Performance Comparison of QAOA Mixers for Ternary Portfolio Optimization

• URL: http://arxiv.org/abs/2602.21562v1
• Date: Wed, 25 Feb 2026 04:35:26 GMT
• Title: Performance Comparison of QAOA Mixers for Ternary Portfolio Optimization
• Abstract summary: The Quantum Approximate Optimization Algorithm (QAOA) is a quantum algorithm proposed for Noisy Intermediate-Scale Quantum (NISQ) devices.<n>We introduce noise based on a depolarizing channel to investigate the behavior of the algorithm in realistic environments.<n>The results show that while XY Mixers exhibit superiority in noiseless settings, their advantage degrades in noisy environments.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Quantum Approximate Optimization Algorithm (QAOA) is a quantum algorithm proposed for Noisy Intermediate-Scale Quantum (NISQ) devices and is regarded as a promising approach to combinatorial optimization problems, with potential applications in the financial sector. In this study, we apply QAOA to the portfolio optimization problem, which is one of the central challenges in financial engineering. A portfolio consists of a combination of multiple assets, and the portfolio optimization problem aims to determine the optimal asset allocation by balancing expected return and risk. In the context of quantum optimization, portfolio optimization is often formulated using discrete variables. Unlike conventional binary formulations, we consider a ternary portfolio optimization problem that accounts for three states-holding, not holding, and short selling-and compare its performance using different mixer operators. Specifically, we implement QAOA with the standard mixer and several XY Mixers (XY Ring, XY Parity Ring, XY Full, and QAMPA), and conducted simulations using real data based on the German stock index (DAX 30) for portfolios consisting of 5 and 8 assets. Furthermore, we introduce noise based on a depolarizing channel to investigate the behavior of the algorithm in realistic environments. The results show that while XY Mixers exhibit superiority in noiseless settings, their advantage degrades in noisy environments, and the optimal choice of mixer depends on both the number of QAOA depths and the noise strength.

