Superconducting qubits are leading candidates in the race to build a quantum computer
capable of realizing computations beyond the reach of modern supercomputers. The …

During the last ten years, superconducting circuits have passed from being interesting
physical devices to becoming contenders for near-future useful and scalable quantum …

Classical machine learning (ML) provides a potentially powerful approach to solving
challenging quantum many-body problems in physics and chemistry. However, the …

Quantum annealing is a computing paradigm that has the ambitious goal of efficiently
solving large-scale combinatorial optimization problems of practical importance. However …

Abstract Noisy Intermediate-Scale Quantum (NISQ) technology will be available in the near
future. Quantum computers with 50-100 qubits may be able to perform tasks which surpass …

The field of quantum algorithms aims to find ways to speed up the solution of computational
problems by using a quantum computer. A key milestone in this field will be when a …

Quantum simulations of the dynamics of QCD have been limited by the complexities of
map** the continuous gauge fields onto quantum computers. By parametrizing the gauge …

A core technology that has emerged from the artificial intelligence revolution is the recurrent
neural network (RNN). Its unique sequence-based architecture provides a tractable …

Inspired by the success of Boltzmann machines based on classical Boltzmann distribution,
we propose a new machine-learning approach based on quantum Boltzmann distribution of …

The Quantum Approximate Optimization Algorithm (QAOA) is designed to run on a gate
model quantum computer and has shallow depth. It takes as input a combinatorial …