Improvement of Quantum Approximate Optimization Algorithm for Max–Cut Problems

Abstract

The objective of this short letter is to study the optimal partitioning of value stream networks into two classes so that the number of connections between them is maximized. Such kind of problems are frequently found in the design of different systems such as communication network configuration, and industrial applications in which certain topological characteristics enhance value–stream network resilience. The main interest is to improve the Max–Cut algorithm proposed in the quantum approximate optimization approach (QAOA), looking to promote a more efficient implementation than those already published. A discussion regarding linked problems as well as further research questions are also reviewed.

Convergence of digitized-counterdiabatic QAOA: circuit depth versus free parameters

“…Recently, many QAOA variants have emerged to enhance its performance [41,[49][50][51][52][53][54][55][56]. A non-exhaustive list of these variants includes: QAOA+ [57], which enhances the conventional QAOA by incorporating an extra problem-independent layer with multiple parameters; adaptive-bias QAOA [58], which adds local fields to the QAOA operators to decrease computation time; adaptive QAOA [13], a version of QAOA that iteratively selects mixers based on a systematic gradient criterion; recursive-QAOA [59], which aims to reduce the problem size by eliminating unnecessary qubits following a non-local scheme.…”

Quantum Dropout for Efficient Quantum Approximate Optimization Algorithm on Combinatorial Optimization Problems

“…The quantum approximate optimization algorithm (QAOA) is a hybrid quantum-classical variational algorithm designed to tackle ground-state problems, especially discrete combinatorial optimization problems [9][10][11][12][13][14][15][16][17][18][19][20][21][22]. It has been shown quite effective in many problems.…”