app.py

import numpy as np
import random
import io
import duckdb
import gradio as gr
import math
from datetime import datetime
import PIL
import matplotlib.pyplot as plt
from PIL import Image
import pennylane as qml
import base64

from numpy import pi
import numpy as np
from qutip import *
from qutip.qip.operations import *
from qutip.qip.circuit import QubitCircuit, Gate

# Define a device
dev = qml.device('default.qubit', wires=10)

def plot_qutip_circuit():
    q = QubitCircuit(2, reverse_states=False)
    q.add_gate("CNOT", controls=[0], targets=[1])
    
    # Display the circuit as an image
    q.png  # Generates and renders the circuit diagram

    return q


# Hugging Face and DuckDB function placeholders
def store_in_hf_dataset(data):
    # Implement storing data in the Hugging Face dataset
    pass

def load_from_hf_dataset():
    # Implement loading data from the Hugging Face dataset
    return []

# Function to buffer the plot and return as PIL image
def buffer_plot_and_get(fig):
    buf = io.BytesIO()
    fig.savefig(buf, format='png')
    buf.seek(0)
    return PIL.Image.open(buf)

# Store image in bytes for DuckDB
def pil_image_to_bytes(image):
    img_byte_arr = io.BytesIO()
    image.save(img_byte_arr, format='PNG')
    return img_byte_arr.getvalue()

# Function to generate a random Hamiltonian
def generate_random_hamiltonian(num_qubits):
    terms = []
    for _ in range(random.randint(1, 5)):
        coeff = round(random.uniform(-1, 1), 2)
        pauli_ops = [random.choice(['I', 'X', 'Y', 'Z']) for _ in range(num_qubits)]
        term = f"{coeff} * {' '.join(pauli_ops)}"
        terms.append(term)
    return " + ".join(terms)

# Function to convert Hamiltonian to QASM code
def hamiltonian_to_qasm(hamiltonian, num_qubits):
    qasm_code = f"OPENQASM 2.0;\ninclude \"qelib1.inc\";\nqreg q[{num_qubits}];\n"
    rotations = {i: 0.0 for i in range(num_qubits)}
    terms = hamiltonian.split(" + ")
    
    for term in terms:
        coeff, paulis = term.split(" * ")
        paulis = paulis.split()
        coeff = float(coeff)

        for i, pauli in enumerate(paulis):
            if pauli == "X":
                qasm_code += f"x q[{i}];\n"
            elif pauli == "Y":
                qasm_code += f"ry(pi/2) q[{i}];\n"
            elif pauli == "Z":
                rotations[i] += coeff

    for i, angle in rotations.items():
        if angle != 0:
            angle_degrees = round(angle * 180 / math.pi, 2)
            qasm_code += f"rz({angle_degrees}) q[{i}];\n"
    
    return qasm_code

# Function for Trotter decomposition
def trotter_decomposition(hamiltonian, order):
    terms = hamiltonian.split(" + ")
    trotter_steps = []
    
    for term in terms:
        coeff, *pauli_ops = term.split(" * ")
        coeff = float(coeff)
        for _ in range(order):
            trotter_steps.append(f"exp({coeff / order}) * ({' * '.join(pauli_ops)})")
        for _ in range(order):
            trotter_steps.append(f"exp({-coeff / order}) * ({' * '.join(pauli_ops)})")
    
    return " + ".join(trotter_steps)

# Store data in DuckDB
def store_in_duckdb(data, db_file='quantum_hamiltonians.duckdb'):
    conn = duckdb.connect(database=db_file)
    conn.execute("""CREATE TABLE IF NOT EXISTS hamiltonians (
        id INTEGER,
        plot BLOB,
        hamiltonian VARCHAR,
        qasm_code VARCHAR,
        trotter_code VARCHAR,
        num_qubits INTEGER,
        trotter_order INTEGER,
        timestamp TIMESTAMP
    )""")
    conn.executemany("""INSERT INTO hamiltonians (id, plot, hamiltonian, qasm_code, trotter_code, num_qubits, trotter_order, timestamp)
                        VALUES (?, ?, ?, ?, ?, ?, ?, ?)""", data)
    conn.close()

# Load results from DuckDB and encode images to base64
def encode_image_from_blob(blob):
    img_buffer = io.BytesIO(blob)
    image = Image.open(img_buffer)
    img_str = base64.b64encode(img_buffer.getvalue()).decode("utf-8")
    return f'<img src="data:image/png;base64,{img_str}" style="max-width:500px;"/>'

def load_from_duckdb(db_file='quantum_hamiltonians.duckdb'):
    conn = duckdb.connect(database=db_file)
    df = conn.execute("SELECT * FROM hamiltonians").df()
    conn.close()

