Understanding Logical-Shift Error Propagation in Quanvolutional Neural Networks

Quanvolutional neural networks (QNNs) have been successful in image classification, exploiting inherent quantum capabilities to improve performance of traditional convolution. Unfortunately, the qubit’s reliability can be a significant issue for QNNs inference, since its logical state can be altered by both intrinsic noise and by the interaction with natural radiation. In this article, we aim […]

Understanding Logical-Shift Error Propagation in Quanvolutional Neural Networks

Quanvolutional Neural Networks (QNNs) have been successful in image classification, exploiting inherent quantum capabilities to improve performance of the traditional convolution. Unfortunately, the qubit’s reliability can be a significant issue for QNNs inference, since its logical state can be altered by both intrinsic noise and by the interaction with natural radiation. In this paper we […]

Machine-Learning-Based Qubit Allocation for Error Reduction in Quantum Circuits

Quantum computing is a quickly growing field with great potential for future technology. Quantum computers in the current noisy intermediate-scale quantum (NISQ) era face two major limitations:1) qubit count and 2) error vulnerability. Although quantum error correction methods exist, they are not applicable to the current size of computers, requiring thousands of qubits, while current […]