−1	✓

Constraint projection: The −1.0 col3 entry is produced by a post-hoc classical step that projects mitigated context products onto the ideal Kochen–Specker sign manifold—it is not a raw unprocessed hardware readout and does not imply a separate noiseless QPU execution.

Why “Partial but Significant”

The reducer addresses measurement (readout) noise and statistical shot noise. It does not correct gate errors or decoherence during computation. Those require hardware improvements or full error correction.

Within its scope, the effect is substantial: about a 22% relative improvement in the main KS observable (from −0.72 to −0.88). For tasks dominated by readout and shot noise, such gains can materially improve structure recovery, correlation estimates, and logical-bit fidelity.

Implications for QPC

QPC’s multi-contextural architecture provides natural hooks for noise reduction:

Architecture alignment

Aggregation — Combining runs across seeds or contexts reduces variance; QPC’s context-aware design supports this at the software level.
Redundancy — Kenogrammatic block encoding enables majority decode; repetition across blocks exploits redundancy inherent in QPC.
Distribution — Holographic-style partial readout and marginal recovery can benefit from per-subset mitigation and aggregation.

The reducer demonstrates that QPC-level software techniques can yield measurable, practical noise reduction on current quantum hardware—without waiting for fault-tolerant systems.