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Autocallable Options Pricing with Integration-Based Exponential Amplitude Loading

Author

Listed:
  • Francesca Cibrario
  • Ron Cohen
  • Emanuele Dri
  • Christian Mattia
  • Or Samimi Golan
  • Tamuz Danzig
  • Giacomo Ranieri
  • Hanan Rosemarin
  • Davide Corbelletto
  • Amir Naveh
  • Bartolomeo Montrucchio

Abstract

We present a comprehensive quantum algorithm tailored for pricing autocallable options, offering a full implementation and experimental validation. Our experiments include simulations conducted on high-performance computing (HPC) hardware, along with an empirical analysis of convergence to the classically estimated value. Our key innovation is an improved integration-based exponential amplitude loading technique that reduces circuit depth compared to state-of-the-art approaches. A detailed complexity analysis in a relevant setting shows an approximately 50x reduction in T-depth for the payoff component relative to previous methods. These contributions represent a step toward more efficient quantum approaches to pricing complex financial derivatives.

Suggested Citation

  • Francesca Cibrario & Ron Cohen & Emanuele Dri & Christian Mattia & Or Samimi Golan & Tamuz Danzig & Giacomo Ranieri & Hanan Rosemarin & Davide Corbelletto & Amir Naveh & Bartolomeo Montrucchio, 2025. "Autocallable Options Pricing with Integration-Based Exponential Amplitude Loading," Papers 2507.19039, arXiv.org.
    • Handle: RePEc:arx:papers:2507.19039
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    File URL: http://arxiv.org/pdf/2507.19039
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    References listed on IDEAS

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    1. Shouvanik Chakrabarti & Rajiv Krishnakumar & Guglielmo Mazzola & Nikitas Stamatopoulos & Stefan Woerner & William J. Zeng, 2020. "A Threshold for Quantum Advantage in Derivative Pricing," Papers 2012.03819, arXiv.org, revised May 2021.
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