At the intersection of machine learning and quantum computing, quantum machine learning
has the potential of accelerating data analysis, especially for quantum data, with …

The recent advent of noisy intermediate-scale quantum devices, especially near-term
quantum computers, has sparked extensive research efforts concerned with their possible …

Abstract Variational Quantum Algorithms (VQAs) may be a path to quantum advantage on
Noisy Intermediate-Scale Quantum (NISQ) computers. A natural question is whether noise …

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 propose VQE circuit fabrics with advantageous properties for the simulation of strongly
correlated ground and excited states of molecules and materials under the Jordan–Wigner …

Abstract Variational Quantum Algorithms (VQAs) are often viewed as the best hope for near-
term quantum advantage. However, recent studies have shown that noise can severely limit …

To harness the potential of noisy intermediate-scale quantum devices, it is paramount to find
the best type of circuits to run hybrid quantum-classical algorithms. Key candidates are …

The quantum Fisher information (QFI) quantifies the ultimate precision of estimating a
parameter from a quantum state and can be regarded as a reliability measure of a quantum …

We develop computationally affordable and encoding independent gradient evaluation
procedures for unitary coupled-cluster type operators, applicable on quantum computers …