Machine learning (ML) has emerged as a formidable force for identifying hidden but
pertinent patterns within a given data set with the objective of subsequent generation of …

Tensor networks (TNs) have become one of the most essential building blocks for various
fields of theoretical physics such as condensed matter theory, statistical mechanics …

A large amount of effort has recently been put into understanding the barren plateau
phenomenon. In this perspective article, we face the increasingly loud elephant in the room …

Parametrized quantum circuits (PQCs) represent a promising framework for using present-
day quantum hardware to solve diverse problems in materials science, quantum chemistry …

We consider the preparation of matrix product states (MPS) on quantum devices via
quantum circuits of local gates. We first prove that faithfully preparing translation-invariant …

Ground state preparation is classically intractable for general Hamiltonians. On quantum
devices, shallow parametrized circuits can be effectively trained to obtain short-range …

We describe tensor network algorithms to optimize quantum circuits for adiabatic quantum
computing. To suppress diabatic transitions, we include counterdiabatic driving in the …

Quantum algorithms for ground-state energy estimation of chemical systems require a high-
quality initial state. However, initial state preparation is commonly either neglected entirely …

Adaptive quantum circuits, which combine local unitary gates, midcircuit measurements, and
feedforward operations, have recently emerged as a promising avenue for efficient state …

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