  Fermionic ansatz state preparation is a critical subroutine in many quantum
algorithms such as Variational Quantum Eigensolver for quantum chemistry and
condensed matter applications. The shallowest circuit depth needed to prepare
Slater determinants and correlated states to date scale 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 ${\mathcal{O}}(d \log_2^2N)$ 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 $d{\ll}{\mathcal{O}}{\left(N / \log_2^2 N\right)}$
fermionic systems with larger basis sets on near-term quantum devices.
