aiqbench analysis
This study demonstrates that practical performance of variational quantum algorithms for dynamic portfolio optimization is critically dependent on specific design choices, including adaptive CVaR scheduling, hybrid optimizer strategies, and hardware-aware ansatz layouts. While the research does not yet achieve quantum advantage over classical exact solvers, it confirms that optimizing qubit connectivity and minimizing routing overhead significantly improves objective values and risk metrics on current QPUs. These findings provide a concrete roadmap for engineers aiming to maximize utility from near-term quantum hardware in financial applications.
VQA for Dynamic Portfolio Optimization: Sampling Strategies, Optimizer Scheduling, and Hardware-Aware Ansatz Design
arXiv:2606.10098v1 Announce Type: cross Abstract: Variational quantum algorithms are increasingly explored for optimization problems at scales relevant to near-term quantum devices. Their practical performance depends strongly on design choices such as the sampling objective, classical optimizer, and ansatz layout before and after hardware transpilation. We study these factors for dynamic portfolio optimization, a multi-period financial problem balancing return, risk, transaction costs, cash-interest effects, and constraints. Using a sampling-based VQA framework on a 150-qubit dynamic portfolio instance, we evaluate several components of the optimization workflow. We propose a specific adaptive CVaR schedule that gradually tightens the sampled tail used for optimization, together with a two-stage optimizer combining global exploration with Particle Swarm Optimization and local refinement with the Nakanishi-Fujii-Todo optimizer. We also study ansatz depth and sequential growth strategies. Finally, we introduce two hardware-aware ansatz-layout modifications: a data-guided colored layout that assigns correlated variables to qubits connected by entangling gates, and a heavy-hex-native deep-chain layout designed to increase native two-qubit interaction depth without additional routing overhead after transpilation. Simulator studies select CVaR, optimizer, and depth configurations, while the ansatz comparison is performed on the ibm_quebec QPU. The results show that sampling strategy, optimizer scheduling, and hardware-aware layout design materially affect performance. In the reported QPU layout comparison, the proposed heavy-hex-native deep-chain layout achieves the best final objective value and CVaR-tail performance among the tested layouts. Although we do not observe quantum advantage over a state-of-the-art exact classical solver, our results provide practical guidance for improving VQA performance on near-term hardware.
Abstract:Variational quantum algorithms are increasingly explored for optimization problems at scales relevant to near-term quantum devices. Their practical performance depends strongly on design choices such as the sampling objective, classical optimizer, and ansatz layout before and after hardware transpilation. We study these factors for dynamic portfolio optimization, a multi-period financial problem balancing return, risk, transaction costs, cash-interest effects, and constraints. Using a sampling-based VQA framework on a 150-qubit dynamic portfolio instance, we evaluate several components of the optimization workflow. We propose a specific adaptive CVaR schedule that gradually tightens the sampled tail used for optimization, together with a two-stage optimizer combining global exploration with Particle Swarm Optimization and local refinement with the Nakanishi-Fujii-Todo optimizer. We also study ansatz depth and sequential growth strategies. Finally, we introduce two hardware-aware ansatz-layout modifications: a data-guided colored layout that assigns correlated variables to qubits connected by entangling gates, and a heavy-hex-native deep-chain layout designed to increase native two-qubit interaction depth without additional routing overhead after transpilation. Simulator studies select CVaR, optimizer, and depth configurations, while the ansatz comparison is performed on the ibm_quebec QPU. The results show that sampling strategy, optimizer scheduling, and hardware-aware layout design materially affect performance. In the reported QPU layout comparison, the proposed heavy-hex-native deep-chain layout achieves the best final objective value and CVaR-tail performance among the tested layouts. Although we do not observe quantum advantage over a state-of-the-art exact classical solver, our results provide practical guidance for improving VQA performance on near-term hardware.
Computational Engineering, Finance, and Science (cs.CE); Quantum Physics (quant-ph)
arXiv:2606.10098 [cs.CE]
(or arXiv:2606.10098v1 [cs.CE] for this version)
https://doi.org/10.48550/arXiv.2606.10098 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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