quantum-portfolio-optimizer

name	quantum-portfolio-optimizer
description	Quantum computing portfolio optimization skill. Uses QAOA, quantum annealing, and hybrid quantum-classical methods for financial portfolio optimization with higher-order moments (skewness, kurtosis) and real-world constraints (cardinality, turnover limits). Activation: quantum portfolio, quantum optimization, QAOA portfolio, 量子组合优化, quantum finance optimization, portfolio optimization quantum.

Quantum Portfolio Optimizer

Quantum computing approach to portfolio optimization using QAOA, quantum annealing, and hybrid methods.

Quick Start

# Basic quantum portfolio optimization
from qiskit_optimization import QuadraticProgram
from qiskit_optimization.algorithms import QAOA

# 1. Define portfolio problem as QUBO
qp = QuadraticProgram("portfolio")
qp.binary_var_list(assets)  # n assets -> n binary variables

# 2. Set objective: maximize return - risk penalty
qp.minimize(linear=-expected_returns, quadratic=risk_covariance)

# 3. Add constraints (optional)
qp.linear_constraint(cardinality_constraint)  # max k assets

# 4. Solve with QAOA
qaoa = QAOA(quantum_instance, reps=3)
result = qaoa.solve(qp)
selected_assets = result.x

Core Workflow

Step 1: Problem Formulation

Convert portfolio optimization to QUBO (Quadratic Unconstrained Binary Optimization):

Objective: min Σ_i(-μ_i x_i) + λ Σ_iΣ_j(σ_ij x_i x_j)

Where:
- x_i ∈ {0,1}: binary selection variable for asset i
- μ_i: expected return of asset i
- σ_ij: covariance between assets i and j
- λ: risk penalty coefficient