A statevector simulator for quantum circuits implemented from scratch using Python and NumPy. This simulator demonstrates both naive matrix multiplication and advanced tensor multiplication methods for simulating quantum circuits.
Quantum computing leverages the principles of quantum mechanics to perform computations. Unlike classical bits, which can be either 0 or 1, quantum bits (qubits) can exist in superpositions of states. This simulator allows you to:
- Initialize quantum states.
- Apply quantum gates (X, H, CNOT).
- Simulate quantum circuits using matrix and tensor multiplication.
- Sample measurement outcomes.
- Compute expectation values of observables.
- Naive Simulation: Uses matrix multiplication to apply gates.
- Advanced Simulation: Utilizes tensor contraction for efficient gate application.
- Measurement Sampling: Simulate quantum measurements based on state probabilities.
- Expectation Values: Compute exact expectation values of quantum operators.
- Performance Analysis: Compare runtime performance between simulation methods.
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Clone the Repository
git clone https://github.com/your-username/Quantum-Statevector-Simulator.git cd Quantum-Statevector-Simulator -
Install Dependencies
pip install -r requirements.txt
For best experience use the .pynb file
Another way reviewing code is given below
Simulating a Quantum Circuit with Matrix Multiplication
from src.Naive-Matrix import simulate_circuit_matrix
n_qubits = 3
final_state = simulate_circuit_matrix(n_qubits)
print("Final State Vector:")
print(final_state)
Simulating a Quantum Circuit with Tensor Multiplication
from src.Advanced-Tensor import simulate_circuit_tensor
n_qubits = 3
final_state = simulate_circuit_tensor(n_qubits)
print("Final State Tensor:")
print(final_state)
Sampling Measurements
from src.Advanced-Tensor import simulate_circuit_tensor
from src.Bonus import sample_measurements
n_qubits = 3
state = simulate_circuit_tensor(n_qubits)
samples = sample_measurements(state, num_samples=1000)
bitstrings = [format(sample, '03b') for sample in samples]
print(f"Sampled bitstrings: {bitstrings[:10]}")
Computing Expectation Values
from src.Advanced-Tensor import simulate_circuit_tensor
from src.Bonus import compute_expectation_value
import numpy as np
n_qubits = 3
state = simulate_circuit_tensor(n_qubits)
Z = np.array([[1, 0],
[0, -1]], dtype=complex)
expectation = compute_expectation_value(state, Z, target_qubit=0)
print(f"Expectation value of Z on qubit 0: {expectation}")
Refer to the notebooks directory for detailed examples and explanations.
The simulator includes runtime analysis comparing naive matrix multiplication and tensor multiplication methods. See the generated plots in the plots directory. Also if you go through the notebook then can check the plots there as well.