diff --git a/parrondos_qwalk_1D.py b/parrondos_qwalk_1D.py new file mode 100644 index 0000000..567ee82 --- /dev/null +++ b/parrondos_qwalk_1D.py @@ -0,0 +1,165 @@ +# %% +import numpy as np +import matplotlib.pyplot as plt +from quantumwalks import run_quantum_walk_simulation +from quantumwalks.discrete_walk import QuantumWalk +from quantumwalks.utils.operators import create_shift_operators + +def create_custom_coin(alpha: float, beta: float, gamma: float) -> np.ndarray: + """ + Create a custom coin operator U(α,β,γ) using vectorized operations. + """ + return np.array([ + [np.exp(1j * alpha) * np.cos(beta), -np.exp(-1j * gamma) * np.sin(beta)], + [np.exp(1j * gamma) * np.sin(beta), np.exp(-1j * alpha) * np.cos(beta)] + ], dtype=np.complex128) + +def calculate_lr_difference_alternating_coins(qw: QuantumWalk, coin_A: np.ndarray, coin_B: np.ndarray) -> np.ndarray: + """ + Calculate P_L - P_R for each step of the walk using alternating coins A and B. + """ + # Initialize state + posn0 = np.zeros(qw.P) + posn0[qw.N] = 1 + psi = np.kron(posn0, qw.initial_coin_state) + + # Pre-allocate arrays + differences = np.zeros(qw.N + 1) + positions = np.arange(-qw.N, qw.N + 1) + + # Create measurement operators matrix once + measurement_ops = np.array([ + np.kron(np.outer(np.eye(qw.P)[i], np.eye(qw.P)[i]), np.eye(2)) + for i in range(qw.P) + ]) + + # Create shift operators using the utility function + shift_plus, shift_minus = create_shift_operators(qw.P) + shift_op = (np.kron(shift_plus, np.outer(qw.coin0, qw.coin0)) + + np.kron(shift_minus, np.outer(qw.coin1, qw.coin1))) + + # Calculate evolution operators for both coins + U_A = shift_op @ np.kron(np.eye(qw.P), coin_A) + U_B = shift_op @ np.kron(np.eye(qw.P), coin_B) + + # Calculate for each step + for step in range(qw.N + 1): + # Vectorized probability calculation + projections = measurement_ops @ psi + prob = np.real(np.sum(projections * np.conjugate(projections), axis=1)) + + # Vectorized P_L and P_R calculation + P_L = np.sum(prob[positions < 0]) + P_R = np.sum(prob[positions >= 0]) + differences[step] = P_L - P_R + + # Evolve state with alternating coin operators + if step < qw.N: + if step % 2 == 0: + psi = U_A @ psi # Apply coin A + else: + psi = U_B @ psi # Apply coin B + + return differences, prob # Return both differences and final probability distribution + +def run_simulation_alternating_coins(steps: int, + alpha_A: float, beta_A: float, gamma_A: float, + alpha_B: float, beta_B: float, gamma_B: float): + """ + Run simulation with alternating coins A and B. + """ + # Create both coin operators + coin_A = create_custom_coin(alpha_A, beta_A, gamma_A) + coin_B = create_custom_coin(alpha_B, beta_B, gamma_B) + initial_state = np.array([1, -1], dtype=np.complex128) / np.sqrt(2) + + print(f"Running quantum walk simulation with alternating coins:") + print(f"Coin A parameters: α={alpha_A:.2f}, β={beta_A:.2f}, γ={gamma_A:.2f}") + print(f"Coin B parameters: α={alpha_B:.2f}, β={beta_B:.2f}, γ={gamma_B:.2f}") + + # Create quantum walk object (using coin_A as default) + qw = QuantumWalk(steps, initial_state, coin_A) + + # Calculate differences and probabilities using alternating coins + differences, prob = calculate_lr_difference_alternating_coins(qw, coin_A, coin_B) + + # Plot results + plot_results(qw, prob, 0.0, differences, steps) # Using 0.0 as execution time placeholder + + return qw, differences, prob + +def plot_results(qw: QuantumWalk, prob: np.ndarray, + exec_time: float, differences: np.ndarray, steps: int): + """ + Plot both probability distribution and P_L - P_R differences. + """ + # Plot probability distribution + positions = np.arange(-qw.N, qw.N + 1) + + plt.figure(figsize=(12, 8)) + plt.plot(positions, prob, 'b-', label='Probability', linewidth=1.5) + plt.plot(positions, prob, 