Quantum neural network architectures that have little to no inductive biases are known to
face trainability and generalization issues. Inspired by a similar problem, recent …

Quantum generative models provide inherently efficient sampling strategies and thus show
promise for achieving an advantage using quantum hardware. In this work, we investigate …

Quantum neural networks (QNNs) have become an important tool for understanding the
physical world, but their advantages and limitations are not fully understood. Some QNNs …

Quantum algorithms manipulate the amplitudes of quantum states to find solutions to
computational problems. In this work, we present a framework for applying a general class of …

This is a review of recent research exploring and extending present-day quantum computing
capabilities for fusion energy science applications. We begin with a brief tutorial on both …

We introduce a framework for simulating, on an (n+ 1)-qubit quantum computer, the action of
a Gaussian bosonic (GB) circuit on a state over 2 n modes. Specifically, we encode the initial …

A number of recent studies have proposed that linear representations are appropriate for
solving nonlinear dynamical systems with quantum computers, which fundamentally act …

We may soon see agencies offering public access to quantum communication networks. In
such networks it may be a feature that certain resources are available only to priority users …

We present an algorithm that uses a single ancilla qubit that can evolve nonlinearly, and
show how to use it to efficiently solve generic nonlinear Schr\" odinger equations, including …

We investigate the limitations of quantum computers for solving nonlinear dynamical
systems. In particular, we tighten the worst-case bounds of the quantum Carleman …