Quantum computing technologies are making steady progress. This has opened new
opportunities for tackling problems whose complexity prevents their description on classical …

Computational models are an essential tool for the design, characterization, and discovery
of novel materials. Computationally hard tasks in materials science stretch the limits of …

We present an algorithm that uses block encoding on a quantum computer to exactly
construct a Krylov space, which can be used as the basis for the Lanczos method to estimate …

Simulation of electronic structure is one of the most promising applications on noisy
intermediate-scale quantum (NISQ) era devices. However, NISQ devices suffer from a …

Quantum subspace diagonalization methods are an exciting new class of algorithms for
solving large-scale eigenvalue problems using quantum computers. Unfortunately, these …

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 …

We introduce a multi-modal, multi-level quantum complex exponential least squares (MM-
QCELS) method to simultaneously estimate multiple eigenvalues of a quantum Hamiltonian …

We propose a computational protocol for quantum simulations of fermionic Hamiltonians on
a quantum computer, enabling calculations on spin defect systems which were previously …

Quantum subspace methods (QSMs) are a class of quantum computing algorithms where
the time-independent Schrödinger equation for a quantum system is projected onto a …

We propose a quantum inverse algorithm (QInverse) to directly determine general
eigenstates by repeatedly applying the inverse power of a shifted Hamiltonian to an arbitrary …