Gauge theories are of paramount importance in our understanding of fundamental
constituents of matter and their interactions. However, the complete characterization of their …

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

The advent of noisy-intermediate scale quantum computers has introduced the exciting
possibility of achieving quantum speedups in machine learning tasks. These devices …

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 …

Tensor networks, a model that originated from quantum physics, has been gradually
generalized as efficient models in machine learning in recent years. However, in order to …

Tensor networks are efficient representations of high-dimensional tensors with widespread
applications in quantum many-body physics. Recently, they have been adapted to the field …

Natural language processing (NLP) is at the forefront of great advances in contemporary AI,
and it is arguably one of the most challenging areas of the field. At the same time, with the …

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 …

Classical probability distributions on sets of sequences can be modeled using quantum
states. Here, we do so with a quantum state that is pure and entangled. Because it is …

One key step in performing quantum machine learning (QML) on noisy intermediate-scale
quantum (NISQ) devices is the dimension reduction of the input data prior to their encoding …