vqa-dynamic-portfolio-optimization

name	vqa-dynamic-portfolio-optimization
description	VQA methodology for dynamic portfolio optimization — sampling strategies (adaptive CVaR scheduling), optimizer scheduling (PSO+NFT hybrid), and hardware-aware ansatz design (data-guided colored layout, heavy-hex deep-chain layout). Activation: vqa portfolio, dynamic portfolio optimization, CVaR scheduling, hardware-aware ansatz, heavy-hex layout, PSO optimizer, quantum portfolio, 动态投资组合优化
metadata	{"arxiv_id":"2606.10098","published":"2026-06-08","categories":"cs.CE, quant-ph"}

VQA Dynamic Portfolio Optimization

Description

Hardware-aware Variational Quantum Algorithm (VQA) methodology for multi-period dynamic portfolio optimization, covering sampling strategy selection, optimizer scheduling, and qubit layout design for NISQ processors.

Core Methodology

Adaptive CVaR Scheduling

Gradually tighten the sampled tail used for optimization: start with broader CVaR percentile (exploration), progressively focus on tail risk (exploitation). Prevents premature convergence to suboptimal portfolios.

Two-Stage Optimizer

• Stage 1: Particle Swarm Optimization (PSO) for global exploration across parameter landscape
• Stage 2: Nakanishi-Fujii-Todo (NFT) optimizer for local refinement with gradient-free precision

Hardware-Aware Ansatz Layout

Data-Guided Colored Layout:

• Map correlated asset variables to qubits connected by native entangling gates
• Use asset correlation matrix to inform qubit assignment
• Reduces SWAP gate overhead during transpilation

Heavy-Hex Deep-Chain Layout:

• Designed specifically for IBM heavy-hex topology