The DLA-parity hardware campaign on ibm_kingston measured a
non-zero asymmetry between the "even" and "odd" initial-state
sectors: peak +17.48 % at depth 6, mean +9.25 % across the eight
depths surveyed, with Fisher combined
| Reading | Claim | This campaign |
|---|---|---|
| Narrow — signature irreproducibility | No classical simulator of the idealised Kuramoto-XY Hamiltonian |
Proved below. |
| Broad — quantum computational advantage | No classical algorithm, given polynomial resources, can simulate the hardware evolution at the system size used in the campaign. |
Not proved. Phase 1 used |
These are different statements. Running them together — "the
observed asymmetry is beyond classical" without specifying which
reading is meant — is overclaim. The narrow reading is the honest
interpretation of the Phase 1 data; the broad reading requires
scaling to GAP_CLOSURE_STATUS.md.
Statement. Let
Proof. Every summand of
-
$[Z_{i}, P] = 0$ because$P$ is a product of$Z$ operators and$[Z_{i}, Z_{j}] = 0$ . -
$[X_{i}X_{j}, P]$ :$X_{i}$ anticommutes with$Z_{i}$ and commutes with every other$Z_{k}$ ; similarly for$X_{j}$ . Conjugating$X_{i}X_{j}$ by$P$ therefore picks up two sign flips, i.e.$(-1)^{2} = +1$ . So$P X_{i} X_{j} P^{-1} = X_{i} X_{j}$ , hence$[X_{i}X_{j}, P] = 0$ . -
$[Y_{i}Y_{j}, P] = 0$ by the same argument —$Y$ also anticommutes with$Z$ on the same site.
Since each summand commutes with
Initial parity eigenvalues. The even sector uses
where
The proof is not left as prose. It is enforced by the test suite and the numerical reference.
-
tests/test_classical_irreproducibility.py(28 tests) performs three independent algebraic checks at every relevant system size:-
[H, P] = 0as aSparsePauliOpidentity — each term pair is reduced symbolically, and every residue coefficient is numerically below$10^{-12}$ (in practice 0.0 exactly). Run at$n = 3, 4, 6$ . -
[H_{Z}, P] = [H_{XY}, P] = 0individually, i.e. each Lie-Trotter generator commutes with$P$ by itself. This is the stronger statement that makes the Trotter decomposition exact on$P$ , not just first-order accurate. - For a parity-eigenstate initial condition, the opposite-parity
mask applied to
$U(t) |\psi_{0}\rangle$ has total probability below$10^{-18}$ at depth 1, 4, 10, and 30 and at$t_{\text{step}}$ = 0.1, 0.3, and 1.7.
-
-
src/scpn_quantum_control/dla_parity/baselines.pycomputes the noiseless leakage reference across the published depth sweep using two independent backends (numpy dense and optional QuTiP). Both reportmax_abs_leakage < 1e-10and agree with each other to$10^{-12}$ . -
tests/test_cross_validation_qutip_dynamiqs.pyseparately confirms that the Hamiltonian matrix itself agrees between our internal Qiskit-based builder, QuTiP, and Dynamiqs to$10^{-10}$ relative. The invariant$[H, P] = 0$ is therefore a property of the physics we specified, not an artefact of the specific Hamiltonian-assembly code.
-
Nothing about quantum advantage at scale. The classical
evolution at
$n = 4$ runs in milliseconds on a laptop. The statement here is strictly about reproducibility of the specific observed signature, not about complexity-class separations. The broad claim ("no efficient classical algorithm can simulate this for any$n$ ") remains open and is tracked under Gap 2 inGAP_CLOSURE_STATUS.md. - Nothing about "hidden" classical models. The statement covers classical simulators that faithfully implement the idealised Hamiltonian. It does not rule out noise-matched classical models that deliberately inject a biased depolarising channel to recreate the observed asymmetry empirically. Such a model would have to reproduce the exact depth and sector dependence, and would be measurement-driven fitting rather than first-principles simulation.
Because the parity-conservation proof depends on paper/submissions/submission_002_phase1_dla_parity/phase1_dla_parity.tex
presents this as a hardware-calibration observation, not as a
quantum-advantage claim, and the distinction made in the table
above is the reason.
docs/falsification.md registers a Phase 1 reproducibility
criterion: the 342-circuit dataset, rerun through the analysis
pipeline, must recover
This document and tests/test_classical_irreproducibility.py
together close the narrow reading of audit item D1 from the
internal audit index.
The broad reading remains open and is tracked in
GAP_CLOSURE_STATUS.md as Gap 2,
with the concrete next step being scaling the campaign via
circuit cutting and/or MPS-falsifying regimes.