Quantum Financial Modeling on Noisy Intermediate-Scale Quantum Hardware: Random Walks Using Approximate Quantum Counting

Abstract

Quantum computers are expected to contribute more efficient and accurate ways of modeling economic processes. Quantum hardware is currently available at a relatively small scale, but effective algorithms are limited by the number of logic gates that can be used, before noise from gate inaccuracies tends to dominate results. Some theoretical algorithms that have been proposed and studied for years do not perform well yet on quantum hardware in practice. This encourages the development of suitable alternative algorithms that play similar roles in limited contexts. This paper implements this strategy in the case of quantum counting, which is used as a component for keeping track of position in a quantum walk, which is used as a model for simulating asset prices over time. We introduce quantum approximate counting circuits that use far fewer 2-qubit entangling gates than traditional quantum counting that relies on binary positional encoding. The robustness of these circuits to noise is demonstrated. While this paper is mainly about robust simplified quantum circuit designs, we compare some aspects of the results with price change distributions from stock indices, and compare the behavior of circuits with and without mid-measurement to trends in the housing market.

Quantum Temporal Winds: Turbulence in Financial Markets

“…Recent contributions by Li and Liang (2020) [25] on extending option pricing models, Widdows and Bhattacharyya (2024) [26] on quantum financial modeling, and insights by Evanoff (2009) [27] on globalization and systemic risk further reinforce the transformative potential of quantum finance. Additional perspectives by Gkillas and Longin (2018) [28] on Bitcoin, and by Zhou et al (2019) [29] on marketing agility under market turbulence, alongside a review of evidence for financial contagion (2015) [30], contribute to an understanding of complex economic environments.…”