[DevFest 2023] Unlocking Quantum Potential with Cirq and TensorFlow (By Haleema Tallat)
Haleema Tallat's talk on quantum machine learning and how you can make and train your own quantum model through Google Cirq and TensorFlow Quantum.
As all other things have evolved with the advancements in machine learning, quantum computing also falls in the same category. Haleema demonstrates that in real-world scenarios where quantum data is involved, quantum machine learning models have shown impactful results over the classical models.
Haleema also believes that we can now also use the excessive amount of quantum data in our real-world applications, which was previously unheard of, through such quantum ML models.
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LinkedIn: / haleema-tallat
Slides: https://speakerdeck.com/gdglahore/unl...
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00:00 History of quantum computing
01:22 Agenda
01:53 Qubit
02:59 Quantum gates
05:22 Quantum circuits
06:12 Preview of quantum machine learning
07:52 Quantum machine learning
10:20 Cirq & Tensorflow Quantum
12:26 Structural form of Cirq and Tensorflow Quantum
13:02 Demo
13:06 Setup
14:28 Loading MNIST
14:52 Filtering
15:27 Downscaling
16:19 Converting data to qubit
17:39 Qubit in action
17:56 Converting Cirq circuits to tensors for TensorFlow Quantum
18:09 Model circuit to run ML algorithms
19:38 Quantum neural network
20:11 Wrapping with Keras
20:29 Hinge loss
20:55 Training quantum model
21:19 Comparing with classical model
24:43 More case studies
25:27 What's next for quantum?
26:03 Learning resources & conclusion