# Quantum Approximate Optimization Algorithm for MaxCut: A Fermionic View

## TL;DR

This paper provides an analytical and numerical study of parameter setting strategies for the Quantum Approximate Optimization Algorithm (QAOA) applied to MaxCut, using a fermionic representation to simplify analysis and improve optimization.

## Contribution

It introduces a fermionic approach to analyze QAOA for MaxCut, deriving analytical expressions and reducing the complexity of parameter optimization.

## Key findings

- Analytical expressions for level-1 QAOA parameters for general graphs.
- Simplified numerical search for optimal parameters using fermionic representation.
- Identification of symmetries and a low-dimensional sub-manifold in the parameter landscape.

## Abstract

Farhi et al. recently proposed a class of quantum algorithms, the Quantum Approximate Optimization Algorithm (QAOA), for approximately solving combinatorial optimization problems. A level-p QAOA circuit consists of p steps; in each step a classical Hamiltonian, derived from the cost function, is applied followed by a mixing Hamiltonian. The 2p times for which these two Hamiltonians are applied are the parameters of the algorithm, which are to be optimized classically for the best performance. As p increases, parameter optimization becomes inefficient due to the curse of dimensionality. The success of the QAOA approach will depend, in part, on finding effective parameter-setting strategies. Here, we analytically and numerically study parameter setting for QAOA applied to MaxCut. For level-1 QAOA, we derive an analytical expression for a general graph. In principle, expressions for higher p could be derived, but the number of terms quickly becomes prohibitive. For a special case of MaxCut, the ring of disagrees, or the 1D antiferromagnetic ring, we provide an analysis for arbitrarily high level. Using a fermionic representation, the evolution of the system under QAOA translates into quantum control of an ensemble of independent spins. This treatment enables us to obtain analytical expressions for the performance of QAOA for any p. It also greatly simplifies numerical search for the optimal values of the parameters. By exploring symmetries, we identify a lower-dimensional sub-manifold of interest; the search effort can be accordingly reduced. This analysis also explains an observed symmetry in the optimal parameter values. Further, we numerically investigate the parameter landscape and show that it is a simple one in the sense of having no local optima.