Problem
Research questions and friction points this paper is trying to address.
Quantum convolutional neural networks
quantum noise
quantum error correction
qubit overhead
bivariate bicycle codes
Low-Overhead Error-Corrected QCNNs Using Bivariate Bicycle Codes
🤖 AI Summary
This work addresses the challenge of non-convergence in quantum convolutional neural networks (QCNNs) on current noisy quantum devices due to the absence of efficient error correction. For the first time, bivariate bicycle (BB) codes—characterized by high thresholds, constant encoding rates, and linear code distance—are integrated into QCNNs, with a distance-4 BB code employed to achieve low-overhead quantum error correction. Simulation results under realistic noisy hardware models demonstrate that this approach substantially reduces resource overhead and effectively resolves the convergence failure observed in unprotected four-qubit QCNNs, thereby significantly enhancing their learning performance and practical viability.
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
bivariate bicycle codes
quantum error correction
quantum convolutional neural networks
low-overhead QEC
fault-tolerant quantum computing
🔎 Similar Papers
No similar papers found.
