Quantum machine learning is a rapidly growing field at the intersection of quantum
technology and artificial intelligence. This review provides a two-fold overview of several key …

Quantum computers are known to provide an exponential advantage over classical
computers for the solution of linear differential equations in high-dimensional spaces. Here …

Quantum kernel methods show promise for accelerating data analysis by efficiently learning
relationships between input data points that have been encoded into an exponentially large …

A variational quantum algorithm for numerically solving partial differential equations (PDEs)
on a quantum computer was proposed by Lubasch et al.[Phys. Rev. A 101, 010301 …

Nonlinear differential equations exhibit rich phenomena in many fields but are notoriously
challenging to solve. Recently, Liu et al.(in: Proceedings of the National Academy of …

Quantifying how far the output of a learning algorithm is from its target is an essential task in
machine learning. However, in quantum settings, the loss landscapes of commonly used …

Several tasks in quantum-information processing involve quantum learning. For example,
quantum sensing, quantum machine learning, and quantum-computer calibration involve …

We study how parallelism can speed up quantum simulation. A parallel quantum algorithm
is proposed for simulating the dynamics of a large class of Hamiltonians with good sparse …

Group convolutions and cross-correlations, which are equivariant to the actions of group
elements, are commonly used to analyze or take advantage of symmetries inherent in a …

State-of-the-art cosmological simulations on classical computers are limited by time, energy,
and memory usage. Quantum computers can perform some calculations exponentially faster …