Unlocking Quantum Potential with Cirq and TensorFlow (By: Haleema Tallat) - DevFest Lahore 2023

Workshop by Haleema Tallat (https://www.linkedin.com/in/haleema-tallat/) at DevFest Lahore 2023 by GDG Lahore.

Transcript

1. Unlocking Quantum Potential With Tensorflow Quantum and Cirq
3. Content Building Blocks Libraries Demonstration Discussion
4. The Qubit • Qubits are the fundamental units of quantum information. • Unlike classical bits (0 or 1), qubits can exist in multiple states simultaneously due to superposition. • Represented mathematically as α|0⟩ + β|1⟩, where α and β are probability amplitudes.
6. Quantum Gates • Analogous to classical logic gates, quantum gates manipulate qubits. • They perform operations like rotations, flips, and entanglement generation. • They are reversible!
8. Quantum Circuits • Combinations of quantum gates form circuits to perform specific tasks. • Circuits manipulate qubits through a sequence of gates to execute quantum algorithms.
10. Young’s Double Slit Experiment
11. Quantum Machine Learning • Quantum Computing is about creating constructive interference for correct answers. • Quantum Machine Learning is about iterative creation of constructive interferences for correct answers.
15. Integration of TensorFlow Quantum and Cirq a. High-Level Quantum Model Design b. TensorFlow's Optimization and Training c. Seamless Integration
17. Demonstration
18. Setup
19. Loading MNIST
20. Filter for 3 and 6
22. Downscale
23. Encode the data as quantum circuits Representing each pixel with a qubit, with the state depending on the value of the pixel. The first step is to convert to a binary encoding.
24. The qubits at pixel indices with values that exceed a threshold, are rotated through an X gate.
25. SVGCircuit(x_train_circ[0])
26. Convert these Cirq circuits to tensors for tfq: x_train_tfcirc = tfq.convert_to_tensor(x_train_circ) x_test_tfcirc = tfq.convert_to_tensor(x_test_circ)
27. Model Circuit
29. Quantum neural network
30. Parametrized Quantum Circuit layer, tfq.layers.PQC, to train the model circuit, on the quantum data. Wrapping with Keras
31. Hinge Loss To use the hinge loss here you need to make two small adjustments. First convert the labels, y_train_nocon, from boolean to [-1,1], as expected by the hinge loss. y_train_hinge = 2.0*y_train_nocon-1.0 y_test_hinge = 2.0*y_test-1.0 Second, use a custiom hinge_accuracy metric that correctly handles [-1, 1] as the y_true labels argument. tf.losses.BinaryAccuracy(threshold=0.0) expects y_true to be a boolean, and so can't be used with hinge loss).
32. Custom hinge_accuracy metric
33. Model Summary
34. Train the quantum model EPOCHS = 3 BATCH_SIZE = 32 NUM_EXAMPLES = len(x_train_tfcirc) x_train_tfcirc_sub = x_train_tfcirc[:NUM_EXAMPLES] y_train_hinge_sub = y_train_hinge[:NUM_EXAMPLES]
35. Training to convergence achieves >85% accuracy! Epoch 1/3 324/324 [==============================] - 64s 196ms/step - loss: 0.7855 - hinge_accuracy: 0.6745 - val_loss: 0.5021 - val_hinge_accuracy: 0.8080 324/324 [==============================] - 63s 196ms/step - loss: 0.3958 - hinge_accuracy: 0.8441 - val_loss: 0.3608 - val_hinge_accuracy: 0.9088 324/324 [==============================] - 63s 195ms/step - loss: 0.3533 - hinge_accuracy: 0.8913 - val_loss: 0.3449 - val_hinge_accuracy: 0.9062 62/62 [==============================] - 3s 43ms/step - loss: 0.3449 - hinge_accuracy: 0.9062
36. Comparing with Classical Model
40. 2. Relabeling and computing PQK features You will now prepare a "stilted" quantum dataset by incorporating quantum components and re-labeling the truncated fashion-MNIST dataset you've created above. In order to get the most seperation between quantum and classical methods, you will first prepare the PQK features and then relabel outputs based on their values. 2.1 Quantum encoding and PQK features You will create a new set of features, based on x_train, y_train, x_test and y_test that is defined to be the 1-RDM on all qubits of:
54. Summary of Test Results
55. More Case Studies High-Dimensional Signal Processing High energy physics
56. What’s next for Quantum?
59. Realistic Prospects • Enhanced Machine Learning Models • Specific Applications ◦ Drug Discovery and Molecular Modeling ◦ Financial Modeling Network Attacks • Quantum Neural Networks (QNNs) • Quantum Hardware Development • Algorithmic Developments
60. For further discussions: haleematallat019@gmail.com linkedin/haleema-tallat Q & A Thank you!