'ro', markersize=4) + plt.fill_between(positions, prob, alpha=0.3) + plt.grid(True, alpha=0.3) + plt.xlabel('Position', fontsize=12) + plt.ylabel('Probability', fontsize=12) + plt.title(f'Quantum Walk Distribution after {steps} steps\n(Alternating Coins)', fontsize=14) + plt.legend() + plt.tight_layout() + plt.show() + + # Plot P_L - P_R differences + plt.figure(figsize=(12, 8)) + x = np.arange(steps + 1) + plt.plot(x, differences, 'b-', label='P_L - P_R', linewidth=1.5) + plt.plot(x, differences, 'ro', markersize=4) + + # Set y-axis range from -1 to 1 + plt.ylim(-1, 1) + + plt.grid(True, alpha=0.3) + plt.xlabel('Step', fontsize=12) + plt.ylabel('P_L - P_R', fontsize=12) + plt.title('Difference between Left and Right Probabilities\n(Alternating Coins)', fontsize=14) + plt.legend() + plt.tight_layout() + plt.show() + + +# %% + +if __name__ == "__main__": + # Set parameters + steps = 800 + + # Parameters for Coin A + alpha_A = -51.0/180 * np.pi # Converting to radians + beta_A = 45.0/180 * np.pi + gamma_A = 0.0/180 * np.pi + + # Parameters for Coin B + alpha_B = 0.0/180 * np.pi + beta_B = 88.0/180 * np.pi + gamma_B = -16.0/180 * np.pi + + # Run simulation with alternating coins + qw, differences, prob = run_simulation_alternating_coins( + steps, + alpha_A, beta_A, gamma_A, + alpha_B, beta_B, gamma_B + ) + + # Print some statistics + positions = np.arange(-qw.N, qw.N + 1) + mean_pos = np.average(positions, weights=prob) + std_dev = np.sqrt(np.average((positions - mean_pos)**2, weights=prob)) + + print("\nFinal Statistics:") + print(f"Mean Position: {mean_pos:.4f}") + print(f"Standard Deviation: {std_dev:.4f}") + print(f"Max Probability: {np.max(prob):.4f}") + +# %% + + + diff --git a/poetry.lock b/poetry.lock new file mode 100644 index 0000000..15d779a --- /dev/null +++ b/poetry.lock @@ -0,0 +1,1482 @@ +# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand. + +[[package]] +name = "appnope" +version = "0.1.4" +description = "Disable App Nap on macOS >= 10.9" +optional = false +python-versions = ">=3.6" +files = [ + {file = "appnope-0.1.4-py2.py3-none-any.whl", hash = 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+++ b/pyproject.toml @@ -0,0 +1,26 @@ +[tool.poetry] +name = "quantumwalks" +version = "0.1.0" +description = "Quantum walk simulation for graphs" +authors = ["Jishnu "] +license = "MIT" +readme = "README.md" + +[tool.poetry.dependencies] +python = "^3.12" +numpy = "^2.2.1" +matplotlib = "^3.10.0" +seaborn = "^0.13.2" +ipywidgets = "^8.1.5" +tdqm = "^0.0.1" +pandas = "^2.2.3" +scienceplots = "^2.1.1" + + +[tool.poetry.group.dev.dependencies] +pytest = "^8.3.4" +ipykernel = "^6.29.5" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/quantumwalks/__init__.py b/quantumwalks/__init__.py new file mode 100644 index 0000000..a3018dc --- /dev/null +++ b/quantumwalks/__init__.py @@ -0,0 +1,3 @@ +from .discrete_walk import QuantumWalk, run_quantum_walk_simulation + +__all__ = ['QuantumWalk', 'run_quantum_walk_simulation'] diff --git a/quantumwalks/__pycache__/__init__.cpython-313.pyc b/quantumwalks/__pycache__/__init__.cpython-313.pyc new file mode 100644 index 0000000..4218c12 Binary files /dev/null and b/quantumwalks/__pycache__/__init__.cpython-313.pyc differ diff --git a/quantumwalks/__pycache__/discrete_walk.cpython-313.pyc b/quantumwalks/__pycache__/discrete_walk.cpython-313.pyc new file mode 100644 index 0000000..e560444 Binary files /dev/null and b/quantumwalks/__pycache__/discrete_walk.cpython-313.pyc differ diff --git a/quantumwalks/__pycache__/visualization.cpython-313.pyc b/quantumwalks/__pycache__/visualization.cpython-313.pyc new file mode 100644 index 0000000..9861cac Binary files /dev/null and b/quantumwalks/__pycache__/visualization.cpython-313.pyc differ diff --git a/quantumwalks/discrete_walk.py