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.
Read the full transcript or listen to the episode here.
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.
Read the full transcript or listen to the episode here.
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