R Notebook - project tidying up

Let’s break down your project into practical steps and address each part systematically.

Step 1: Prove MQPA with a Known Dataset

We’ll start by selecting a known dataset to prove the MQPA. For simplicity, we’ll use a dataset available in the QM9 dataset collection, which contains quantum chemical calculations.

Setup and Environment

Ensure you have the necessary libraries installed. We will use Python with libraries like Qiskit, Pennylane, PyQuil, and standard data science libraries such as Pandas and NumPy.

# Install necessary libraries
!pip install qiskit pennylane pyquil pandas numpy matplotlib scikit-learn

import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler
import qiskit
from qiskit import QuantumCircuit, Aer, transpile, assemble
from qiskit.visualization import plot_histogram
import pennylane as qml
from pyquil import Program
from pyquil.gates import H, CNOT
from pyquil.api import QuantumComputer
import matplotlib.pyplot as plt

Load and Preprocess Dataset

We’ll load the QM9 dataset and preprocess it. For simplicity, we’ll use a sample of the dataset.

# Load QM9 dataset (use a sample for demonstration)
df = pd.read_csv('path_to_qm9_sample.csv')  # Replace with the path to your QM9 dataset

# Preprocess data
# Select a subset of features and target
features = df[['feature1', 'feature2', 'feature3']]  # Replace with actual feature names
target = df['target']  # Replace with actual target name

# Standardize the features
scaler = StandardScaler()
features_scaled = scaler.fit_transform(features)

Implement MQPA

We’ll define a simple MQPA for demonstration. This will include creating quantum circuits and using quantum gates.

# Define MQPA function
def mqpa_circuit(params):
    n_qubits = 3  # Define number of qubits
    dev = qml.device('default.qubit', wires=n_qubits)

    @qml.qnode(dev)
    def circuit(params):
        for i in range(n_qubits):
            qml.RX(params[i], wires=i)
            qml.RY(params[i+n_qubits], wires=i)
            qml.RZ(params[i+2*n_qubits], wires=i)
        for i in range(n_qubits - 1):
            qml.CNOT(wires=[i, i+1])
        return [qml.expval(qml.PauliZ(i)) for i in range(n_qubits)]

    return circuit

# Example usage of MQPA
params = np.random.rand(3*3)  # Initialize parameters
result = mqpa_circuit(params)
print('MQPA result:', result)

Step 2: Create a Paper Teaching the Professor

We’ll create a detailed paper explaining the MQPA, including quantum data representation as outlined in the IEEE paper and the curriculum provided.

Outline of the Paper

1. Introduction
   • Overview of MQPA
   • Importance of quantum data representation
2. Quantum Data Representation
   • Discrete quantum states (qubits)
   • Continuous variable systems
   • Insights from IEEE paper (include relevant points)
3. Implementation of MQPA
   • Description of the dataset (QM9)
   • Preprocessing steps
   • Implementation details (include code snippets)
   • Results and analysis
4. Conclusion
   • Summary of findings
   • Future work

Step 3: Autoencoder and GAN with QM9 Dataset

Classical Autoencoder

import tensorflow as tf
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Dense

# Define classical autoencoder
input_dim = features_scaled.shape[1]
encoding_dim = 2

input_layer = Input(shape=(input_dim,))
encoder = Dense(encoding_dim, activation='relu')(input_layer)
decoder = Dense(input_dim, activation='sigmoid')(encoder)

autoencoder = Model(input_layer, decoder)
autoencoder.compile(optimizer='adam', loss='mse')

# Train autoencoder
autoencoder.fit(features_scaled, features_scaled, epochs=50, batch_size=32, shuffle=True)

Quantum Autoencoder

# Define quantum autoencoder
n_qubits = input_dim

dev = qml.device('default.qubit', wires=n_qubits)

@qml.qnode(dev)
def quantum_autoencoder(params, x):
    qml.templates.AmplitudeEmbedding(x, wires=range(n_qubits), normalize=True)
    for i in range(n_qubits):
        qml.RY(params[i], wires=i)
    for i in range(n_qubits - 1):
        qml.CNOT(wires=[i, i+1])
    return [qml.expval(qml.PauliZ(i)) for i in range(n_qubits)]

# Example usage
params = np.random.rand(n_qubits)
encoded = [quantum_autoencoder(params, x) for x in features_scaled]
print('Quantum encoded data:', encoded)

GAN for Particle Detection and Anomaly Detection

Classical and Quantum implementations will be provided with detailed steps.

Step 4: Step-by-Step Implementation

We will proceed step-by-step with practical examples and code snippets to ensure a clear understanding.

Step 5: Assess and Organize

We’ll assess each step and organize the information into a coherent structure for easy reference and learning.

Step 6: First-Person Responses

I will guide you through each step in a first-person narrative to facilitate understanding.

Let’s start with Step 1 and proceed from there. Please provide any specific details or preferences you have for the dataset or additional requirements.