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tfquantum_starter.py
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tfquantum_starter.py
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# -*- coding: utf-8 -*-
"""TFQuantum_starter.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1aq8jWy1AEpNqn1xb_ojhRTpiePNe5ebZ
[](https://colab.research.google.com/github/Rishit-dagli/TFUG-Mysuru-2020/blob/master/TFQuantum_starter.ipynb)
# Getting started with [TensorFlow Quantum](https://www.tensorflow.org/quantum)
In this notebook you will build your first hybrid quantum classical model with
[Cirq](https://cirq.readthedocs.io/en/stable/) and TensorFlow Quantum (TFQ). We will build a very simple model to do
binary classification in this notebook. You will then use Keras to create a wrapper for the model and simulate it to
train and evluate the model.
> Note: This notebook is designed to be run in Google Colab if you want to run it locally or on a Jupyter notebook you
would skip the code cells with the `Colab only` comment.
## Setup
### Install TensorFlow 2.x (Colab only)
"""
# Colab only
pip install -q tensorflow==2.1.0
"""### Install TensorFlow Quantum (Colab only)"""
# Colab only
pip install -q tensorflow-quantum
"""### Imports
Now import TensorFlow and the module dependencies:
"""
import cirq
import random
import numpy as np
import sympy
import tensorflow as tf
import tensorflow_quantum as tfq
from matplotlib import pyplot as plt
from cirq.contrib.svg import SVGCircuit
"""### Place a qubit on the grid
You will then place a qubit on thee grid
"""
qubit = cirq.GridQubit(0, 0)
"""## Prepare quantum data
The first thing you would do is set up the labels and parameters for preparation of the quantum data. For simplicity
here we have included just 2 data points `a` and `b`.
"""
expected_labels = np.array([[1, 0], [0, 1]])
"""Randonly rotate `x` and `z` axis"""
angle = np.random.uniform(0, 2 * np.pi)
"""## Building the quantum Circuit
You will now build the quantum circuit and also convert it into a tensor
"""
a = cirq.Circuit(cirq.ry(angle)(qubit))
b = cirq.Circuit(cirq.ry(angle + np.pi / 2)(qubit))
quantum_data = tfq.convert_to_tensor([a, b])
SVGCircuit(a)
SVGCircuit(b)
"""## Building the hybrid model
This section also shows the interoperatability between TensorFlow and Cirq. With the TFQ PQC layer you can easily
embed your quantum part of the model within a standard classical Keras model.
"""
q_data_input = tf.keras.Input(shape = (), dtype = tf.dtypes.string)
theta = sympy.Symbol("theta")
q_model = cirq.Circuit(cirq.ry(theta)(qubit))
expectation = tfq.layers.PQC(q_model, cirq.Z(qubit))
expectation_output = expectation(q_data_input)
classifier = tf.keras.layers.Dense(2, activation = tf.keras.activations.softmax)
classifier_output = classifier(expectation_output)
"""You will now define the optimizer and loss functions for your model"""
model = tf.keras.Model(inputs = q_data_input,
outputs = classifier_output)
model.compile(optimizer = tf.keras.optimizers.Adam(learning_rate = 0.1),
loss = tf.keras.losses.CategoricalCrossentropy())
"""## Traaining the model
Training the model is just like training any other Keras model and is made easy.
"""
history = model.fit(x = quantum_data,
y = expected_labels,
epochs = 250)
"""## Evaluating the model"""
plt.plot(history.history['loss'])
plt.title('model loss')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train'], loc='upper left')
plt.show()
"""## Performing inference"""
noise = np.random.uniform(-0.25, 0.25, 2)
test_data = tfq.convert_to_tensor([
cirq.Circuit(cirq.ry(noise[0])(qubit)),
cirq.Circuit(cirq.ry(noise[1] + np.pi/2)(qubit)),
])
"""You can see in the below cell that our model does a good job with this data though it was very easy."""
predictions = model.predict(test_data)
predictions