Constrained Counterdiabatic Quantum Approximate Optimization Algorithm for Portfolio Optimization

TL;DR

This paper presents a novel constrained counterdiabatic extension of QAOA, called CCD-QAOA, which improves portfolio optimization performance by incorporating approximate adiabatic gauge potentials into the variational ansatz.

Contribution

The paper introduces CCD-QAOA, a new method that enhances constrained portfolio optimization by integrating counterdiabatic driving into QAOA, outperforming existing variants.

Findings

CCD-QAOA achieves higher approximation ratios than standard QAOA variants.

Numerical simulations confirm improved performance under realistic constraints.

Benchmarking shows consistent benefits at fixed QAOA depth.

Abstract

We introduce a counterdiabatic (CD) extension of the Quantum Approximate Optimization Algorithm (QAOA) for constrained portfolio optimization. By incorporating approximate adiabatic gauge potentials generated from nested commutators of the Ising-type portfolio problem Hamiltonian and the Hamming weight-preserving XY mixer Hamiltonian into our variational ansatz, the resulting Constrained Counterdiabatic QAOA (CCD-QAOA) achieves improved optimization performance under realistic budget and risk constraints. Benchmarking against standard XY-mixer QAOA, Grover-mixer QAOA, and penalty-based QAOA formulations, our numerical simulations demonstrate that, for a fixed QAOA depth, our CCD-QAOA approach consistently results in better approximation ratios.