Three experts in quantum Monte Carlo: Quantum Monte Carlo with Gustavo Ordoñez of Moody’s Analytics, Giorgios Korpas of HSBC, and Iordanis Kerenidis of QCWare, are interviewed by Yuval.
Key points:
- Expert Backgrounds: Gustavo Ordoñez: Senior Director Data Scientist at Moody's Analytics, focuses on quantum computing. Iordanis Kerenidis: Head of Quantum Algorithms for QC Ware, specializes in optimization and machine learning for finance. Giorgios Korpas: Research Scientist at HSBC, background in theoretical physics and quantum optimization.
- What is Monte Carlo?: A stochastic process used to model market scenarios for asset pricing.
- Difference Between Monte Carlo and Quantum Monte Carlo: Quantum Monte Carlo leverages quantum algorithms like the Grover algorithm to perform tasks quadratically faster.
- Advantages of Quantum Monte Carlo: Improved sample complexity and accuracy. Potential for significant speed-up with fewer samples.
- Challenges and Limitations: Hardware limitations: Need for high-fidelity qubits. Depth of quantum circuits: Current hardware not sufficient for deep circuits. Data loading and readout also need to be efficient.
- Timeframe for Practical Use: Medium-term, likely 5-10 years, due to hardware and algorithmic challenges.
- Team Composition for Research: Need for interdisciplinary teams: Quantum scientists, financial experts, and stochastic mathematicians.
- Final Thoughts: Quantum Monte Carlo has the potential to revolutionize financial modeling but faces significant challenges that require interdisciplinary solutions.
