Low-Overhead Error-Corrected QCNNs Using Bivariate Bicycle Codes
For researchers working on quantum machine learning, this work provides a more efficient error correction method to make QCNNs feasible on near-term quantum devices, though it is an incremental improvement over existing QEC approaches.
The paper addresses high noise and qubit overhead in quantum convolutional neural networks (QCNNs) by proposing a low-overhead error correction technique using bivariate bicycle (BB) codes. Simulations show that an unprotected 4-qubit QCNN fails to converge, while the proposed distance-4 BB QEC method improves performance, representing a step toward practical QCNNs.
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.