Quantum computers are expected to surpass the computational capabilities of classical
computers during this decade and have transformative impact on numerous industry sectors …

Despite its undeniable success, classical machine learning remains a resource-intensive
process. Practical computational efforts for training state-of-the-art models can now only be …

With the rapid advance of quantum machine learning, several proposals for the quantum-
analogue of convolutional neural network (CNN) have emerged. In this work, we benchmark …

Distributed training across several quantum computers could significantly improve the
training time and if we could share the learned model, not the data, it could potentially …

Abstract Machine learning has achieved dramatic success in a broad spectrum of
applications. Its interplay with quantum physics may lead to unprecedented perspectives for …

This paper presents a quantum convolutional neural network (QCNN) for the classification of
high energy physics events. The proposed model is tested using a simulated dataset from …

Quantum Methods for Neural Networks and Application to Medical Image Classification
Page 1 Quantum Methods for Neural Networks and Application to Medical Image …

Quantum computers are expected to have substantial impact on the finance industry, as they
will be able to solve certain problems considerably faster than the best known classical …

Quantum neural network (QNN), or equivalently, the parameterized quantum circuit (PQC)
with a gradient-based classical optimizer, has been broadly applied to many experimental …

Jonas Landman: jonas. landman@ qcware. com we trained on these small-scale datasets
require fewer parameters compared to standard classical benchmarks. While this …