Digitized-counterdiabatic quantum approximate optimization algorithm

Abstract

The quantum approximate optimization algorithm (QAOA) has proved to be an effective classical-quantum algorithm serving multiple purposes, from solving combinatorial optimization problems to finding the ground state of many-body quantum systems. Since QAOA is an ansatz-dependent algorithm, there is always a need to design ansatz for better optimization. To this end, we propose a digitized version of QAOA enhanced via the use of shortcuts to adiabaticity. Specifically, we use a counterdiabatic (CD) driving term to design a better ansatz, along with the Hamiltonian and mixing terms, enhancing the global performance. We apply our digitizedcounterdiabatic QAOA to Ising models, classical optimization problems, and the P-spin model, demonstrating that it outperforms standard QAOA in all cases we study.

Scaling of the quantum approximate optimization algorithm on superconducting qubit based hardware

“…The execution time estimation also depends on the variant of QAOA. For example, counteradiabatic driving reduces p by adding counteradiabatic gates and an extra parameter at each layer [91,92] while Recursive-QAOA [61] will also produce different execution time estimates. However, the extra gates and different number of parameters to optimize may impact the execution time as well.…”

Portfolio Optimization with Digitized-Counterdiabatic Quantum Algorithms

“…Over the years, many modifications have been reported in the standard QAOA [51][52][53]. Among them, the addition of terms using counterdiabatic (CD) driving has shown significant improvements in finding ground states of manybody Hamiltonians [29][30][31]. One of the algorithms following the same principle is the digitized-counterdiabatic quantum approximate optimization algorithm (DC-QAOA).…”

“…Recently, Hegade et al showed the advantage of CD driving in DAdQC methods [24], which have shown interesting improvements in many-body ground state preparations and adiabatic quantum factorization problems [25]. It was also studied that the inclusion of these approximate counterdiabatic terms could enhance the performance of QAOA while solving combinatorial optimization problems and state preparation of many-body ground states [29][30][31]. This article considers the advantages of digitized-counterdiabatic quantum computing (DCQC) and digitized-counterdiabatic quantum approximate optimization algorithms (DC-QAOA) in financial applications.…”

Shortcuts to Quantum Approximate Optimization Algorithm

“…Recently, there is a research indicates QAOA is at least counterdiabatic and has a better performance than finite time adiabatic evolution [4]. And there is also an effort to add a conterdiabatic term to the ansatz of QAOA to reduce the quantum circuit depth [15].…”