b/quantumwalks/discrete_walk.py new file mode 100644 index 0000000..9f37c2c --- /dev/null +++ b/quantumwalks/discrete_walk.py @@ -0,0 +1,178 @@ +import numpy as np +import time +from typing import Tuple, Optional +from tqdm import tqdm +from .utils.operators import create_hadamard, create_shift_operators +from .utils.visualization import plot_distribution + +class QuantumWalk: + def __init__(self, + num_steps: int, + initial_coin_state: Optional[np.ndarray] = None, + coin_operator: Optional[np.ndarray] = None): + """ + Initialize Quantum Walk parameters + + Args: + num_steps (int): Number of steps for the quantum walk + initial_coin_state (np.ndarray, optional): Custom initial coin state. + Default: (coin0 - i*coin1)/√2 + coin_operator (np.ndarray, optional): Custom 2x2 coin operator matrix. + Default: Hadamard operator + """ + self.N = num_steps + self.P = 2 * num_steps + 1 # Total number of positions + + # Validate and set custom coin operator + if coin_operator is not None: + if coin_operator.shape != (2, 2): + raise ValueError("Coin operator must be a 2x2 matrix") + self.custom_coin = coin_operator + else: + self.custom_coin = None + + # Setup operators first (to initialize coin0 and coin1) + self.setup_operators() + + # Validate and set custom initial coin state + if initial_coin_state is not None: + if initial_coin_state.shape != (2,): + raise ValueError("Initial coin state must be a 2D vector") + if not np.isclose(np.linalg.norm(initial_coin_state), 1): + raise ValueError("Initial coin state must be normalized") + self.initial_coin_state = initial_coin_state + else: + # Default initial state: (coin0 - i*coin1)/√2 + self.initial_coin_state = (self.coin0 - 1j*self.coin1)/np.sqrt(2) + + def setup_operators(self): + """Setup quantum operators and initial states""" + # Coin basis states + self.coin0 = np.array([1, 0], dtype=np.complex128) + self.coin1 = np.array([0, 1], dtype=np.complex128) + + # Coin operator (Hadamard by default) + self.H = self.custom_coin if self.custom_coin is not None else create_hadamard() + + # Shift operators + self.shift_plus, self.shift_minus = create_shift_operators(self.P) + + def evolve_state(self, state: np.ndarray, U: np.ndarray) -> np.ndarray: + """ + Evolve quantum state using evolution operator + + Args: + state (np.ndarray): Current quantum state + U (np.ndarray): Evolution operator + + Returns: + np.ndarray: Evolved quantum state + """ + return U.dot(state) + + def calculate_evolution_operator(self) -> np.ndarray: + """ + Calculate the evolution operator + + Returns: + np.ndarray: Complete evolution operator + """ + # Coin operator + coin_op = np.kron(np.eye(self.P), self.H) + + # Shift operator + shift_op = (np.kron(self.shift_plus, np.outer(self.coin0, self.coin0)) + + np.kron(self.shift_minus, np.outer(self.coin1, self.coin1))) + + return shift_op.dot(coin_op) + + def simulate(self) -> Tuple[np.ndarray, float]: + """ + Perform quantum walk simulation + + Returns: + Tuple[np.ndarray, float]: Probability distribution and execution time + """ + start_time = time.time() + + # Initial state + posn0 = np.zeros(self.P) + posn0[self.N] = 1 # Start at center + psi0 = np.kron(posn0, self.initial_coin_state) + + # Evolution operator + U = self.calculate_evolution_operator() + + # Evolve the system + psiN = np.linalg.matrix_power(U, self.N).dot(psi0) + + # Calculate probabilities + prob = np.zeros(self.P) + for k in tqdm(range(self.P), desc="Calculating probabilities"): + posn = np.zeros(self.P) + posn[k] = 1 + M_hat_k = np.kron(np.outer(posn, posn), np.eye(2)) + proj = M_hat_k.dot(psiN) + prob[k] = np.real(proj.dot(np.conjugate(proj))) + + execution_time = time.time() - start_time + return prob, execution_time + + def analyze_statistics(self, prob: np.ndarray) -> dict: + """ + Analyze statistical