Depth Optimized Ansatz Circuit in QAOA for Max-Cut

Abstract

While a Quantum Approximate Optimization Algorithm (QAOA) is intended to provide a quantum advantage in finding approximate solutions to combinatorial optimization problems, noise in the system is a hurdle in exploiting its full potential. Several error mitigation techniques have been studied to lessen the effect of noise on this algorithm. Recently, Majumdar et al. proposed a Depth First Search (DFS) based method to reduce n − 1 CNOT gates in the ansatz design of QAOA for finding Max-Cut in a graph G = (V, E), |V | = n. However, this method tends to increase the depth of the circuit, making it more prone to relaxation error. The depth of the circuit is proportional to the height of the DFS tree, which can be n − 1 in the worst case. In this paper, we propose an O(∆ • n 2 ) greedy heuristic algorithm, where ∆ is the maximum degree of the graph, that finds a spanning tree of lower height, thus reducing the overall depth of the circuit while still retaining the n − 1 reduction in the number of CNOT gates needed in the ansatz. We numerically show that this algorithm achieves 10 times increase in the probability of success for each iteration of QAOA for Max-Cut. We further show that although the average depth of the circuit produced by this heuristic algorithm still grows linearly with n, our algorithm reduces the slope of the linear increase from 1 to 0.11.

Filtering variational quantum algorithms for combinatorial optimization

“…Local cost functions and shallow ansatz circuits can be used to avoid barren plateaus [46]. F-VQE might also benefit from the original QAOA ansatz [12] or generalizations thereof such as the Hamiltonian variational ansatz [47,48], the quantum alternating operator ansatz [49], the hardware-efficient mixer-phaser ansatz [50] or the depth optimized QAOA ansatz [51]. The ansatz can be specifically selected to minimize experimental noise on quantum hardware [52].…”

An Expressive Ansatz for Low-Depth Quantum Optimisation

“…Several variants of the original QAOA algorithm have been developed, each with different operators and initial states [14][15][16][17][18][19][20][21][22][23][24][25] or different objective functions for tuning the variational parameters [26,27]. Depth-reduction techniques [28,29] or methods like circuit cutting [30,31] that optimise QAOA circuits while taking into account quantum hardware limitations; as well as classical aspects such as hyper-parameter optimisation and exploitation of problem structure, have been studied as well [18,19,[32][33][34][35][36][37][38][39]. However, one key drawback of realistic QAOA implementations is the need for deep quantum circuits with many qubits [40][41][42][43][44][45].…”