{"id":7118,"date":"2025-05-28T13:33:59","date_gmt":"2025-05-28T20:33:59","guid":{"rendered":"https:\/\/labs.engineering.asu.edu\/mps-lab\/?page_id=7118"},"modified":"2025-07-17T14:37:43","modified_gmt":"2025-07-17T21:37:43","slug":"cse-494-598-quantum-computation","status":"publish","type":"page","link":"https:\/\/labs.engineering.asu.edu\/mps-lab\/teaching\/cse-494-598-quantum-computation\/","title":{"rendered":"CSE 494\/598 : Quantum Computation"},"content":{"rendered":"\n

Overview<\/h3>\n\n\n\n

Quantum computing is not merely a novel computational paradigm\u2014it represents an opportunity to exploit the counterintuitive principles of quantum mechanics for real-time problem solving. This course focuses on the cutting-edge field of Quantum Machine Learning<\/strong> (QML<\/strong>) with an emphasis on the mathematical<\/span> and <\/strong>coding<\/span> aspects. <\/p>\n\n\n\n

\n\n\n\n

Syllabus<\/strong>:<\/h2>\n\n\n\n
\n
Spring 2025<\/a><\/div>\n<\/div>\n\n\n\n
<\/div>\n\n\n\n
\n\n\n\n

Prerequisites<\/h3>\n\n\n\n
    \n
  • Linear Algebra:<\/strong> A strong foundation in linear algebra, including vectors, matrices, determinants, eigenvalues, and eigenvectors, is essential.<\/li>\n\n\n\n
  • Probability and Statistics:<\/strong> Familiarity with probability theory, statistical distributions, and basic statistical inference is necessary.<\/li>\n\n\n\n
  • Programming:<\/strong> Proficiency in a programming language like Python is recommended, as it is widely used in quantum computing and machine learning.<\/li>\n\n\n\n
  • Classical Machine Learning:<\/strong> A foundational understanding of classical machine learning algorithms and techniques (e.g., linear regression, logistic regression, neural networks) would be beneficial.<\/li>\n\n\n\n
  • Basic Complex Analysis:<\/strong> Familiarity with complex numbers and their properties will be valuable.<\/li>\n<\/ul>\n\n\n\n
    \n\n\n\n

    Main Topics<\/h3>\n\n\n\n
    \"\"<\/figure>\n\n\n\n
    \n
    \n

    Quantum Computation<\/h4>\n\n\n\n
      \n
    1. Single Qubit Systems<\/li>\n\n\n\n
    2. Multi-Qubit Systems<\/li>\n\n\n\n
    3. Entanglement<\/li>\n\n\n\n
    4. Measurement<\/li>\n\n\n\n
    5. Quantum Algorithms<\/li>\n<\/ol>\n<\/div>\n\n\n\n
      \n

      Machine Learning<\/h3>\n\n\n\n
        \n
      1. Intro to Vector Spaces<\/li>\n\n\n\n
      2. Kernel Machine Learning<\/li>\n\n\n\n
      3. Feature Maps <\/li>\n\n\n\n
      4. Gradients and Optimization<\/li>\n<\/ol>\n<\/div>\n\n\n\n
        \n

        Quantum Machine Learning<\/h3>\n\n\n\n
          \n
        1. Parameterized Quantum Circuits <\/li>\n\n\n\n
        2. Data Re-Uploading Models<\/li>\n\n\n\n
        3. Quantum Kernel Machine Learning<\/li>\n<\/ol>\n<\/div>\n<\/div>\n\n\n\n
          \n\n\n\n

          Learning Outcomes<\/h3>\n\n\n\n

          Upon completion of this course, students will be able to –<\/p>\n\n\n\n

            \n
          • Explain the main physics experiments that helped develop quantum physics and understand the postulates of quantum computing.<\/li>\n\n\n\n
          • Demonstrate the evolution of quantum states in single-bit and multi-bit quantum circuits.<\/li>\n\n\n\n
          • Demonstrate understanding and operation of the main quantum computing algorithms.<\/li>\n\n\n\n
          • Explain the main sources of noise in quantum computation and why NISQ devices exist.<\/li>\n\n\n\n
          • Explain the main methods of classical data encoding into quantum circuits.<\/li>\n\n\n\n
          • Demonstrate understanding of main quantum machine learning algorithms, including variational quantum circuits, data re-uploading, quantum kernels, and quantum generative models.<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"

            Overview Quantum computing is not merely a novel computational paradigm\u2014it represents an opportunity to exploit the counterintuitive principles of quantum mechanics for real-time problem solving. This course focuses on the cutting-edge field of Quantum Machine Learning (QML) with an emphasis on the mathematical and coding aspects. Syllabus: Prerequisites Main Topics Quantum Computation Machine Learning Quantum…<\/p>\n","protected":false},"author":221,"featured_media":0,"parent":3003,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":""},"class_list":["post-7118","page","type-page","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/labs.engineering.asu.edu\/mps-lab\/wp-json\/wp\/v2\/pages\/7118","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/labs.engineering.asu.edu\/mps-lab\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/labs.engineering.asu.edu\/mps-lab\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/labs.engineering.asu.edu\/mps-lab\/wp-json\/wp\/v2\/users\/221"}],"replies":[{"embeddable":true,"href":"https:\/\/labs.engineering.asu.edu\/mps-lab\/wp-json\/wp\/v2\/comments?post=7118"}],"version-history":[{"count":0,"href":"https:\/\/labs.engineering.asu.edu\/mps-lab\/wp-json\/wp\/v2\/pages\/7118\/revisions"}],"up":[{"embeddable":true,"href":"https:\/\/labs.engineering.asu.edu\/mps-lab\/wp-json\/wp\/v2\/pages\/3003"}],"wp:attachment":[{"href":"https:\/\/labs.engineering.asu.edu\/mps-lab\/wp-json\/wp\/v2\/media?parent=7118"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}