Applications such as simulating complicated quantum systems or solving large-scale linear
algebra problems are very challenging for classical computers, owing to the extremely high …

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 …

Modern quantum machine learning (QML) methods involve variationally optimizing a
parameterized quantum circuit on a training data set, and subsequently making predictions …

Parametrized quantum circuits serve as ansatze for solving variational problems and
provide a flexible paradigm for the programming of near-term quantum computers. Ideally …

Quantum computers can exploit a Hilbert space whose dimension increases exponentially
with the number of qubits. In experiment, quantum supremacy has recently been achieved …

Abstract Variational Quantum Algorithms (VQAs) have received considerable attention due
to their potential for achieving near-term quantum advantage. However, more work is …

The variational method is a versatile tool for classical simulation of a variety of quantum
systems. Great efforts have recently been devoted to its extension to quantum computing for …

We show that nonlinear problems including nonlinear partial differential equations can be
efficiently solved by variational quantum computing. We achieve this by utilizing multiple …

Among the many computational challenges faced across different disciplines, quantum-
mechanical systems pose some of the hardest ones and offer a natural playground for the …

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 …