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Quantum Class 22, Thurs 2022-12-01

1 Final project presentations, updated

  1. Everyone who requested a specific date, got it. I assigned the others to even out the calendar.

  2. No matter when you talk, you have until Fri Dec 9 to submit your project.

1.1 Thurs, Dec 1

  1. Denzell D

  2. Paul R

1.2 Mon, Dec 5

  1. Richard P

  2. Steve L

  3. Vansh RC

  4. Charles C and Sanghyun K

1.3 Thurs, Dec 8

  1. Adam G

  2. Almed E

  3. Noah P

  4. Alice B

  5. Alex B

  6. Oliver S

2 Quantum computing and Machine Learning

  1. Quantum Machine Learning vs. Machine Learning for Quantum Computing by Mats Granath 28:50 Apr 29, 2022

    "Machine learning and quantum computing are two expanding technologies with tremendous potential. What if they are combined, into quantum machine learning (QML)? I will give an overview of QML, pointing out that quantum mechanics makes it difficult to “quantize” classical ML algorithms such as artificial neural networks. Instead, existing QML algorithms are typically hybrid algorithms, part classical, part quantum. An alternative to making ML quantum is to use ML as part of the software required to operate a quantum computer. I will give some examples from my own research of both types of algorithms, using a hybrid QML approach to address a logistics problem and using deep reinforcement learning for compiling quantum code and for quantum error correction."

    The first 10 minutes is a very nice review, so we'll start after it.

  2. TensorFlow Quantum: A software platform for hybrid quantum-classical ML 27:15 Mar 11, 2020

    "We introduce TensorFlow Quantum, an open-source library for the rapid prototyping of novel hybrid quantum-classical ML algorithms. This library will extend the scope of current ML under TensorFlow and provides the necessary toolbox for bringing quantum computing and machine learning research communities together to control and model quantum data."

  3. Hybrid quantum-classical Neural Networks with PyTorch and Qiskit