One of the main challenges of quantum many‐body physics is the exponential growth in the
dimensionality of the Hilbert space with system size. This growth makes solving the …

Abstract Machine learning is a promising application of quantum computing, but challenges
remain for implementation today because near-term devices have a limited number of …

Hybrid quantum-classical algorithms provide ways to use noisy intermediate-scale quantum
computers for practical applications. Expanding the portfolio of such techniques, we propose …

Quantum circuits with hierarchical structure have been used to perform binary classification
of classical data encoded in a quantum state. We demonstrate that more expressive circuits …

Matrix product states (MPSs), a tensor network designed for one-dimensional quantum
systems, were recently proposed for generative modeling of natural data (such as images) in …

The search for an application of near-term quantum devices is widespread. Quantum
machine learning is touted as a potential utilisation of such devices, particularly those out of …

Inspired by coarse-graining approaches used in physics, we show how similar algorithms
can be adapted for data. The resulting algorithms are based on layered tree tensor networks …

We compare and contrast the statistical physics and quantum physics inspired approaches
for unsupervised generative modeling of classical data. The two approaches represent …

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 …

Interest in quantum computing has increased significantly. Tensor network theory has
become increasingly popular and widely used to simulate strongly entangled correlated …