qpiai-quantum 0.1.35

A comprehensive Python-based quantum computing framework providing modular implementations of quantum algorithms and optimization solvers

QpiAI Quantum SDK

A comprehensive quantum computing framework with modular implementations, built for researchers, educators, and quantum application developers.

Features

Core Quantum Computing

• Circuit Building: Intuitive quantum circuit construction with support for quantum and classical registers
• Gate Operations: Comprehensive set of quantum gates including:
  • Single-qubit: H, X, Y, Z, S, S†, T, T†, SX, ID
  • Rotation: RX, RY, RZ, P (phase)
  • Two-qubit: CX (CNOT), CY, CZ, SWAP, iSWAP, CP, RZZ
  • Multi-qubit: CCX (Toffoli), CSWAP (Fredkin)
• Measurement: Flexible measurement operations with classical register support
• Simulation Backends: Statevector simulator, Density matrix simulator, and Tensor network simulator
• QPU Access: Run circuits on QpiAI Indus quantum processing unit
• Circuit Utilities: Circuit depth, size, gate statistics (list_gates), composition (compose), inverse, and barrier operations

Quantum Algorithms

• Grover's Search: Amplitude amplification for unstructured search
• Shor's Algorithm: Integer factorization
• Quantum Fourier Transform (QFT): Core subroutine for many quantum algorithms
• Quantum Phase Estimation (QPE): Eigenvalue estimation
• Simon's Algorithm: Finding hidden bit strings
• Bernstein-Vazirani: Determining hidden linear functions
• Deutsch-Jozsa: Distinguishing constant from balanced functions
• Quantum Random Number Generator (QRNG): True quantum randomness
• Amplitude Estimation: Quantum speedup for Monte Carlo estimation (standard and iterative variants)

Quantum Information & States

• Statevector: Full quantum state representation and manipulation
• Density Matrix: Mixed state representation with comprehensive operations
• Entangled State Generation: Bell states, GHZ states, W states, and Cluster states

Visualization Tools

• Circuit Diagrams: Matplotlib-based circuit rendering with light/dark themes and math-text support
• Plotly Visualizer: Interactive circuit and result visualization
• Bloch Sphere: Interactive 3D Bloch sphere visualization (Plotly-based)
• Histogram Plots: Measurement outcome visualization
• State Vector Plots: Amplitude and phase visualization

Backend & Job Management

• Direct Execution: Run circuits with configurable shots, device, and simulation method
• JobManager: Unified interface for job submission, status tracking, cancellation, and history
• Job Result Handling: Structured results with counts, statevectors, and density matrices

Authentication & Cloud

• QpiAI Cloud Authentication: Secure access to QpiAI cloud platform and QPU resources via QpiAIQuantumAuth

Installation

PyPI Installation (Recommended)

The QpiAI Quantum SDK is available as an open-source package. Install directly from PyPI:

pip install qpiai-quantum

Verify Installation

python -c 'import qpiai_quantum; print(f"QpiAI Quantum SDK v{qpiai_quantum.__version__} installed successfully")'

Requirements

• Python 3.8 or higher
• Dependencies are automatically installed with pip

Quick Start

Basic Circuit Example

from qpiai_quantum import Circuit

# Create a quantum circuit with 2 qubits and 2 classical bits
circuit = Circuit(2, 2)

# Apply quantum gates
circuit.h(0)        # Hadamard gate on qubit 0
circuit.cx(0, 1)    # CNOT gate (control: qubit 0, target: qubit 1)

# Measure qubits
circuit.measure([0, 1], [0, 1])

# Visualize the circuit
circuit.show()

print("Bell state circuit created successfully!")

