Option Pricing using Quantum Computers
By Nikitas Stamatopoulos, Daniel J. Egger, Yue Sun, Christa Zoufal, Raban Iten, Ning Shen, Stefan Woerner. Quantum, 2019
328 citations recorded by the source corpus.
We present a methodology to price options and portfolios of options on a gate-based quantum computer using amplitude estimation, an algorithm which provides a quadratic speedup compared to classical Monte Carlo methods. The options that we cover include vanilla options, multi-asset options and path-dependent options such as barrier options. We put an emphasis on the implementation of the quantum circuits required to build the input states and operators needed by amplitude estimation to price the different option types. Additionally, we show simulation results to highlight how the circuits that we implement price the different option contracts. Finally, we examine the performance of option pricing circuits on quantum hardware using the IBM Q Tokyo quantum device. We employ a simple, yet effective, error mitigation scheme that allows us to significantly reduce the errors arising from noisy two-qubit gates.
Subfields: Artificial Intelligence, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/7fb656d76cb0d9f2adb4910e99ab4698400841c5
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Stefan Woerner
- Jakub Marecek
- Daniel J. Egger
- Rudy Raymond
- Andrea Simonetto
- Elena Yndurain
- Scott McFaddin
- Martin Mevissen
In Statistical and Nonlinear Physics
Citations in the corpus
- Warm-starting quantum optimization (2020, background): “Gate-based quantum computers are expected to aid in solving problems such as quantum chemistry calculations [1–3], machine learning [4, 5], financial simulation [6–13] and combinatorial optimization [14, 15].”
- Quantum Computing for Finance: State-of-the-Art and Future Prospects (2020, background): “This implies that we start with a weighted-sum operator, discussed in detail in [7], and then continue with adder circuits [52] to iteratively combine the intermediate results.”