A universal fault-tolerant quantum computer that can efficiently solve problems such as
integer factorization and unstructured database search requires millions of qubits with low …

Machine learning is a branch of artificial intelligence, and it has been widely used in many
science and engineering areas, such as data mining, natural language processing …

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 …

Quantum machine learning is one of the most promising applications of quantum computing
in the noisy intermediate-scale quantum (NISQ) era. We propose a quantum convolutional …

Imaginary-time evolution (ITE) on a quantum computer is a promising formalism for
obtaining the ground state of a quantum system. The probabilistic ITE (PITE) exploits …

A novel quantum algorithm for solving advection–diffusion equation by the lattice Boltzmann
method is proposed. The presented quantum algorithm is composed of two major segments …

Realizing nonunitary transformations on unitary-gate-based quantum devices is critically
important for simulating a variety of physical problems, including open quantum systems and …

Quantum computers are predicted to outperform classical ones for solving partial differential
equations, perhaps exponentially. Here we consider a prototypical PDE—the heat equation …

Principal component analysis (PCA) is a widely applied but rather time-consuming tool in
machine learning techniques. In 2014, Lloyd, Mohseni, and Rebentrost proposed a quantum …

In this paper, a new algorithm for solving the Navier–Stokes equations (NSE) on a quantum
device is presented. For the fluid flow equations, the stream function-vorticity formulation is …