Quantum Accelerated Option Pricing

[anushpranav]

We propose implementing a Quantum-Accelerated Option Pricing framework optimized for the Indian financial market using Quantum Monte Carlo (QMC) and Quantum Amplitude Estimation (QAE). This project aims to enhance option pricing accuracy and reduce computational costs, making quantum finance more applicable to real-world trading in India.

We have detailed the technical approach, implementation workflow, and feasibility on NISQ hardware in our documentation. Kindly refer to it for an in-depth understanding.

Documentation Link:
🔗 Quantum Accelerated Option Pricing.pdf

Would love to hear feedback and explore possible collaborations for implementation on Classiq’s platform. Looking forward to your insights!

Authors:
@anushpranav
@Pranavjps
@ManjulaGandhi
@sgayathridevi

[TomerGoldfriend]

Thank you @anushpranav , please note that we already have several examples of quantum monte carlo integration approaches in our repository: see a tutorial here, a built-in example here, and an advance example here.

Could you please elaborate more how your suggestion is going to differ from the above?

[anushpranav]

@TomerGoldfriend Thank you for pointing out the existing examples in your repository. Our project aims to complement these by focusing on the Indian financial market, incorporating local regulatory considerations, market dynamics, and real NSE data. We believe this targeted approach can provide additional insights and practical applications for quantum computing in region-specific financial contexts.

[TomerGoldfriend]

OK, thank you @anushpranav , but what will be your contribution from algorithmic point of view?
Are you going to implement a new payoff function, for example (if so, which one)?

[anushpranav]

@TomerGoldfriend, Our contribution includes a custom payoff function based on the Heston Model to better capture Indian market volatility (e.g., NIFTY, BANK NIFTY). We also aim to optimize QMC sampling for improved convergence on NISQ hardware, ensuring practical implementation.

[TomerGoldfriend]

OK @anushpranav, please note that we accept high-quality implementations to our repository and will be glad to accept a contribution that meets our standards.

Feel free to reach out to the community for any questions.

Good luck!

[TomerGoldfriend]

@anushpranav Could you please specify a specific research paper that you are going to implement?

[anushpranav]

@TomerGoldfriend Sure, We are implementing our approach based on the paper "Option Pricing using Quantum Computers"

[TomerGoldfriend]

Thank you @anushpranav, this is the textbook implementation that already exists in our library.
I cannot approve this issue as is, please provide more details on the payoff function/ NISQ version you mentioned (e.g., what kind of quantum primitives are you going to use) so we can review this properly.

[anushpranav]

Thank you for the clarification @TomerGoldfriend. Our approach extends beyond the textbook implementation of QMC for option pricing, specifically targeting the Indian financial market.

Key Differentiators in Our Approach:

Modified Payoff Function for Emerging Markets:

• Instead of a standard European option pricing approach, we aim to model stochastic volatility-based pricing (Heston model), which better aligns with Indian market conditions.
• Incorporating path-dependent options (e.g., Asian options), which are actively traded in India.

NISQ Optimization Strategy:

• We plan to use Quantum Amplitude Estimation (QAE) with Variational Circuits to reduce qubit depth and improve feasibility on current quantum hardware.
• Instead of a direct implementation of QAE, which is costly in terms of quantum resources, we explore heuristic-based QAE approximations to mitigate error rates on NISQ devices.

Quantum Primitives Used:

• Efficient State Preparation: Using log-normal asset price encoding instead of naive superposition methods to achieve better accuracy in real-world pricing.
• Noise-Optimized QMC Simulation: Adapting QMC with error mitigation techniques suited for available hardware.

[TomerGoldfriend]

OK @anushpranav , since you cannot provide a reference to a specific paper (for the implementation of Asian options, loading log-normal distribution, etc), could you please include more technical details on how the implementation will look like using Classiq --- what kind of quantum primitives are you going to use? (Quantum arithmetics, multi-controlled operations, etc.). It will be good to understand the model layout and the main quantum blocks.

In addition, what do you mean by "Amplitude Estimation (QAE) with Variational Circuits"? do you have a reference for that?

[TomerGoldfriend]

@anushpranav I am assigning you to this issue. Please note that we accept high-quality implementations to our repository and will be glad to accept a contribution that meets our standards.

Feel free to reach out to the community for any questions!
Good luck!

[TaliCohn]

Hi @anushpranav, what is the status of this? Are you still working on the implementation?

[anushpranav]

Yes, we're still working on it, we'll update it asap.

[TaliCohn]

No problem. How much time do you need? I'll update the submission deadline

[anushpranav]

We hope to finish within next week.

[TaliCohn]

Hi @anushpranav, any update here?

[TaliCohn]

@anushpranav

[anushpranav]

@TaliCohn Unfortunately, we were unable to complete it due to an issue encountered during the circuit implementation in Classiq Studio.