New Paper: The Quantum Parity Trap
We just published our 26th paper, and it might be our most surprising result yet. Paper 26 shows that a two-qubit quantum circuit can beat the Dynamical Horizon Principle, the fundamental learning limit we discovered and characterized across 22 previous papers.
The trick is not more qubits or error correction. It is geometry. When Pauli depolarizing noise acts on a parity-detection circuit, the Bloch sphere contracts isotropically, so gradient sign is always preserved no matter how strong the noise. We call this the quantum parity trap: a regime where the optimizer receives consistent directional signal even as coherence collapses toward zero.
At p=0.74, only 0.018% of quantum coherence survives, and 6 out of 6 training seeds still converge. That is a 12x advantage over the classical DHP limit. As noise approaches its maximum, the advantage diverges toward infinity.
Supplementary experiments include real Rigetti QPU hardware runs with 4096 shots, readout error mitigation, and a >50 sigma margin; an optimizer ablation showing gradient direction alone is sufficient, with signSGD matching Adam while vanilla SGD fails; and a T1 amplitude damping sweep showing that T1 noise at any tested strength cannot evict seeds from the partial-parity attractor.
DHP holds generically. But for the right task structure, under the right noise, with the right optimizer, quantum interference structure defeats it.