Quantum Computing has the potential to speed up many financial use cases. To make this happen, there is a need for new algorithmic developments that leverage new hardware features. This paper is an example of this progress, bringing the industry closer to solving financial use cases with Quantum Computing.
The capabilities of current quantum hardware significantly constrain the scale of experimental demonstrations. This makes it challenging to perform demonstrations on hardware of quantum algorithms for practically relevant use cases. Portfolio optimization is an important use case in finance that lends itself to be tackled by quantum computing.
The Harrow-Hassidim-Lloyd (HHL) algorithm solves linear systems of equations and it can be used to solve portfolio optimization by casting this problem into a linear system. HHL solves a quantum version of the linear systems problem, whose solution can allow to observe useful properties about the optimal portfolio. However, most of the components of HHL are far out of the reach of noisy intermediate-scale quantum devices, which has led to the proposal of hybrid classical-quantum variants that leverage the power of both classical and quantum computation.
Members of J.P. Morgan’s Global Technology Applied Research team introduce Hybrid HHL++ that bridges the gap between proposed near-term implementations of HHL and the kinds of quantum circuits that can be executed on today’s hardware. To demonstrate the effectiveness of their procedure, they successfully applied Hybrid HHL++ to small-scale portfolio-optimization problems on the Quantinuum System Model H-series trapped-ion quantum computers.