The density matrix renormalization group (DMRG) is widely acknowledged as a highly
effective and accurate method for solving one-dimensional quantum many-body systems …

Variational quantum calculations have borrowed many tools and algorithms from the
machine learning community in the recent years. Leveraging great expressive power and …

Variational approaches, such as variational Monte Carlo (VMC) or the variational quantum
eigensolver (VQE), are powerful techniques to tackle the ground-state many-electron …

Efficient preparation of arbitrary entangled quantum states is crucial for quantum
computation. This is particularly important for noisy intermediate-scale quantum simulators …

We extend the recently introduced Clifford dressed Time-Dependent Variational Principle
(TDVP) to efficiently compute many-body wavefunction amplitudes in the computational …

Motivated by recent advances in digital quantum simulation and the overall prospective of
solving correlated many-electron problems using quantum algorithms, we design a gate …

In recent years, the remarkable progress in machine learning has revolutionized various
fields, including natural sciences, and in particular the physical sciences. One area where …

Quantum electronic systems are important for technological innovation and scientific
progress. Exploring entanglement and strong correlation in quantum systems can also lead …