Authentication & API Key Setup

Before running circuits on any backend (including the local simulator), you need to authenticate with your API key:

from qpiai_quantum import QpiAIQuantumAuth

# Login with your API key (obtain from https://qcloud.qpiai.tech/)
QpiAIQuantumAuth.login(api_key="your_api_key_here")

# Verify your API key
QpiAIQuantumAuth.verify_api_key()

# View available remote compute resources (Note: this does not list the local simulator)
QpiAIQuantumAuth.list_compute_resources()

Alternatively, set your API key in a qcloud.env file in your project root:

API_KEY="your_api_key_here"

Running a Circuit on QpiAI Simulators

from qpiai_quantum import Circuit

# Create a circuit
circuit = Circuit(2, 2)
circuit.h(0)
circuit.cx(0, 1)
circuit.measure([0, 1], [0, 1])

# Execute on the statevector simulator
job_result = circuit.run(shots=10000, experiment_name="Bell State", device_name="QpiAI-QSV-Local")

# Get results
counts = job_result.get_counts()
print(f"Measurement results: {counts}")

Quantum Algorithms

from qpiai_quantum import GroverSearch, QFT, ShorsAlgorithm

# Grover's search
grover = GroverSearch(num_qubits=3, oracle_type="custom")

# Quantum Fourier Transform
qft = QFT(num_qubits=4)

# Shor's algorithm for factorization
shor = ShorsAlgorithm(N=15)

State Preparation

from qpiai_quantum import BellStateGenerator, GHZStateGenerator, WStateGenerator, ClusterStateGenerator

# Generate Bell state (maximally entangled 2-qubit state)
bell_gen = BellStateGenerator(num_qubits=2)
bell_circuit = bell_gen.generate_state()

# Generate GHZ state (n-qubit entangled state)
ghz_gen = GHZStateGenerator(num_qubits=3)
ghz_circuit = ghz_gen.generate_state()

# Generate W state (n-qubit entangled state)
w_gen = WStateGenerator(num_qubits=3)
w_circuit = w_gen.generate_state()

# Generate Cluster state (graph-state entanglement)
cluster_gen = ClusterStateGenerator(num_qubits=4)
cluster_circuit = cluster_gen.generate_state()

Quantum Information

from qpiai_quantum import Statevector, DensityMatrix

# Create and inspect a statevector
sv = Statevector([1, 0])  # |0⟩ state

# Create a density matrix
dm = DensityMatrix([[1, 0], [0, 0]])  # |0⟩⟨0|

Documentation & Tutorials

• Tutorial Notebooks: Explore comprehensive examples in the sdk_notebooks/ directory.
• Getting Started Guide: Learn the basics of quantum circuit construction
• Documentation: Complete SDK documentation at https://docs.qcloud.qpiai.tech/

Use Cases

Research

Experiment with quantum logic gates and circuits, develop new quantum computing approaches, and prototype novel quantum applications.

Education

Learn quantum computing concepts with practical, hands-on implementations. Perfect for students, educators, and quantum computing enthusiasts.

Development

Build quantum applications and integrate quantum computing capabilities into your projects with a clean, intuitive API.

Contributing

We welcome contributions from the community! Whether it's bug reports, feature requests, or questions, we'd love to hear from you.

Please see CONTRIBUTING.md for guidelines on:

• Reporting bugs and issues
• Requesting new features
• Contributing code
• Asking questions and getting support
• Code of conduct

License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

Links & Resources

• PyPI Package: https://pypi.org/project/qpiai-quantum/
• Documentation: https://docs.qcloud.qpiai.tech/
• QpiAI QCloud Platform: https://qcloud.qpiai.tech/
• Support Email: [e-mail]

Citation

If you use the QpiAI Quantum SDK in your research, please cite it as follows:

@software{qpiai_quantum_sdk,
  author = {{QpiAI}},
  title = {QpiAI Quantum SDK},
  year = {2026},
  url = {https://github.com/qpiai/quantum-sdk},
  note = {Company Website: \url{https://www.qpiai.tech/}}
}

Acknowledgments

Built with modern Python quantum computing libraries and optimized for performance and ease of use.

---

Copyright © 2026 QpiAI. All rights reserved.