In this paper, we have formulated quantum beetle antennae search (QBAS), a meta-heuristic optimization algorithm, and a variant of beetle antennae search (BAS). We apply it to portfolio selection, a well-known finance problem. Quantum computing is gaining immense popularity among the scientific community as it outsmarts the conventional computing in efficiency and speed. All the traditional computing algorithms are not directly compatible with quantum computers, for that we need to formulate their variants using the principles of quantum mechanics. In the portfolio optimization problem, we need to find the set of optimal stock such that it minimizes the risk factor and maximizes the mean-return of the portfolio. To the best of our knowledge, no quantum meta-heuristic algorithm has been applied to address this problem yet. We apply QBAS on real-world stock market data and compare the results with other meta-heuristic optimization algorithms. The results obtained show that the QBAS outperforms swarm algorithms such as the particle swarm optimization (PSO) and the genetic algorithm (GA).

在本文中，我们制定了量子甲虫天线搜索（QBAS），一种元启发式优化算法以及一种甲虫天线搜索（BAS）的变体。我们将其应用于投资组合选择，这是一个众所周知的财务问题。量子计算在效率和速度方面都超过了传统计算，因此在科学界越来越受欢迎。所有传统的计算算法都不与量子计算机直接兼容，因为我们需要使用量子力学原理来表示它们的变体。在投资组合优化问题中，我们需要找到一组最优库存，以使其最小化风险因素并最大化投资组合的平均回报。据我们所知，尚未应用量子元启发式算法来解决此问题。我们将QBAS应用于实际的股票市场数据，并将结果与​​其他元启发式优化算法进行比较。获得的结果表明，QBAS优于群算法，例如粒子群优化（PSO）和遗传算法（GA）。