Option Pricing on Noisy Intermediate-Scale Quantum Computers: A Quantum Neural Network Approach

• URL: http://arxiv.org/abs/2604.19832v1
• Date: Mon, 20 Apr 2026 23:03:57 GMT
• Title: Option Pricing on Noisy Intermediate-Scale Quantum Computers: A Quantum Neural Network Approach
• Abstract summary: We propose a fully quantum approach to option pricing based on Quantum Neural Networks (QNNs)<n>We present one of the first implementations of such a methodology on currently available quantum hardware.<n>Our implementation utilizes a compact 2-qubit QNN architecture evaluated across multiple state-of-the-art quantum processors.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In a global derivatives market with notional values in the hundreds of trillions of dollars, the accuracy and efficiency of pricing models are of fundamental importance, with direct implications for risk management, capital allocation, and regulatory compliance. In this work, we employ the Black-Scholes-Merton (BSM) framework not as an end in itself, but as a controlled benchmark environment in which to rigorously assess the capabilities of quantum machine learning methods. We propose a fully quantum approach to option pricing based on Quantum Neural Networks (QNNs), and, to the best of our knowledge, present one of the first implementations of such a methodology on currently available quantum hardware. Specifically, we investigate whether QNNs, by exploiting the geometric structure of Hilbert space, can effectively approximate option pricing functions. Our implementation utilizes a compact 2-qubit QNN architecture evaluated across multiple state-of-the-art quantum processors, including IBM Fez, IQM Garnet, IonQ Forte, and Rigetti Ankaa-3. This cross-platform study reveals distinct hardware-dependent performance characteristics while demonstrating that accurate pricing approximations can be achieved consistently across different devices despite the constraints of Noisy Intermediate-Scale Quantum (NISQ) hardware. The results provide empirical evidence that QNN-based approaches constitute a viable framework for derivative pricing. While the analysis is conducted within the BSM setting, the broader significance lies in the potential extension of these methods to more realistic and computationally demanding models, including local volatility, stochastic volatility, and interest rate frameworks commonly used in practice.

