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 . Inspired by data-loading circuits developed for quantum machine learning, we propose an alternate paradigm that provides shallower, yet scalable, two-qubit gate-depth circuits to prepare such states with fermions, offering a subexponential reduction in over existing approaches in second quantization, enabling high-accuracy studies of fermionic systems with larger basis sets on near-term quantum devices.
- Received 25 January 2023
- Revised 23 May 2023
- Accepted 27 July 2023
DOI:https://doi.org/10.1103/PhysRevA.108.022416
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