Python SDK

The most intuitive way to build quantum applications. Our Python SDK combines powerful quantum computing capabilities with the simplicity of Python. From beginners to experts, everyone can build production-grade quantum software.

# Install via pip

pip install ishara-quantum

# Create your first quantum circuit

from ishara import QuantumCircuit, execute

qc = QuantumCircuit(2, 2)

qc.h(0)  # Hadamard gate

qc.cx(0, 1)  # CNOT gate

qc.measure([0,1], [0,1])

result = execute(qc, backend='ishara-qpu')

print(result.get_counts())

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Key Features

Intuitive API

Pythonic interface that feels natural. Chain operations fluently, use context managers for resource management, and leverage type hints for IDE autocomplete. Minimal boilerplate, maximum productivity.

Comprehensive Algorithm Library

Pre-built implementations of VQE, QAOA, Grover, Shor, QFT, and more. Parameterized and customizable. Focus on your problem, not on gate-level details.

Unified Backend Interface

Single API for simulators, real quantum hardware, and hybrid systems. Switch backends with one parameter. Automatic circuit optimization for target hardware.

Visualization Tools

Beautiful circuit diagrams, state vector plots, and Bloch sphere animations. Interactive Jupyter widgets for exploration. Export publication-quality figures.

Error Mitigation

Built-in error mitigation techniques: zero-noise extrapolation, measurement error mitigation, and dynamical decoupling. Improve results on NISQ devices with one line of code.

Integration with Data Science Stack

Works seamlessly with NumPy, Pandas, Matplotlib, and Scikit-learn. Quantum circuits as pandas DataFrames. ML pipelines with quantum feature maps.

Code Examples

Bell State Creation

Create entangled quantum states in just 3 lines. This fundamental example demonstrates superposition and entanglement—the core of quantum computing.

from ishara import QuantumCircuit

# Create Bell state |Φ+⟩
qc = QuantumCircuit(2)
qc.h(0) # Superposition
qc.cx(0, 1) # Entanglement

# Visualize
qc.draw(output='mpl')

Variational Quantum Eigensolver

Solve quantum chemistry problems with VQE. This example computes the ground state energy of a molecule using a variational quantum algorithm.

from ishara.algorithms import VQE
from ishara.chemistry import Molecule

# Define H2 molecule
h2 = Molecule(
  atoms='H 0 0 0; H 0 0 0.735',
  basis='sto-3g'
)

# Run VQE
vqe = VQE(
  hamiltonian=h2.get_hamiltonian(),
  ansatz='UCCSD'
)
result = vqe.run(backend='ishara-qpu')
print(f'Energy: {result.eigenvalue} Ha')

Quantum Machine Learning

Build quantum neural networks for classification tasks. This example shows how to create a variational quantum classifier compatible with scikit-learn.

from ishara.ml import QuantumKernel
from sklearn.svm import SVC

# Create quantum feature map
qk = QuantumKernel(
feature_map='ZZFeatureMap',
num_qubits=4
)

# Use in scikit-learn
clf = SVC(kernel=qk.evaluate)
clf.fit(X_train, y_train)
predictions = clf.predict(X_test)

Optimization with QAOA

Solve combinatorial optimization problems using the Quantum Approximate Optimization Algorithm. Ideal for MaxCut, TSP, and scheduling problems.

from ishara.algorithms import QAOA
from ishara.optimization import MaxCut

# Define graph
graph = [(0,1), (1,2), (2,3), (3,0)]

# Setup MaxCut problem
maxcut = MaxCut(graph)

# Run QAOA
qaoa = QAOA(
problem=maxcut,
p=3 # circuit depth
)
result = qaoa.run(backend='ishara-qpu')
print(f'Best cut: {result.best_measurement}')

API Reference

Circuit Construction

qc = QuantumCircuit(n_qubits, n_classical)

qc.h(qubit)  # Hadamard

qc.x(qubit)  # Pauli-X

qc.y(qubit)  # Pauli-Y

qc.z(qubit)  # Pauli-Z

qc.rx(theta, qubit)  # X-rotation

qc.ry(theta, qubit)  # Y-rotation

qc.rz(theta, qubit)  # Z-rotation

qc.cx(control, target)  # CNOT

qc.measure(qubit, classical)

Execution

# Execute on backend

result = execute(

  circuit,

  backend='ishara-qpu',

  shots=1024,

  optimization_level=3

)

# Get results

counts = result.get_counts()

statevector = result.get_statevector()

memory = result.get_memory()

Backends

from ishara import available_backends

# List all backends

backends = available_backends()

# Get backend info

backend = get_backend('ishara-qpu')

print(backend.status())

print(backend.properties())

print(backend.num_qubits)

Noise Simulation

from ishara.noise import NoiseModel

# Build noise model

noise = NoiseModel()

noise.add_depolarizing_error(0.001, 1)

noise.add_thermal_relaxation(

  t1=50e-6, t2=70e-6, gate_time=50e-9

)

# Run with noise

execute(qc, backend='simulator',

        noise_model=noise)

Visualization

# Circuit diagram

qc.draw(output='mpl')

# State vector

from ishara.visualization import plot_state

plot_state(statevector, 'city')

# Bloch sphere

from ishara.visualization import plot_bloch

plot_bloch(statevector)

Circuit Optimization

from ishara.transpiler import transpile

# Optimize circuit

qc_optimized = transpile(

  qc,

  backend='ishara-qpu',

  optimization_level=3

)

print(f'Gates: {qc.depth()} → '

      f'{qc_optimized.depth()}')

Installation Guide

1. Install via pip

Requires Python 3.8 or higher. Compatible with Windows, macOS, and Linux.

pip install ishara-quantum

# Optional: install visualization tools

pip install ishara-quantum[visualization]

# Optional: install all extras

pip install ishara-quantum[all]

2. Configure API Credentials

Create an account and get your API key from the dashboard.

from ishara import save_account

save_account(token='YOUR_API_KEY')

3. Verify Installation

Run a simple test to ensure everything is working.

from ishara import QuantumCircuit, execute

qc = QuantumCircuit(1, 1)

qc.h(0)

qc.measure(0, 0)

result = execute(qc, backend='simulator')

print("Success!", result.get_counts())

Ecosystem Integration

Jupyter Notebooks

Rich integration with Jupyter. Interactive widgets for circuit building and visualization. Automatic display of circuits and results.

NumPy & SciPy

State vectors and operators are NumPy arrays. Use SciPy for linear algebra operations. Seamless interoperability.

Pandas

Export measurement results as DataFrames. Analyze quantum data with pandas tools. Integration with data pipelines.

Matplotlib

Beautiful visualizations using matplotlib backend. Customize plots with standard matplotlib API. Publication-ready figures.

Scikit-learn

Quantum kernels compatible with sklearn. Build hybrid quantum-classical ML pipelines. Use familiar sklearn syntax.

TensorFlow & PyTorch

Integrate quantum layers in deep learning models. Quantum neural networks as TF/PyTorch modules. End-to-end differentiability.