Quantum Machine Learning for Trading
Book 1
Book 2
Book 3
Book 1
Book 2
Book 3
Book 1
Book 2
Book 3
Book 1
Book 2
Book 3
Home > Business and Economics > Finance and accounting > Finance and the finance industry > Quantum Machine Learning for Trading: Harnessing QML, Quantum Annealing, and Hybrid Models for Financial Market: Cutting-Edge Quantum-AI Approaches for Portfolio Optimization and Alpha D(3 Finance in Superposition)
Quantum Machine Learning for Trading: Harnessing QML, Quantum Annealing, and Hybrid Models for Financial Market: Cutting-Edge Quantum-AI Approaches for Portfolio Optimization and Alpha D(3 Finance in Superposition)

Quantum Machine Learning for Trading: Harnessing QML, Quantum Annealing, and Hybrid Models for Financial Market: Cutting-Edge Quantum-AI Approaches for Portfolio Optimization and Alpha D(3 Finance in Superposition)


     0     
5
4
3
2
1



International Edition


X
About the Book

Reactive PublishingThe future of quantitative finance is not just artificial intelligence, it's quantum intelligence. As traditional machine learning models push against the boundaries of classical computing, quantum machine learning (QML) offers a radical leap forward in speed, complexity, and the ability to model financial systems that were once computationally impossible. Quantum Machine Learning for Trading: Harnessing QML, Quantum Annealing, and Hybrid Models for Financial Markets is a groundbreaking guide that brings quantum theory into the heart of trading strategy. James Preston bridges the gap between cutting-edge research and practical market applications, showing how quantum technologies can redefine alpha generation, portfolio optimization, and risk management. Inside you'll discover how to: Apply quantum annealing to optimize large, complex portfolios under real-world constraints. Build hybrid classical-quantum algorithms that outperform traditional machine learning in financial contexts. Use QML models for high-frequency trading, volatility forecasting, and systemic risk analysis. Leverage quantum simulators to generate synthetic market data and test strategies beyond classical limits. Anticipate the regulatory and infrastructure shifts that will shape the adoption of quantum finance. Packed with technical depth and market-driven insight, this book equips quants, traders, and researchers with the tools to stay ahead of the quantum curve. Whether you're exploring QML for the first time or seeking to deploy advanced hybrid systems in production, you'll learn how to transform the theoretical promise of quantum into a real-world trading advantage. Welcome to the quantum era of finance. The edge belongs to those who prepare now.


Best Sellers


Product Details
  • ISBN-13: 9798264344626
  • Publisher: Independently Published
  • Publisher Imprint: Independently Published
  • Height: 254 mm
  • No of Pages: 590
  • Returnable: N
  • Spine Width: 30 mm
  • Weight: 1056 gr
  • ISBN-10: 8264344623
  • Publisher Date: 08 Sep 2025
  • Binding: Paperback
  • Language: English
  • Returnable: N
  • Series Title: 3 Finance in Superposition
  • Sub Title: Harnessing QML, Quantum Annealing, and Hybrid Models for Financial Market: Cutting-Edge Quantum-AI Approaches for Portfolio Optimization and Alpha D
  • Width: 178 mm


Similar Products

Add Photo
Add Photo

Customer Reviews

REVIEWS      0     
Click Here To Be The First to Review this Product
Quantum Machine Learning for Trading: Harnessing QML, Quantum Annealing, and Hybrid Models for Financial Market: Cutting-Edge Quantum-AI Approaches for Portfolio Optimization and Alpha D(3 Finance in Superposition)
Independently Published -
Quantum Machine Learning for Trading: Harnessing QML, Quantum Annealing, and Hybrid Models for Financial Market: Cutting-Edge Quantum-AI Approaches for Portfolio Optimization and Alpha D(3 Finance in Superposition)
Writing guidlines
We want to publish your review, so please:
  • keep your review on the product. Review's that defame author's character will be rejected.
  • Keep your review focused on the product.
  • Avoid writing about customer service. contact us instead if you have issue requiring immediate attention.
  • Refrain from mentioning competitors or the specific price you paid for the product.
  • Do not include any personally identifiable information, such as full names.