Related papers

• Benchmarking Quantum Algorithmic Resilience for CVaR Portfolio Optimization: The Expressibility-Coherence Trade-off [0.0]
  Quantum optimization offers theoretical advantages for complex financial modeling.<n>Physical implementation on Noisy Intermediate Scale Quantum (NISQ) devices is severely constrained by hardware topology.<n>This study presents a benchmarking analysis between a Hardware Efficient Variational Quantum Neural Network (HE-VQNN) and the Warm Start Quantum Approximate Optimization Algorithm (WS-QAOA)
  arXiv  Detail & Related papers  (2026-06-05T17:07:59Z)
• Closing the Loop: Resource-aware Hybrid NAS Guided by Analytical and Hardware-Calibrated Quantum Cost Modeling [2.5435687567731926]
  Training quantum circuits on real devices requires thousands of circuit executions, which is impractical on current NISQ devices.<n>We propose an analytical quantum cost model that estimates quantum hardware resources using real backend calibration data.<n>We present Hyb-HANAS, a hardware-aware hybrid neural architecture search framework, which jointly optimize accuracy, hardware cost, and parameter count.
  arXiv  Detail & Related papers  (2026-02-28T12:34:23Z)
• AQER: a scalable and efficient data loader for digital quantum computers [62.40228216126285]
  We develop AQER, a scalable AQL method that constructs the loading circuit by systematically reducing entanglement in target states.<n>We conduct systematic experiments to evaluate the effectiveness of AQER, using synthetic datasets, classical image and language datasets, and a quantum many-body state datasets with up to 50 qubits.
  arXiv  Detail & Related papers  (2026-02-02T14:39:42Z)
• Quantum Kernel Methods: Convergence Theory, Separation Bounds and Applications to Marketing Analytics [0.22940141855172033]
  This work studies the feasibility of applying quantum kernel methods to a real consumer classification task in the NISQ regime.<n>We present a hybrid pipeline that combines a quantum- Kernel Support Vector Machine (Q-SVM) with a quantum feature extraction module (QFE)
  arXiv  Detail & Related papers  (2025-10-11T14:11:41Z)
• Hybrid Quantum-Classical Neural Networks for Few-Shot Credit Risk Assessment [52.05742536403784]
  This work tackles the challenge of few-shot credit risk assessment.<n>We design and implement a novel hybrid quantum-classical workflow.<n>A Quantum Neural Network (QNN) was trained via the parameter-shift rule.<n>On a real-world credit dataset of 279 samples, our QNN achieved a robust average AUC of 0.852 +/- 0.027 in simulations and yielded an impressive AUC of 0.88 in the hardware experiment.
  arXiv  Detail & Related papers  (2025-09-17T08:36:05Z)
• Reinforcement Learning for Quantum Network Control with Application-Driven Objectives [53.03367590211247]
  Dynamic programming and reinforcement learning offer promising tools for optimizing control strategies.<n>We propose a novel RL framework that directly optimize non-linear, differentiable objective functions.<n>Our work comprises the first step towards non-linear objective function optimization in quantum networks with RL, opening a path towards more advanced use cases.
  arXiv  Detail & Related papers  (2025-09-12T18:41:10Z)
• FD4QC: Application of Classical and Quantum-Hybrid Machine Learning for Financial Fraud Detection A Technical Report [36.1999598554273]
  This report investigates and compares the efficacy of classical, quantum, and quantum-hybrid machine learning models for the binary behavioural classification of fraudulent financial activities.<n>We implement and evaluate a range of models on the IBM Anti-Money Laundering (AML) dataset.<n>We propose Fraud Detection for Quantum Computing (FD4QC), a practical, API-driven system architecture designed for real-world deployment.
  arXiv  Detail & Related papers  (2025-07-25T16:08:22Z)
• VQC-MLPNet: An Unconventional Hybrid Quantum-Classical Architecture for Scalable and Robust Quantum Machine Learning [50.95799256262098]
  Variational quantum circuits (VQCs) hold promise for quantum machine learning but face challenges in expressivity, trainability, and noise resilience.<n>We propose VQC-MLPNet, a hybrid architecture where a VQC generates the first-layer weights of a classical multilayer perceptron during training, while inference is performed entirely classically.
  arXiv  Detail & Related papers  (2025-06-12T01:38:15Z)
• Contextual Quantum Neural Networks for Stock Price Prediction [0.0]
  We apply quantum machine learning (QML) to predict the stock prices of multiple assets using a contextual quantum neural network.<n>This architecture represents the first of its kind in quantum finance, offering superior predictive power and computational efficiency.<n>Our findings highlight the transformative potential of QML in financial applications, paving the way for more advanced, resource-efficient quantum algorithms.
  arXiv  Detail & Related papers  (2025-02-26T22:39:23Z)
• Quantum Computational Algorithms for Derivative Pricing and Credit Risk in a Regime Switching Economy [0.0]
  We introduce a class of processes that are both realistic in terms of mimicking financial market risks as well as more amenable to potential quantum computational advantages. We study algorithms to estimate credit risk and option pricing on a gate-based quantum computer.
  arXiv  Detail & Related papers  (2023-11-01T20:15:59Z)
• QKSAN: A Quantum Kernel Self-Attention Network [53.96779043113156]
  A Quantum Kernel Self-Attention Mechanism (QKSAM) is introduced to combine the data representation merit of Quantum Kernel Methods (QKM) with the efficient information extraction capability of SAM. A Quantum Kernel Self-Attention Network (QKSAN) framework is proposed based on QKSAM, which ingeniously incorporates the Deferred Measurement Principle (DMP) and conditional measurement techniques. Four QKSAN sub-models are deployed on PennyLane and IBM Qiskit platforms to perform binary classification on MNIST and Fashion MNIST.
  arXiv  Detail & Related papers  (2023-08-25T15:08:19Z)
• QSAN: A Near-term Achievable Quantum Self-Attention Network [73.15524926159702]
  Self-Attention Mechanism (SAM) is good at capturing the internal connections of features. A novel Quantum Self-Attention Network (QSAN) is proposed for image classification tasks on near-term quantum devices.
  arXiv  Detail & Related papers  (2022-07-14T12:22:51Z)
• Realization of arbitrary doubly-controlled quantum phase gates [62.997667081978825]
  We introduce a high-fidelity gate set inspired by a proposal for near-term quantum advantage in optimization problems. By orchestrating coherent, multi-level control over three transmon qutrits, we synthesize a family of deterministic, continuous-angle quantum phase gates acting in the natural three-qubit computational basis.
  arXiv  Detail & Related papers  (2021-08-03T17:49:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.