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Evidence record 465 · automatically gathered

A Coherence Law for Trainability in Noisy Equivariant Quantum Neural Networks

Symmetry provides a quantum neural network structure, but on its own it does not keep the network trainable once noise is present. We ask which physical quantity decides whether the gradients of an equivariant circuit survive decoherence, and we answer with a compact training law. Working with U(1)-equivariant brickwork circuits that conserve a charge, we find that two distinct effects govern a trainable gradient. Causality fixes where the gradient can live, confining it to the backward light co

Record details

Published: 28 June 2026
Source: arXiv
Category: Research
Topics: Regulation
Retrieved: 14 July 2026

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How to cite this record

ethics.ai (28 June 2026), “A Coherence Law for Trainability in Noisy Equivariant Quantum Neural Networks,” evidence record 465, https://ethics.ai/record/465 (originally published by arXiv).

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