Quantum-Assisted Portfolio Optimization Using Classiq's Circuit Synthesis

[Kamalirajendiran03]

We are a team of @Kamalirajendiran03 , @rizanuma under the guidance of @ManjulaGandhi , @sgayathridevi is participating in the Classiq Intelligence Challenge 2025, working on Quantum Asset Allocation Optimization. Our project explores quantum-enhanced portfolio management using Classiq’s automated circuit synthesis.

I hereby attach the project proposal bellow :

PROPOSAL FOR QUANTUM ASSET ALLOCATION OPTIMIZATION.pdf

We welcome feedback, discussions, and contributions to help refine and improve our approach. Feel free to share your insights and ideas here to make this project a success!

[NadavClassiq]

Hello @Kamalirajendiran03 !

Thank you for your proposal on Quantum-Assisted Portfolio Optimization Using Classiq's Circuit Synthesis

Please include a summary with a link to the relevant paper of your proposal above, focusing on how you plan to implement it using Classiq.

As this concept originates from classical portfolio optimization methods, could you clarify which components you intend to implement using quantum computing? Do you have a circuit example?

Please note that we accept only high-quality implementations in our repository and will be glad to accept a contribution that meets our standards.

Feel free to reach out to the community if you have any questions.

Thanks!

[Kamalirajendiran03]

Thank you for your feedback and for reviewing our proposal! We appreciate the opportunity to contribute to Classiq’s repository.

Summary & Paper Reference
Our project explores Quantum-Assisted Portfolio Optimization using Classiq’s circuit synthesis to enhance asset allocation. We aim to test the feasibility of quantum algorithms (QAOA & VQE) in improving risk-adjusted portfolio selection.

We are building on the Black-Litterman model, incorporating quantum methods to assess their capability in portfolio optimization. Our reference paper:
Black litterman portfolio optimization.pdf

What is the Black-Litterman Model?
The Black-Litterman model improves traditional Modern Portfolio Theory (MPT) by integrating market equilibrium data with investor views, leading to better diversification and risk control.

It addresses key challenges of MPT, such as:

• Over-concentrated portfolios due to extreme reliance on estimated returns.
• Sensitivity to small input changes, leading to unstable allocations.
• Lack of flexibility in incorporating subjective expert opinions.
• By combining historical data with investor expectations, the model creates more stable and realistic portfolio allocations.

Since some computational steps in Black-Litterman (e.g., mean-variance optimization) are resource-intensive, we are exploring how quantum computing might help in:

1. Testing Portfolio Optimization as a QUBO Problem
We encode the mean-variance optimization process as a Quadratic Unconstrained Binary Optimization (QUBO) problem.
This allows us to experiment with quantum solvers to assess feasibility.

2. Using QAOA for Asset Selection
QAOA (Quantum Approximate Optimization Algorithm) helps explore optimal asset weights while integrating investor views.
We aim to determine whether QAOA can efficiently select assets in a risk-return balanced manner. Applying VQE for Risk Optimization

3.Variational Quantum Eigensolver (VQE) is used to minimize portfolio variance while maximizing expected returns.We are testing whether VQE can provide efficient risk analysis compared to classical solvers.

Hybrid Quantum-Classical Approach

• Preprocessing: Classical computation of market equilibrium returns and investor beliefs.
• Quantum Processing: Experimenting with QAOA and VQE for portfolio weight optimization.
• Post-processing: Classical refinement and benchmarking.

Since Classiq’s platform enables high-level quantum circuit synthesis, which helps us to :

• Automate ansatz design for QAOA and VQE, optimizing circuit depth.
• Efficiently map mathematical models (like QUBO) to quantum circuits.
• Ensure compatibility with real hardware to evaluate practical feasibility.
• Compare performance with classical methods to determine quantum advantage.

Next Steps
We are currently designing optimized QAOA circuits and will share a working example soon.

Our primary goals are:

• Evaluating whether quantum methods improve asset allocation.
• Optimizing circuit structures for better performance.
• Benchmarking against classical Black-Litterman implementations.

Since this is an exploratory effort, we are testing the feasibility of quantum approaches rather than guaranteeing an immediate advantage. We welcome any insights on how to refine our implementation further.

Thank you for your guidance!