NZ-SeQTech Documentation

Installation

To use NZ-SeQTech, first install it using pip:

!pip install nz-seqtech

Quantum DNA Encoding

You can encode DNA sequences using various techniques provided by NZ-SeQTech’s quantum_dna_encoding module. Here’s an example of how to use the cosine_encoding function:

import nz_seqtech
from nz_seqtech.quantum_dna_encoding import cosine_encoding

# Encode DNA sequence using cosine encoding
encoded_sequence = cosine_encoding('sequence')
print(encoded_sequence)

This example imports the necessary module and function, then uses cosine_encoding to encode a DNA sequence. The resulting encoded sequence is stored in the encoded_sequence variable.

When using the function, make sure to replace ‘sequence’ with your actual DNA sequence.

For more encoding techniques and visualization functions, you can explore other functions in the quantum_dna_encoding module:

amplitude_encoding
qft_encoding
phase_encoding
NZ23_encoding
NZ22_encoding
draw_circuit
get_statevector
visualize_bloch_multivector
visualize_state_hinton
visualize_state_city
visualize_state_paulivec

Refer to the documentation or use the help function for each specific function to learn more about its parameters and usage.

Classical DNA Sequence Analysis

You can also perform classical DNA sequence analysis using functions from the classical_operations module. For example:

import nz_seqtech
from nz_seqtech.classical_operations import analyze_dna_seq

# Analyze DNA sequence
analysis_result = analyze_dna_seq('sequence')
print(analysis_result)

Replace ‘sequence’ with your actual DNA sequence when using the function.

Classical Machine Learning Models

NZ-SeQTech provides classical machine learning models that you can use for DNA sequence analysis. Import the desired model from the classical_ml_models module and follow the usage examples provided in the documentation for each specific model.

import nz_seqtech
from nz_seqtech.classical_ml_models import knn_model

# Create and train a k-nearest neighbors model
model = knn_model(X_train, X_test, y_train, y_test, suggested_data=True)

Replace X_train , X_test , y_train and y_test with your actual data.

Quantum Machine Learning Models

If you are interested in quantum machine learning, you can explore the quantum machine learning models provided by NZ-SeQTech in the quantum_ml_models module. Follow the usage examples in the documentation for each specific model.

import nz_seqtech
from nz_seqtech.classical_ml_models import QNN

# Create and train a Quantum Neural Network (QNN) model
qnn_model = QNN(X, y, suggested_data=True)

Replace X and y with your actual data.

Note: The suggested_data parameter is set to True to indicate using the provided datasets in the package for testing. If you prefer to use your own data, set suggested_data to False and provide your training and testing data.