    # Convert results to HTML with images
    html_content = []
    for index, row in df.iterrows():
        plot_blob = row['plot']
        encoded_img = encode_image_from_blob(plot_blob)

        html_content.append(f"""
            <table style='width: 100%; border-collapse: collapse; margin: 10px;'>
                <tr>
                    <td style='width: 30%; text-align: center;'>
                        <h3>Circuit {index + 1}</h3>
                        {encoded_img}  <!-- Display the image -->
                    </td>        
                    <td style='padding: 10px;'>
                        <table style='width: 100%; border-collapse: collapse;'>
                            <tr>
                                <td><strong>Hamiltonian:</strong></td><td>{row['hamiltonian']}</td>
                            </tr>
                            <tr>
                                <td><strong>QASM Representation:</strong></td><td>{row['qasm_code']}</td>
                            </tr>
                            <tr>
                                <td><strong>Trotter Decomposition:</strong></td><td>{row['trotter_code']}</td>
                            </tr>
                            <tr>
                                <td><strong>Number of Qubits:</strong></td><td>{row['num_qubits']}</td>
                            </tr>
                            <tr>
                                <td><strong>Trotter Order:</strong></td><td>{row['trotter_order']}</td>
                            </tr>
                            <tr>
                                <td><strong>Timestamp:</strong></td><td>{row['timestamp']}</td>
                            </tr>
                        </table>
                    </td>
                </tr>
            </table>
        """)

    return "".join(html_content)

# Function to generate Hamiltonians
def generate_hamiltonians(num_hamiltonians, selected_qubits, selected_order, write_to_hf, write_to_duckdb):
    results_table = []
    timestamp = datetime.now()

    for i in range(num_hamiltonians):
        num_qubits = random.choice(selected_qubits)
        order = selected_order
        hamiltonian = generate_random_hamiltonian(num_qubits)
        qasm_code = hamiltonian_to_qasm(hamiltonian, num_qubits)
        trotter_code = trotter_decomposition(hamiltonian, order)

        # Create a dummy plot (replace with actual plot creation logic)
        fig, ax = plt.subplots()
        ax.plot([0, 1], [0, 1])
        circuit_plot_image = buffer_plot_and_get(fig)
        circuit_plot_bytes = pil_image_to_bytes(circuit_plot_image)

        # Append data to results table
        results_table.append((i + 1, circuit_plot_bytes, hamiltonian, qasm_code, trotter_code, num_qubits, order, timestamp))

    # Write data to Hugging Face dataset if selected
    if write_to_hf:
        store_in_hf_dataset(results_table)
    
    # Write data to DuckDB if selected
    if write_to_duckdb:
        store_in_duckdb(results_table)

# Function to load results from either DuckDB or Hugging Face dataset
def load_results(load_from_hf, load_from_duckdb1):
    if load_from_hf:
        return load_from_hf_dataset()
    if load_from_duckdb1:
        return load_from_duckdb()

# Gradio app
with gr.Blocks() as app:
    gr.Markdown("# Quantum Hamiltonian Generator")
    
    with gr.Tab("Generate Hamiltonians"):
        num_hamiltonians = gr.Dropdown(label="Select number of Hamiltonians to generate", choices=[1, 10, 20, 100], value=20)
        qubit_choices = [1, 2, 3, 4, 5, 6]
        selected_qubits = gr.CheckboxGroup(label="Select number of qubits", choices=qubit_choices, value=[1])
        order_choices = [1, 2, 3, 4, 5]
        selected_order = gr.Dropdown(label="Select Trotter order", choices=order_choices, value=1)

        # Checkboxes for writing to HF dataset and DuckDB
        write_to_hf = gr.Checkbox(label="Write to Hugging Face dataset", value=False)
        write_to_duckdb = gr.Checkbox(label="Write to DuckDB", value=True)

        generate_button = gr.Button("Generate Hamiltonians")
        status = gr.Markdown("Click 'Generate Hamiltonians' to start the process.")

        def update_status(num, qubits, order, write_hf, write_duckdb):
            generate_hamiltonians(num, qubits, order, write_hf, write_duckdb)
            return "Data stored as per selection."

        generate_button.click(update_status, inputs=[num_hamiltonians, selected_qubits, selected_order, write_to_hf, write_to_duckdb], outputs=status)

    with gr.Tab("View Results"):
        load_from_hf = gr.Checkbox(label="Load from Hugging Face dataset", value=False)
        load_from_duckdb1 = gr.Checkbox(label="Load from DuckDB", value=True)

        load_button = gr.Button("Load Results")
        output_display = gr.HTML()

        load_button.click(load_results, inputs=[load_from_hf, load_from_duckdb1], outputs=output_display)

app.launch()