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 …

We re-examine holographic versions of the c-theorem and entanglement entropy in the
context of higher curvature gravity and the AdS/CFT correspondence. We select the gravity …

Using the anti-de Sitter conformal theory correspondence, we examine holographic
renormalization group flows in a framework where the bulk gravity contains higher curvature …

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 …

Models for nonunitary quantum dynamics, such as quantum circuits that include projective
measurements, have recently been shown to exhibit rich quantum critical behavior. There …

Entanglement renormalization techniques are applied to numerically investigate the ground
state of the spin-1 2 Heisenberg model on a kagome lattice. Lattices of N={36, 144,∞} sites …

Tensor network states are powerful variational Ansätze for many-body ground states of
quantum lattice models. The use of Monte Carlo sampling techniques in tensor network …