Investigating Quantum Approximate Optimization Algorithms under Bang-bang Protocols

Abstract

The quantum approximate optimization algorithm (QAOA) is widely seen as a possible usage of noisy intermediate-scale quantum (NISQ) devices. We analyze the algorithm as a bang-bang protocol with fixed total time and a randomized greedy optimization scheme. We investigate the performance of bang-bang QAOA on MAX-2-SAT, finding the appearance of phase transitions with respect to the total time. As the total time increases, the optimal bang-bang protocol experiences a number of jumps and plateaus in performance, which match up with an increasing number of switches in the standard QAOA formulation. At large times, it becomes more difficult to find a globally optimal bang-bang protocol and performances suffer. We also investigate the effects of changing the initial conditions of the randomized optimization algorithm and see that better local optima can be found by using an adiabatic initialization.

Open source code of this paper is available: github.com/google-research/google-research/tree/master/bangbang_qaoa

References in corpus (5)

• Quantum Annealing in the Transverse Ising Model
• From the Quantum Approximate Optimization Algorithm to a Quantum Alternating Operator Ansatz
• Optimizing Variational Quantum Algorithms using Pontryagin's Minimum Principle
• Quantum Optimization with a Novel Gibbs Objective Function and Ansatz Architecture Search
• Glassy Phase of Optimal Quantum Control

Cited by in corpus (10)

• A Review on Quantum Approximate Optimization Algorithm and its Variants
• Quantum annealing initialization of the quantum approximate optimization algorithm
• Parameters Fixing Strategy for Quantum Approximate Optimization Algorithm
• Quantifying the efficiency of state preparation via quantum variational eigensolvers
• Recursive greedy initialization of the quantum approximate optimization algorithm with guaranteed improvement
• Squeezing and quantum approximate optimization
• Binary Control Pulse Optimization for Quantum Systems
• Automatic Depth Optimization for Quantum Approximate Optimization Algorithm
• Switching Time Optimization for Binary Quantum Optimal Control
• Quantum Alternating Operator Ansatz for the Preparation and Detection of Long-Lived Singlet States in NMR