To create an investment portfolio that maximizes expected return while minimizing risk, tailored to constraints like liquidity, ESG goals, or regulatory limits.

The Challenge:

Traditional models (like mean-variance optimization) rely on linear assumptions and static correlations, which:

• Struggle to adapt to volatile, nonlinear markets

• Ignore hidden correlations between assets

• Can’t scale efficiently with large portfolios or diverse asset classes

Investors need smarter tools that capture complex interactions between assets, respond to real-time market shifts, and deliver robust portfolio allocations under uncertainty.

Quantum-Inspired Solution: Smarter Portfolio Decisions with HessQ

HessQ applies Quantum-Inspired Optimization (QUBO) and tensor modeling to solve portfolio construction as a global optimization problem.

Captures non-linear correlations between assets (beyond just Pearson or covariance)

Balances multiple objectives (e.g., return, risk, ESG impact, liquidity, sector diversity)

Finds optimal asset weights by simulating thousands of scenarios in parallel using QUBO logic

The Result

Outputs Delivered:

• Optimized asset weights with trade-off visualizations (return vs. risk)

• Better diversification across sectors and risk types

• ynamic rebalancing suggestions based on new market data

Why HessQ Outperforms Traditional Models

• Sees beyond linear stats: Detects hidden dependencies and subtle patterns

• Scales with complexity: Works across thousands of assets, constraints, and scenarios

• Adapts in real time: Recommends updates as markets shift

• Aligns with modern priorities: Includes ESG metrics and alternative data

• Bridges finance and AI: Merges financial logic with quantum-inspired ML