Derivative Pricing using Quantum Signal Processing

TL;DR

This paper introduces a quantum signal processing method to encode financial derivative payoffs directly into quantum amplitudes, significantly reducing quantum resource requirements and advancing the feasibility of quantum advantage in derivative pricing.

Contribution

The paper presents a novel QSP-based approach that minimizes quantum arithmetic, lowering resource demands compared to existing methods for derivative pricing.

Findings

Reduces T-gate count by approximately 16 times

Decreases logical qubits by about 4 times

Estimates quantum advantage with 4.7k qubits and 10^9 T-gates at 45MHz

Abstract

Pricing financial derivatives on quantum computers typically includes quantum arithmetic components which contribute heavily to the quantum resources required by the corresponding circuits. In this manuscript, we introduce a method based on Quantum Signal Processing (QSP) to encode financial derivative payoffs directly into quantum amplitudes, alleviating the quantum circuits from the burden of costly quantum arithmetic. Compared to current state-of-the-art approaches in the literature, we find that for derivative contracts of practical interest, the application of QSP significantly reduces the required resources across all metrics considered, most notably the total number of T-gates by $\sim 16$x and the number of logical qubits by $\sim 4$x. Additionally, we estimate that the logical clock rate needed for quantum advantage is also reduced by a factor of $\sim 5$x. Overall, we find that quantum advantage will require $4.7$k logical qubits, and quantum devices that can execute $10^9$ T-gates at a rate of $45$MHz. While in this work we focus specifically on the payoff component of the derivative pricing process where the method we present is most readily applicable, similar techniques can be employed to further reduce the resources in other applications, such as state preparation.