The variational quantum eigensolver (VQE) is a method that uses a hybrid quantum-
classical computational approach to find eigenvalues of a Hamiltonian. VQE has been …

Quantum machine learning, which involves running machine learning algorithms on
quantum devices, has garnered significant attention in both academic and business circles …

Quantum machine learning (QML) models are aimed at learning from data encoded in
quantum states. Recently, it has been shown that models with little to no inductive biases (ie …

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 …

The Fermi-Hubbard model is of fundamental importance in condensed-matter physics, yet is
extremely challenging to solve numerically. Finding the ground state of the Hubbard model …

We compare the bfgs optimizer, adam and NatGrad in the context of vqes. We systematically
analyze their performance on the qaoa ansatz for the transverse field Ising and the XXZ …

We propose a quantum-classical hybrid algorithm of the power method, here dubbed as the
quantum power method, to evaluate H^ n| ψ⟩ with quantum computers, where n is a non …

We simulate the effects of different types of noise in state preparation circuits of variational
quantum algorithms. We first use a variational quantum eigensolver to find the ground state …

We explore the possibility to perform symmetry restoration with the variation after projection
technique on a quantum computer followed by additional postprocessing. The final goal is to …

Adaptive quantum variational algorithms are particularly promising for simulating strongly
correlated systems on near-term quantum hardware, but they are not yet viable due, in large …