Algorithm for initializing a generalized fermionic Gaussian state on a
quantum computer
- URL: http://arxiv.org/abs/2105.13047v2
- Date: Tue, 3 Aug 2021 08:24:50 GMT
- Title: Algorithm for initializing a generalized fermionic Gaussian state on a
quantum computer
- Authors: Michael P. Kaicher, Simon B. J\"ager, Frank K. Wilhelm
- Abstract summary: We present explicit expressions for the central piece of a variational method developed by Shi et al.
We derive iterative analytical expressions for the evaluation of expectation values of products of fermionic creation and subroutine operators.
We present a simple gradient-descent-based algorithm that can be used as an optimization in combination with imaginary time evolution.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We present explicit expressions for the central piece of a variational method
developed by Shi et al. which extends variational wave functions that are
efficiently computable on classical computers beyond mean-field to generalized
Gaussian states [1]. In particular, we derive iterative analytical expressions
for the evaluation of expectation values of products of fermionic creation and
annihilation operators in a Grassmann variable-free representation. Using this
result we find a closed expression for the energy functional and its gradient
of a general fermionic quantum many-body Hamiltonian. We present a simple
gradient-descent-based algorithm that can be used as an optimization subroutine
in combination with imaginary time evolution, which by construction guarantees
a monotonic decrease of the energy in each iteration step. Due to the
simplicity of the quantum circuit implementing the variational state Ansatz,
the results of the algorithms discussed here and in [1] could serve as an
improved, beyond mean-field initial state in quantum computation.
[1] Tao Shi, Eugene Demler, and J. Ignacio Cirac. Variational study of
fermionic and bosonic systems with non-gaussian states: Theory and
applications. Annals of Physics, 390: 245-302, 2018.
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