Related papers

• Integrated Prediction and Multi-period Portfolio Optimization [29.582959310549594]
  Multi-period portfolio optimization accounts for transaction costs, path-dependent risks, and the intertemporal structure of trading decisions.<n>This paper introduces IPMO, a model for multi-period mean-variance portfolio optimization with turnover penalties.<n>For scalability, we introduce a mirror-descent fixed-point (MDFP) differentiation scheme that avoids factorizing the Karush-Kuhn-Tucker (KKT) systems.
  arXiv  Detail & Related papers  (2025-12-12T04:31:22Z)
• Hot-Starting Quantum Portfolio Optimization [39.916647837440316]
  Combinatorial optimization with a smooth and convex objective function arises naturally in applications such as discrete mean-variance portfolio optimization.<n>We introduce a novel approach that restricts the search space to discrete solutions in the vicinity of the continuous optimum by constructing a compact Hilbert space.<n> Experiments on software solvers and a D-Wave Advantage quantum annealer demonstrate that our method outperforms state-of-the-art techniques.
  arXiv  Detail & Related papers  (2025-10-13T08:47:43Z)
• Achieving High-Quality Portfolio Optimization with the Variational Quantum Eigensolver [3.361857599742077]
  Portfolio optimization is a fundamental problem in finance that aims to determine the optimal allocation of assets within a portfolio to maximize returns while minimizing risk.<n>Quantum computing offers the potential to solve such problems more efficiently than classical methods.<n>We employ the Variational Quantum Eigensolver (VQE) to address the portfolio optimization problem.
  arXiv  Detail & Related papers  (2025-08-26T03:03:36Z)
• Quantifying the advantages of applying quantum approximate algorithms to portfolio optimisation [0.0]
  We present an end-to-end quantum approximate optimisation algorithm (QAOA) to solve the discrete global minimum variance portfolio model. This model finds a portfolio of risky assets with the lowest possible risk contingent on the number of traded assets being discrete.
  arXiv  Detail & Related papers  (2024-10-21T17:59:05Z)
• MG-Net: Learn to Customize QAOA with Circuit Depth Awareness [51.78425545377329]
  Quantum Approximate Optimization Algorithm (QAOA) and its variants exhibit immense potential in tackling optimization challenges. The requisite circuit depth for satisfactory performance is problem-specific and often exceeds the maximum capability of current quantum devices. We introduce the Mixer Generator Network (MG-Net), a unified deep learning framework adept at dynamically formulating optimal mixer Hamiltonians.
  arXiv  Detail & Related papers  (2024-09-27T12:28:18Z)
• Approximating under the Influence of Quantum Noise and Compute Power [3.0302054726041017]
  The quantum approximate optimisation algorithm (QAOA) is at the core of many scenarios that aim to combine the power of quantum computers and classical high-performance computing appliances for optimisation. We study factors that affect solution quality and temporal behaviour of four QAOA variants using comprehensive density-matrix-based simulations. Our results, accompanied by a comprehensive reproduction package, show strong differences between QAOA variants that can be pinpointed to narrow and specific effects.
  arXiv  Detail & Related papers  (2024-08-05T07:48:49Z)
• Bayesian Parameterized Quantum Circuit Optimization (BPQCO): A task and hardware-dependent approach [49.89480853499917]
  Variational quantum algorithms (VQA) have emerged as a promising quantum alternative for solving optimization and machine learning problems. In this paper, we experimentally demonstrate the influence of the circuit design on the performance obtained for two classification problems. We also study the degradation of the obtained circuits in the presence of noise when simulating real quantum computers.
  arXiv  Detail & Related papers  (2024-04-17T11:00:12Z)
• Federated Conditional Stochastic Optimization [110.513884892319]
  Conditional optimization has found in a wide range of machine learning tasks, such as in-variant learning tasks, AUPRC, andAML. This paper proposes algorithms for distributed federated learning.
  arXiv  Detail & Related papers  (2023-10-04T01:47:37Z)
• QAOA Performance in Noisy Devices: The Effect of Classical Optimizers and Ansatz Depth [0.32985979395737786]
  The Quantum Approximate Optimization Algorithm (QAOA) is a variational quantum algorithm for Near-term Intermediate-Scale Quantum computers (NISQ) This paper presents an investigation into the impact realistic noise on the classical vectors. We find that while there is no significant difference in the performance of classicals in a state simulation, the Adam and AMSGrads perform best in the presence of shot noise.
  arXiv  Detail & Related papers  (2023-07-19T17:22:44Z)
• Benchmarking the performance of portfolio optimization with QAOA [0.12110562441696866]
  We present a detailed study of portfolio optimization using different versions of the quantum approximate optimization algorithm (QAOA) We will discuss several possible choices of the variational form and of different classical algorithms for finding the corresponding optimized parameters. We also analyze the influence of statistical sampling errors (due to a finite number of shots) and gate and readout errors (due to imperfect quantum hardware)
  arXiv  Detail & Related papers  (2022-07-21T15:53:46Z)
• Scaling Quantum Approximate Optimization on Near-term Hardware [49.94954584453379]
  We quantify scaling of the expected resource requirements by optimized circuits for hardware architectures with varying levels of connectivity. We show the number of measurements, and hence total time to synthesizing solution, grows exponentially in problem size and problem graph degree. These problems may be alleviated by increasing hardware connectivity or by recently proposed modifications to the QAOA that achieve higher performance with fewer circuit layers.
  arXiv  Detail & Related papers  (2022-01-06T21:02:30Z)
• Weighted QMIX: Expanding Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning [66.94149388181343]
  We present a new version of a popular $Q$-learning algorithm for MARL. We show that it can recover the optimal policy even with access to $Q*$. We also demonstrate improved performance on predator-prey and challenging multi-agent StarCraft benchmark tasks.
  arXiv  Detail & Related papers  (2020-06-18T18:34:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.