properties of the quantum walk + + Args: + prob (np.ndarray): Probability distribution + + Returns: + dict: Dictionary containing statistical measures + """ + positions = np.arange(-self.N, self.N + 1) + mean_position = np.average(positions, weights=prob) + variance = np.average((positions - mean_position)**2, weights=prob) + std_dev = np.sqrt(variance) + + return { + "mean_position": mean_position, + "standard_deviation": std_dev, + "variance": variance, + "max_probability": np.max(prob), + "min_probability": np.min(prob) + } + + def plot_results(self, prob: np.ndarray, exec_time: float): + """ + Plot the probability distribution + + Args: + prob (np.ndarray): Probability distribution + exec_time (float): Execution time + """ + positions = np.arange(-self.N, self.N + 1) + plot_distribution(positions, prob, self.N, exec_time) + + +def run_quantum_walk_simulation(steps: int, + initial_coin_state: Optional[np.ndarray] = None, + coin_operator: Optional[np.ndarray] = None): + """ + Run complete quantum walk simulation with analysis + + Args: + steps (int): Number of steps for the quantum walk + initial_coin_state (np.ndarray, optional): Custom initial coin state + coin_operator (np.ndarray, optional): Custom coin operator + """ + # Initialize and run simulation + qw = QuantumWalk(steps, initial_coin_state, coin_operator) + prob, exec_time = qw.simulate() + + # Plot results + qw.plot_results(prob, exec_time) + + # Analyze and print statistics + stats = qw.analyze_statistics(prob) + print("\nQuantum Walk Statistics:") + for key, value in stats.items(): + print(f"{key.replace('_', ' ').title()}: {value:.4f}") diff --git a/quantumwalks/discrete_walk_numba.py b/quantumwalks/discrete_walk_numba.py new file mode 100644 index 0000000..cc4eaa8 --- /dev/null +++ b/quantumwalks/discrete_walk_numba.py @@ -0,0 +1,147 @@ +import numpy as np +import matplotlib.pyplot as plt +from typing import Tuple +from numba import jit +import time +from tqdm import tqdm + +class QuantumWalk: + def __init__(self, num_steps: int): + """ + Initialize Quantum Walk parameters + """ + self.N = num_steps + self.P = 2 * num_steps + 1 + self.setup_operators() + + @staticmethod + @jit(nopython=True) + def create_hadamard() -> np.ndarray: + """Create Hadamard operator with Numba optimization""" + return np.array([[1, 1], [1, -1]]) / np.sqrt(2) + + def setup_operators(self): + """Setup quantum operators""" + # Coin basis states + self.coin0 = np.array([1, 0], dtype=np.complex128) + self.coin1 = np.array([0, 1], dtype=np.complex128) + + # Hadamard operator + self.H = self.create_hadamard() + + # Shift operators + self.shift_plus = np.roll(np.eye(self.P), 1, axis=0) + self.shift_minus = np.roll(np.eye(self.P), -1, axis=0) + + @jit(nopython=True) + def evolve_state(self, state: np.ndarray, U: np.ndarray) -> np.ndarray: + """Evolve quantum state using evolution operator""" + return U.dot(state) + + def calculate_evolution_operator(self) -> np.ndarray: + """Calculate the evolution operator""" + # Coin operator + coin_op = np.kron(np.eye(self.P), self.H) + + # Shift operator + shift_op = (np.kron(self.shift_plus, np.outer(self.coin0, self.coin0)) + + np.kron(self.shift_minus, np.outer(self.coin1, self.coin1))) + + return shift_op.dot(coin_op) + + def simulate(self) -> Tuple[np.ndarray, float]: + """ + Perform quantum walk simulation + Returns: probability distribution and execution time + """ + start_time = time.time() + + # Initial state + posn0 = np.zeros(self.P) + posn0[self.N] = 1 # Start at center + psi0 = np.kron(posn0, (self.coin0 + self.coin1)/np.sqrt(2)) + + # Evolution operator + U = self.calculate_evolution_operator() + + # Evolve the system + psiN = np.linalg.matrix_power(U, self.N).dot(psi0) + + # Calculate probabilities + prob = np.zeros(self.P) + for k in