Quantum Approximate Optimization Algorithm with Cat Qubits
- URL: http://arxiv.org/abs/2305.05556v1
- Date: Tue, 9 May 2023 15:44:52 GMT
- Title: Quantum Approximate Optimization Algorithm with Cat Qubits
- Authors: Pontus Vikst{\aa}l, Laura Garc\'ia-\'Alvarez, Shruti Puri, Giulia
Ferrini
- Abstract summary: We numerically simulate solving MaxCut problems using QAOA with cat qubits.
We show that running QAOA with cat qubits increases the approximation ratio for random instances of MaxCut with respect to qubits encoded into two-level systems.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The Quantum Approximate Optimization Algorithm (QAOA) -- one of the leading
algorithms for applications on intermediate-scale quantum processors -- is
designed to provide approximate solutions to combinatorial optimization
problems with shallow quantum circuits. Here, we study QAOA implementations
with cat qubits, using coherent states with opposite amplitudes. The dominant
noise mechanism, i.e., photon losses, results in $Z$-biased noise with this
encoding. We consider in particular an implementation with Kerr resonators. We
numerically simulate solving MaxCut problems using QAOA with cat qubits by
simulating the required gates sequence acting on the Kerr non-linear
resonators, and compare to the case of standard qubits, encoded in ideal
two-level systems, in the presence of single-photon loss. Our results show that
running QAOA with cat qubits increases the approximation ratio for random
instances of MaxCut with respect to qubits encoded into two-level systems.
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