Abstract
Fermionic Ansatz state preparation is a critical subroutine in many quantum algorithms such as the variational quantum eigensolver for quantum chemistry and condensed-matter applications. The shallowest circuit depth needed to prepare Slater determinants and correlated states to date scales at least linearly with respect to the system size N. Inspired by data-loading circuits developed for quantum machine learning, we propose an alternate paradigm that provides shallower, yet scalable, O(d log22 N) two-qubit gate-depth circuits to prepare such states with d fermions, offering a subexponential reduction in N over existing approaches in second quantization, enabling high-accuracy studies of d & DLANGBRAC; O(N/ log22 N) fermionic systems with larger basis sets on near-term quantum devices.