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
• Increases native two-qubit interaction depth without additional routing
• Best final objective value and CVaR-tail performance among tested layouts on ibm_quebec QPU

Key Findings

• Sampling strategy, optimizer scheduling, and hardware-aware layout design materially affect VQA performance
• No quantum advantage observed over state-of-the-art classical solver on tested instances
• Heavy-hex-native deep-chain layout outperforms standard layouts on real QPU
• Tested on 150-qubit dynamic portfolio instance balancing return, risk, transaction costs, and cash-interest effects

Implementation Steps

1. Formulate QUBO: Encode multi-period portfolio optimization (return, risk, transaction costs, cash-interest, constraints)
2. Design Ansatz: Choose hardware-aware layout (colored or heavy-hex deep-chain)
3. Set CVaR Schedule: Adaptive tightening from ~50th to ~10th percentile
4. Train Stage 1: PSO with broad parameter ranges
5. Train Stage 2: NFT optimizer with refined starting point
6. Evaluate: Compare on QPU vs classical solver baseline

Pitfalls

• No quantum advantage yet: Classical exact solvers still outperform on tested instances
• 150-qubit scale: Results may not extrapolate to larger portfolios
• Heavy-hex topology specific: Layout optimization is processor-specific
• Simulator → QPU gap: Simulator-selected configurations may not transfer directly to hardware

Activation Keywords

• vqa portfolio optimization
• dynamic portfolio quantum
• CVaR adaptive scheduling
• hardware-aware ansatz
• heavy-hex layout
• PSO NFT optimizer
• quantum portfolio multi-period
• VQA动态投资组合
• 量子CVaR优化