Webinar: Rethinc. Labs — Dynamic Portfolio Optimization with Real Datasets Using Quantum Processors and Quantum-Inspired Tensor Networks
Rethinc. Labs invites you to join us as we host Román Orús, Ikerbasque Research Professor at the Donostia International Physics Center in San Sebastián, Spain. Roman will present the findings from his research on determining the optimal trading trajectory for an investment portfolio of assets over a period of time. Dynamic portfolio optimization is well known to be NP-Hard and is central to quantitative finance.
Roman and his team have implemented a number of quantum and quantum-inspired algorithms on different hardware platforms to solve its discrete formulation using real data from daily prices over 8 years of 52 assets, providing a detailed comparison of the obtained Sharpe ratios, profits and computing times. His team has applied classical solvers (Gekko, exhaustive), D-Wave Hybrid quantum annealing, two different approaches based on Variational Quantum Eigensolvers on IBM-Q (one of them brand-new and tailored to the problem), and for the first time in this context, a quantum-inspired optimizer based on Tensor Networks.
Roman will share his conclusions on which specific hardware platform can handle the largest systems with calculations up to 1272 fully-connected qubits for demonstrative purposes. He will also discuss how to mathematically implement other possible real-life constraints, sharing several ideas to further improve the performance of the studied methods.
For more information, contact:
Chelsea Donahue, Rethinc. Labs Assistant Director