tqdm(range(self.P), desc="Calculating probabilities"): + posn = np.zeros(self.P) + posn[k] = 1 + M_hat_k = np.kron(np.outer(posn, posn), np.eye(2)) + proj = M_hat_k.dot(psiN) + prob[k] = np.real(proj.dot(np.conjugate(proj))) + + execution_time = time.time() - start_time + return prob, execution_time + + def plot_distribution(self, prob: np.ndarray, exec_time: float): + """ + Plot the probability distribution with enhanced visualization + """ + positions = np.arange(-self.N, self.N + 1) + + plt.figure(figsize=(12, 8)) + + # Main probability distribution + plt.plot(positions, prob, 'b-', label='Probability', linewidth=1.5) + plt.plot(positions, prob, 'ro', markersize=4) + + # Fill area under the curve + plt.fill_between(positions, prob, alpha=0.3) + + # Formatting + plt.grid(True, alpha=0.3) + plt.xlabel('Position', fontsize=12) + plt.ylabel('Probability', fontsize=12) + plt.title(f'Quantum Walk Distribution after {self.N} steps\n' + f'Execution Time: {exec_time:.2f} seconds', fontsize=14) + + # Add statistical information + plt.text(0.02, 0.98, f'Standard Deviation: {np.std(prob):.4f}\n' + f'Mean Position: {np.average(positions, weights=prob):.4f}', + transform=plt.gca().transAxes, + bbox=dict(facecolor='white', alpha=0.8), + verticalalignment='top') + + plt.legend() + plt.tight_layout() + plt.show() + + def analyze_statistics(self, prob: np.ndarray) -> dict: + """ + Analyze statistical properties of the quantum walk + """ + positions = np.arange(-self.N, self.N + 1) + mean_position = np.average(positions, weights=prob) + variance = np.average((positions - mean_position)**2, weights=prob) + std_dev = np.sqrt(variance) + + return { + "mean_position": mean_position, + "standard_deviation": std_dev, + "variance": variance, + "max_probability": np.max(prob), + "min_probability": np.min(prob) + } + +def run_quantum_walk_simulation(steps: int): + """ + Run complete quantum walk simulation with analysis + """ + # Initialize and run simulation + qw = QuantumWalk(steps) + prob, exec_time = qw.simulate() + + # Plot results + qw.plot_distribution(prob, exec_time) + + # Analyze and print statistics + stats = qw.analyze_statistics(prob) + print("\nQuantum Walk Statistics:") + for key, value in stats.items(): + print(f"{key.replace('_', ' ').title()}: {value:.4f}") diff --git a/quantumwalks/utils/__init__.py b/quantumwalks/utils/__init__.py new file mode 100644 index 0000000..3c3550c --- /dev/null +++ b/quantumwalks/utils/__init__.py @@ -0,0 +1,4 @@ +from .operators import create_hadamard, create_shift_operators +from .visualization import plot_distribution + +__all__ = ['create_hadamard', 'create_shift_operators', 'plot_distribution'] diff --git a/quantumwalks/utils/__pycache__/__init__.cpython-313.pyc b/quantumwalks/utils/__pycache__/__init__.cpython-313.pyc new file mode 100644 index 0000000..803b9e4 Binary files /dev/null and b/quantumwalks/utils/__pycache__/__init__.cpython-313.pyc differ diff --git a/quantumwalks/utils/__pycache__/operators.cpython-313.pyc b/quantumwalks/utils/__pycache__/operators.cpython-313.pyc new file mode 100644 index 0000000..71288a1 Binary files /dev/null and b/quantumwalks/utils/__pycache__/operators.cpython-313.pyc differ diff --git a/quantumwalks/utils/__pycache__/visualization.cpython-313.pyc b/quantumwalks/utils/__pycache__/visualization.cpython-313.pyc new file mode 100644 index 0000000..f186860 Binary files /dev/null and b/quantumwalks/utils/__pycache__/visualization.cpython-313.pyc differ diff --git a/quantumwalks/utils/operators.py b/quantumwalks/utils/operators.py new file mode 100644 index 0000000..a4c94da --- /dev/null +++ b/quantumwalks/utils/operators.py @@ -0,0 +1,11 @@ +import numpy as np + +def create_hadamard() -> np.ndarray: + """Create Hadamard operator""" + return np.array([[1, 1], [1, -1]]) / np.sqrt(2) + +def