Low-Overhead Error-Corrected QCNNs Using Bivariate Bicycle Codes

Researchers proposed a low-overhead error-correction technique for quantum convolutional neural networks (QCNNs) using bivariate bicycle (BB) codes. Simulations showed that an unprotected 4-qubit QCNN fails to converge under realistic noise, while the distance-4 BB code enables practical QCNN execution with reduced qubit cost. This work addresses key noise and scalability barriers for quantum machine learning.

arXiv:2607.05724v1 Announce Type: new Abstract: Quantum convolutional neural networks QCNNs combine the power of quantum computing and classical CNN for computational speedup in classification tasks. However, noise levels on state-of-the-art quantum devices remain too high for practical QCNN execution. In addition, despite the reliable surface code providing a method for error rates below a threshold value, they have a prohibitively large qubit cost. Recently introduced bivariate bicycle BB codes are of particular interest for their high error threshold, constant encoding rate, and linear code distance. Through simulation with realistic hardware noise sources, we demonstrate that a 4-qubit unprotected QCNN fails to converge and exhibits a worse learning rate compared to numerical simulations. Addressing both limitations, we propose a distance-4 BB quantum error-correction QEC technique for QCNNs. In doing so, we validate that our low-overhead QEC technique for QCNNS represents a step toward practical QCNNs.