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 expected to surpass the computational capabilities of classical
computers during this decade and have transformative impact on numerous industry sectors …

Nonlinear differential equations model diverse phenomena but are notoriously difficult to
solve. While there has been extensive previous work on efficient quantum algorithms for …

Quantum computers are designed to outperform standard computers by running quantum
algorithms. Areas in which quantum algorithms can be applied include cryptography, search …

We propose a quantum algorithm to solve systems of nonlinear differential equations. Using
a quantum feature map encoding, we define functions as expectation values of parametrized …

We present substantially generalized and improved quantum algorithms over prior work for
inhomogeneous linear and nonlinear ordinary differential equations (ODE). Specifically, we …

Solving linear systems of equations is a common problem that arises both on its own and as
a subroutine in more complex problems: given a matrix A and a vector b→, find a vector x→ …

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

Variational hybrid quantum-classical algorithms are promising candidates for near-term
implementation on quantum computers. In these algorithms, a quantum computer evaluates …

We construct quantum algorithms to compute the solution and/or physical observables of
nonlinear ordinary differential equations (ODEs) and nonlinear Hamilton-Jacobi equations …