Time series generation for option pricing on quantum computers using tensor network

[2402.17148] Time series generation for option pricing on quantum computers using tensor network Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Quantum Physics arXiv:2402.17148 (quant-ph) [Submitted on 27 Feb 2024 ( v1 ), last revised 9 Apr 2026 (this version, v2)] Title: Time series generation for option pricing on quantum computers using tensor network Authors: Nozomu Kobayashi , Yoshiyuki Suimon , Koichi Miyamoto View a PDF of the paper titled Time series generation for option pricing on quantum computers using tensor network, by Nozomu Kobayashi and 2 other authors View PDF Abstract: Finance, especially option pricing, is a promising industrial field that might benefit from quantum computing. While quantum algorithms for option pricing have been proposed, it is desired to devise more efficient implementations of costly operations in the algorithms, one of which is preparing a quantum state that encodes a probability distribution of the underlying asset price. In particular, in pricing a path-dependent option, we need to generate a state encoding a joint distribution of the underlying asset price at multiple time points, which is more demanding. To address these issues, we propose a novel approach that uses a Matrix Product State (MPS), which can be encoded into a state of qubits, as a generative model for time series generation. We focus on the training of such an MPS and present its procedure in detail. To validate our approach, taking the Heston model as a target, we conduct numerical experiments to generate time series in the model. Our findings demonstrate the capability of the MPS model to generate paths in the Heston model, highlighting its potential for path-dependent option pricing on quantum computers. Comments: 18 pages, 3 figures Subjects: Quantum Physics (quant-ph) ; Machine Learning (cs.LG); Computational Finance (q-fin.CP) Cite as: arXiv:2402.17148 [quant-ph] (or arXiv:2402.17148v2 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2402.17148 Focus to learn more arXiv-issued DOI via DataCite Journal reference: Quantum Mach. Intell. 8, 39 (2026) Related DOI : https://doi.org/10.1007/s42484-026-00342-3 Focus to learn more DOI(s) linking to related resources Submission history From: Koichi Miyamoto [ view email ] [v1] Tue, 27 Feb 2024 02:29:24 UTC (431 KB) [v2] Thu, 9 Apr 2026 00:31:14 UTC (433 KB) Full-text links: Access Paper: View a PDF of the paper titled Time series generation for option pricing on quantum computers using tensor network, by Nozomu Kobayashi and 2 other authors View PDF TeX Source view license Current browse context: quant-ph < prev | next > new | recent | 2024-02 Change to browse by: cs cs.LG q-fin q-fin.CP References & Citations INSPIRE HEP NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

Related documents

• Quantum Machine Learning for Time Series#246674
• Hybrid Variational Quantum-Classical Framework with Adaptive Weighting and Efficiency Assessment#1076148
• Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation#432496
• Practical Scalability of Tensor Network Quantum Emulators for Molecular Hamiltonian Simulation#684221
• Practical Scalability of Tensor Network Quantum Emulators for Molecular Hamiltonian Simulation#682446