Quantum Machine Learning for Trading: Harnessing QML, Quantum Annealing, and Hybrid Models for Financial Market: Cutting-Edge Quantum-AI Approaches for Portfolio Optimization and Alpha D(3 Finance in Superposition)

Required fields are marked with *

Review Title*
Review
    Add Photo Add up to 6 photos
    Would you recommend this product to a friend?
    Tag this Book Read more
    Does your review contain spoilers?
    What type of reader best describes you?
    I agree to the terms & conditions
    You may receive emails regarding this submission. Any emails will include the ability to opt-out of future communications.

    CUSTOMER RATINGS AND REVIEWS AND QUESTIONS AND ANSWERS TERMS OF USE

    These Terms of Use govern your conduct associated with the Customer Ratings and Reviews and/or Questions and Answers service offered by Bookswagon (the "CRR Service").


    By submitting any content to Bookswagon, you guarantee that:
    • You are the sole author and owner of the intellectual property rights in the content;
    • All "moral rights" that you may have in such content have been voluntarily waived by you;
    • All content that you post is accurate;
    • You are at least 13 years old;
    • Use of the content you supply does not violate these Terms of Use and will not cause injury to any person or entity.
    You further agree that you may not submit any content:
    • That is known by you to be false, inaccurate or misleading;
    • That infringes any third party's copyright, patent, trademark, trade secret or other proprietary rights or rights of publicity or privacy;
    • That violates any law, statute, ordinance or regulation (including, but not limited to, those governing, consumer protection, unfair competition, anti-discrimination or false advertising);
    • That is, or may reasonably be considered to be, defamatory, libelous, hateful, racially or religiously biased or offensive, unlawfully threatening or unlawfully harassing to any individual, partnership or corporation;
    • For which you were compensated or granted any consideration by any unapproved third party;
    • That includes any information that references other websites, addresses, email addresses, contact information or phone numbers;
    • That contains any computer viruses, worms or other potentially damaging computer programs or files.
    You agree to indemnify and hold Bookswagon (and its officers, directors, agents, subsidiaries, joint ventures, employees and third-party service providers, including but not limited to Bazaarvoice, Inc.), harmless from all claims, demands, and damages (actual and consequential) of every kind and nature, known and unknown including reasonable attorneys' fees, arising out of a breach of your representations and warranties set forth above, or your violation of any law or the rights of a third party.


    For any content that you submit, you grant Bookswagon a perpetual, irrevocable, royalty-free, transferable right and license to use, copy, modify, delete in its entirety, adapt, publish, translate, create derivative works from and/or sell, transfer, and/or distribute such content and/or incorporate such content into any form, medium or technology throughout the world without compensation to you. Additionally,  Bookswagon may transfer or share any personal information that you submit with its third-party service providers, including but not limited to Bazaarvoice, Inc. in accordance with  Privacy Policy


    All content that you submit may be used at Bookswagon's sole discretion. Bookswagon reserves the right to change, condense, withhold publication, remove or delete any content on Bookswagon's website that Bookswagon deems, in its sole discretion, to violate the content guidelines or any other provision of these Terms of Use.  Bookswagon does not guarantee that you will have any recourse through Bookswagon to edit or delete any content you have submitted. Ratings and written comments are generally posted within two to four business days. However, Bookswagon reserves the right to remove or to refuse to post any submission to the extent authorized by law. You acknowledge that you, not Bookswagon, are responsible for the contents of your submission. None of the content that you submit shall be subject to any obligation of confidence on the part of Bookswagon, its agents, subsidiaries, affiliates, partners or third party service providers (including but not limited to Bazaarvoice, Inc.)and their respective directors, officers and employees.

    Accept

    Fresh on the Shelf


    Inspired by your browsing history


    Your review has been submitted!

    You've already reviewed this product!