Quantum programming languages are essential to translate ideas into instructions that can
be executed by a quantum computer. Not only are they crucial to the programming of …

Reversible logic circuits have been historically motivated by theoretical research in low-
power electronics as well as practical improvement of bit manipulation transforms in …

Quantum state preparation is an important subroutine for quantum computing. We show that
any n-qubit quantum state can be prepared with a Θ (n)-depth circuit using only single-and …

Artificial neural networks are the heart of machine learning algorithms and artificial
intelligence. Historically, the simplest implementation of an artificial neuron traces back to …

The variational quantum eigensolver is one of the most promising approaches for
performing chemistry simulations using noisy intermediate-scale quantum (NISQ) …

Electron transport in realistic physical and chemical systems often involves the nontrivial
exchange of energy with a large environment, requiring the definition and treatment of open …

The calculation time for the energy of atoms and molecules scales exponentially with system
size on a classical computer but polynomially using quantum algorithms. We demonstrate …

The quantum state preparation problem aims to prepare an-qubit quantum state from the
initial state, for a given unit vector with. The problem is of fundamental importance in …

Quantum circuit depth minimization is critical for practical applications of circuit-based
quantum computation. In this work, we present a systematic procedure to decompose …

Generative modeling using samples drawn from the probability distribution constitutes a
powerful approach for unsupervised machine learning. Quantum mechanical systems can …