Theory for equivariant quantum neural networks
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 …
face trainability and generalization issues. Inspired by a similar problem, recent …
Trainability barriers and opportunities in quantum generative modeling
Quantum generative models provide inherently efficient sampling strategies and thus show
promise for achieving an advantage using quantum hardware. In this work, we investigate …
promise for achieving an advantage using quantum hardware. In this work, we investigate …
Problem-dependent power of quantum neural networks on multiclass classification
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 …
physical world, but their advantages and limitations are not fully understood. Some QNNs …
Quantum computing for fusion energy science applications
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 …
capabilities for fusion energy science applications. We begin with a brief tutorial on both …
Non-linear transformations of quantum amplitudes: Exponential improvement, generalization, and applications
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 …
computational problems. In this work, we present a framework for applying a general class of …
Censorship of quantum resources in quantum networks
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 …
such networks it may be a feature that certain resources are available only to priority users …
Koopman von Neumann mechanics and the Koopman representation: A perspective on solving nonlinear dynamical systems with quantum computers
A number of recent studies have proposed that linear representations are appropriate for
solving nonlinear dynamical systems with quantum computers, which fundamentally act …
solving nonlinear dynamical systems with quantum computers, which fundamentally act …
Problem-Dependent Power of Quantum Neural Networks on Multi-Class Classification
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 …
physical world, but their advantages and limitations are not fully understood. Some QNNs …
Conditions for a quadratic quantum speedup in nonlinear transforms with applications to energy contract pricing
Computing nonlinear functions over multilinear forms is a general problem with applications
in risk analysis. For instance in the domain of energy economics, accurate and timely risk …
in risk analysis. For instance in the domain of energy economics, accurate and timely risk …
Gate-based quantum simulation of Gaussian bosonic circuits on exponentially many modes
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 …
action of a Gaussian Bosonic (GB) circuit on a state over $2^ n $ modes. Specifically, we …