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

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

Tensor networks (TNs) are a family of computational methods built on graph-structured
factorizations of large tensors, which have long represented state-of-the-art methods for the …

Quantum state preparation is a vital routine in many quantum algorithms, including solution
of linear systems of equations, Monte Carlo simulations, quantum sampling, and machine …

It is a critical challenge to simultaneously achieve high interpretability and high efficiency
with the current schemes of deep machine learning (ML). The tensor network (TN), a well …

A comprehensive coverage of the state-of-the-art in quantum machine learning (QML)
methodologies, with a unique perspective on their applications for wireless communications …

While recent breakthroughs have proven the ability of noisy intermediate-scale quantum
(NISQ) devices to achieve quantum advantage in classically-intractable sampling tasks, the …

We introduce an quantum-classical hybrid algorithm, named automatic quantum circuit
encoding (AQCE), that is designed to encode an arbitrary quantum state| Ψ〉 onto an …

Running quantum algorithms often involves implementing complex quantum circuits with
such a large number of multi-qubit gates that the challenge of tackling practical applications …

We propose a variational approach for preparing entangled quantum states on quantum
computers. The methodology involves training a unitary operation to match with a target …