Qubits

Overview

The Qubits is a Python-based library for simulating quantum circuits. It leverages PyTorch to efficiently represent and manipulate quantum states and gates. This library is designed for developers and researchers who want to prototype and experiment with quantum algorithms without needing access to actual quantum hardware.

Features

• Quantum Gates: Includes common gates like Pauli-X, Pauli-Y, Pauli-Z, Hadamard, CNOT, and parameterized rotation gates (Rx, Ry, Rz).
• Quantum and Classical Registers: Simulate quantum states and classical measurement storage.
• Gate Application: Apply single- and multi-qubit gates on quantum circuits.
• Measurement: Measure qubits and collapse the quantum state.
• Lightweight: Minimal dependencies with a PyTorch backend.

Installation

You can install the library using pip after cloning the repository:

pip install Kala_Qubits

Usage

Import the Library

from Qubits import Gate, QuantumCircuit

Create a Quantum Circuit

# Create a 2-qubit circuit with 2 classical bits
qc = QuantumCircuit(2, 2)

# Add gates
qc.apply(Gate.hadamard(), [0])  # Apply Hadamard to qubit 0
qc.apply(Gate.cnot(), [0, 1])  # Apply CNOT with qubit 0 as control and qubit 1 as target

# Execute the circuit
qc.execute()

# Measure all qubits
result = qc.measure_all()
print(f"Measurement outcome: {result}")

Add Custom Gates

import numpy as np

# Define a custom gate matrix
custom_matrix = np.array([[1, 0], [0, -1]])
custom_gate = Gate.custom(custom_matrix)

# Apply the custom gate
qc.apply(custom_gate, [0])

API Reference

Gate

Methods

• Gate.pauli_x()
• Gate.pauli_y()
• Gate.pauli_z()
• Gate.hadamard()
• Gate.cnot()
• Gate.rotation_x(theta)
• Gate.rotation_y(theta)
• Gate.rotation_z(theta)
• Gate.custom(matrix)

QuantumCircuit

Methods

• apply(gate, qubits)
• measure(qubit, cbit)
• measure_all()
• execute()

QuantumRegister and ClassicalRegister

Used internally to manage quantum states and classical bits.

Contributing

1. Fork the repository.
2. Create a new branch for your feature or bugfix.
3. Submit a pull request with a detailed explanation of your changes.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Acknowledgments

• Inspired by Qiskit and PyTorch.
• Designed for educational and research purposes.