create_shift_operators(size: int) -> tuple[np.ndarray, np.ndarray]: + """Create shift operators""" + shift_plus = np.roll(np.eye(size), 1, axis=0) + shift_minus = np.roll(np.eye(size), -1, axis=0) + return shift_plus, shift_minus diff --git a/quantumwalks/utils/visualization.py b/quantumwalks/utils/visualization.py new file mode 100644 index 0000000..2dc1b76 --- /dev/null +++ b/quantumwalks/utils/visualization.py @@ -0,0 +1,33 @@ +import matplotlib.pyplot as plt +import scienceplots +import numpy as np + +plt.style.use(['science','notebook']) + +def plot_distribution(positions: np.ndarray, prob: np.ndarray, + steps: int, exec_time: float): + plt.figure(figsize=(12, 8)) + + # Main probability distribution + plt.plot(positions, prob, 'b-', label='Probability', linewidth=1.5) + plt.plot(positions, prob, 'ro', markersize=4) + + # Fill area under the curve + plt.fill_between(positions, prob, alpha=0.3) + + # Formatting + plt.grid(True, alpha=0.3) + plt.xlabel('Position', fontsize=12) + plt.ylabel('Probability', fontsize=12) + plt.title(f'Quantum Walk Distribution after {steps} steps', fontsize=14) + + # Add statistical information + plt.text(0.02, 0.98, f'Median Position: {np.median(positions):.4f}\n' + f'Mean Position: {np.average(positions, weights=prob):.4f}', + transform=plt.gca().transAxes, + bbox=dict(facecolor='white', alpha=0.8), + verticalalignment='top') + + plt.legend() + plt.tight_layout() + plt.show() diff --git a/tests/__pycache__/test_discrete_walk.cpython-313-pytest-8.3.4.pyc b/tests/__pycache__/test_discrete_walk.cpython-313-pytest-8.3.4.pyc new file mode 100644 index 0000000..ff69da7 Binary files /dev/null and b/tests/__pycache__/test_discrete_walk.cpython-313-pytest-8.3.4.pyc differ diff --git a/tests/__pycache__/test_operator.cpython-313-pytest-8.3.4.pyc b/tests/__pycache__/test_operator.cpython-313-pytest-8.3.4.pyc new file mode 100644 index 0000000..9067e86 Binary files /dev/null and b/tests/__pycache__/test_operator.cpython-313-pytest-8.3.4.pyc differ diff --git a/tests/__pycache__/test_visualization.cpython-313-pytest-8.3.4.pyc b/tests/__pycache__/test_visualization.cpython-313-pytest-8.3.4.pyc new file mode 100644 index 0000000..2591571 Binary files /dev/null and b/tests/__pycache__/test_visualization.cpython-313-pytest-8.3.4.pyc differ diff --git a/tests/test_discrete_walk.py b/tests/test_discrete_walk.py new file mode 100644 index 0000000..6b30ba1 --- /dev/null +++ b/tests/test_discrete_walk.py @@ -0,0 +1,27 @@ +import pytest +import numpy as np +from quantumwalks import QuantumWalk + +def test_quantum_walk_initialization(): + steps = 10 + qw = QuantumWalk(steps) + assert qw.N == steps + assert qw.P == 2 * steps + 1 + +def test_probability_conservation(): + steps = 5 + qw = QuantumWalk(steps) + prob, _ = qw.simulate() + assert np.isclose(np.sum(prob), 1.0, atol=1e-10) + +def test_statistics_calculation(): + steps = 5 + qw = QuantumWalk(steps) + prob, _ = qw.simulate() + stats = qw.analyze_statistics(prob) + + assert "mean_position" in stats + assert "standard_deviation" in stats + assert "variance" in stats + assert "max_probability" in stats + assert "min_probability" in stats diff --git a/tests/test_operator.py b/tests/test_operator.py new file mode 100644 index 0000000..88c54b7 --- /dev/null +++ b/tests/test_operator.py @@ -0,0 +1,15 @@ +import pytest +import numpy as np +from quantumwalks.utils.operators import create_hadamard + +def test_hadamard_properties(): + H = create_hadamard() + # Test unitarity + assert np.allclose(H.dot(H.T), np.eye(2)) + # Test hermiticity + assert np.allclose(H, H.T) + +def test_hadamard_normalization(): + H = create_hadamard() + assert np.isclose(np.linalg.norm(H.dot(np.array([1, 0]))), 1.0) + assert np.isclose(np.linalg.norm(H.dot(np.array([0, 1]))), 1.0)