amin-qvm 2.0.0

Amin-QVM: Quantum Computing Library With Built In Novum QVM

amin-qvm Library Documentation

Overview

The amin-qvm library has good tools for simulating quantum algorithms and neural networks. It has classes for quantum state manipulation, representation of quantum gates, and implementations of algorithms like Grover and Deutsch-Jozsa.

Modules in the Library

novum

Class: Novum

This is the primary class representing a quantum system.

• Initialization: Novum(num_qubits: int) Initiates a quantum system with a specified number of qubits.

• Methods:

  • execute_qasm(qasm_code: str): Executes QASM code on the quantum system.
  • convert_to_binary(input_data: str, num_bits: int): Converts CSV data into binary representation.
  • prepare_classical_state(input_data): Prepares the quantum state based on classical binary input data.
  • apply_gate(gate_matrix: np.ndarray, qubit_indices: List[int]): Applies a specified gate to the qubits of the quantum system.
  • apply_oracle(b1, b2, entanglements): Applies an oracle gate to the qubits using the specified entanglements.
  • measure_qubits(): Measures the qubits and returns the outcome.
  • plot_results(title="Measurement Results"): Plots the measurement outcomes of the qubits.

gates

Defines an assortment of quantum gates represented as NumPy arrays.

• Gate Variables: PauliX, PauliY, PauliZ, Hadamard, Identity, S, T, CNOT, SWAP, etc.

• Functions: PhaseShift(theta): Returns a phase shift gate for a given angle. Rx(theta), Ry(theta), Rz(theta): Rotation gates around the x, y, and z-axis.

qasm_processing

Provides functions for converting QASM code into executable Python code using the Novum library.

• Functions: qasm_to_python(qasm_code: str): Converts QASM code into Python code commands for the Novum system.

tools

Functions supporting quantum neural network operations.

• Functions: update_weights(...), compute_gradients(...), initialize_weights(...), apply_layer(...), forward_pass(...), calculate_loss(...), train_qnn(...)

equations

Basic mathematical functions used in quantum computing.

• Functions: inner_product(...), eigen(...), dot_product(...), normalize_state(...), tensor_product(...), is_unitary(...)

grover

Implements Grover's search algorithm as a class.

• Class: Grover
  • Initialization: Grover(data: list[str], target: str, iterations: int = 1)
  • Methods:
    • run(): Executes the Grover search algorithm.
    • prepare_superposition_state(): Sets up the initial superposition across all qubits.
    • apply_oracle(): Applies the oracle function.
    • apply_diffusion_operator(): Applies the diffusion (inversion about the mean) operator.

deutschjozsa

Implementation of the Deutsch-Jozsa algorithm as a class.

• Class: DeutschJozsa
  • Inherits from Novum.
  • Initialization: DeutschJozsa(num_qubits: int, f)
  • Methods:
    • oracle(): Encodes the provided function f into the quantum oracle.
    • run(): Executes the Deutsch-Jozsa algorithm to determine if the function f is constant or balanced.

Usage

Installation

pip install amin-qvm

Setting Up the Environment

Make sure you have Python installed on your system, because like, how else are you going to use the library :)

Creating a Novum Instance

To create a Novum system and run QASM:

from amin_qvm.system.novum import Novum

# Create an instance with 2 qubits
novum = Novum(2)

# Run QASM code
qasm_code = """
qreg[2];
cx 0,1;
h 0;
measure;
"""
novum.execute_qasm(qasm_code)

Applying Gates and Running Algorithms

from amin_qvm.system.gates import Hadamard
from amin_qvm.algorithms.grover import Grover

# Initialize Grover's algorithm with data and target
grover = Grover(data=["00", "01", "10", "11"], target="10")
grover.run()