Recent advances in classical reinforcement learning (RL) and quantum computation point to
a promising direction for performing RL on a quantum computer. However, potential …

In this paper we propose two scale-inspired local feature extraction methods based on
Quantum Convolutional Neural Network (QCNN) in the Tensorflow quantum framework for …

Once developed for quantum theory, tensor networks (TNs) have been established as a
successful machine learning (ML) paradigm. Now, they have been ported back to the …

The matrix product state (MPS) belongs to the most important models in, for example,
quantum information sciences and condensed-matter physics. However, realizing an N …

We introduce a hybrid model combining a quantum-inspired tensor network and a
variational quantum circuit to perform supervised learning tasks. This architecture allows for …

Quantum many-body control is a central milestone en route to harnessing quantum
technologies. However, the exponential growth of the Hilbert space dimension with the …

We demonstrate the use of tensor networks for image classification with the TensorNetwork
open source library. We explain in detail the encoding of image data into a matrix product …

It is a critical challenge to simultaneously achieve high interpretability and high efficiency
with the current schemes of deep machine learning (ML). The tensor network (TN), a well …

This paper presents a methodology for image classification using Graph Neural Network
(GNN) models. We transform the input images into region adjacency graphs (RAGs), in …

Hybrid quantum-classical algorithms based on variational circuits are a promising approach
to quantum machine learning problems for near-term devices, but the selection of the …