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

Quantum machine learning is at the intersection of two of the most sought after research
areas—quantum computing and classical machine learning. Quantum machine learning …

The curse of dimensionality associated with the Hilbert space of spin systems provides a
significant obstruction to the study of condensed matter systems. Tensor networks have …

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 …

Neural quantum states are a new family of variational Ansätze for quantum-many body wave
functions with advantageous properties in the notoriously challenging case of two spatial …

Tensor network states are used to approximate ground states of local Hamiltonians on a
lattice in D spatial dimensions. Different types of tensor network states can be seen to …

Tensor network decompositions offer an efficient description of certain many-body states of a
lattice system and are the basis of a wealth of numerical simulation algorithms. In a recent …

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 …

We describe an iterative method to optimize the multiscale entanglement renormalization
ansatz for the low-energy subspace of local Hamiltonians on a D-dimensional lattice. For …

We explain how to implement, in the context of projected entangled-pair states (PEPSs), the
general procedure of fermionization of a tensor network introduced in P. Corboz and G …