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 qutip import * from qutip.qip.operations import * from qutip.qip.circuit import QubitCircuit, Gate import pennylane as qml from math import pi # Define a Pennylane device dev = qml.device('default.qubit', wires=10) # Hugging Face and DuckDB function placeholders def store_in_hf_dataset(data): pass def load_from_hf_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() # Encode the image in base64 to display in HTML 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'' # 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 to parse QASM code and create Pennylane circuit def qasm_to_pennylane(qasm_code): qasm_lines = qasm_code.split("\n") num_qubits = int(qasm_lines[2].split('[')[1].split(']')[0]) # Extract number of qubits from QASM @qml.qnode(dev) def circuit(): for line in qasm_lines: if "x" in line: qml.PauliX(int(line.split('q[')[1].split(']')[0])) elif "rz" in line: angle = float(line.split('(')[1].split(')')[0]) qml.RZ(angle, int(line.split('q[')[1].split(']')[0])) elif "ry" in line: qml.RY(pi / 2, int(line.split('q[')[1].split(']')[0])) return qml.state() return circuit # 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() # Function to load results from DuckDB 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"""

Circuit {index + 1}

{encoded_img}
Hamiltonian:{row['hamiltonian']}
QASM Representation:{row['qasm_code']}
Trotter Decomposition:{row['trotter_code']}
Number of Qubits:{row['num_qubits']}
Trotter Order:{row['trotter_order']}
Timestamp:{row['timestamp']}
""") 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) # Generate Pennylane circuit from QASM code circuit = qasm_to_pennylane(qasm_code) # Draw the Pennylane circuit and save as an image fig, ax = qml.draw_mpl(circuit)() 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() # 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) # 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()