A comprehensive quantum computing framework with modular implementations, built for researchers, educators, and quantum application developers.
The following diagram illustrates how QpiAI's products work together. The QpiAI Quantum SDK can execute circuits locally via built-in simulators, or submit jobs remotely to QpiAI QCloud for access to high-performance cloud simulators and QPUs.
Show Mermaid Source Code
flowchart LR
%% 1. Client Layer
subgraph Client ["Client"]
Notebooks["Jupyter Notebooks/
Python Program"]
Agents["AI Agents"]
end
%% 2. SDK Layer
SDK["QpiAI SDK"]
%% 3. Execution Branches
subgraph Local ["Local Execution"]
LocalSim["Local Simulator"]
end
%% Remote Execution Layer
subgraph Remote ["QpiAI QCloud"]
CloudSims["QCloud Simulators"]
QPU{{"QPU"}}
end
%% The Pipeline Flow
Notebooks --> SDK
Agents --> SDK
SDK -->|Local Run| LocalSim
SDK -->|Remote Job| Remote
- 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
- Grover's Search: Amplitude amplification for unstructured search
- Shor's Algorithm: Integer factorization (educational implementation for small N)
- 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
- Iterative Amplitude Estimation: Maximum-likelihood amplitude estimation for Monte Carlo-style workflows (canonical QPE-based variant planned)
- 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
- 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
- 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
- QpiAI Cloud Authentication: Secure access to QpiAI cloud platform and QPU resources via
QpiAIQuantumAuth
The QpiAI Quantum SDK is available as an open-source package. Install directly from PyPI:
pip install qpiai-quantumpython -c 'import qpiai_quantum; print(f"QpiAI Quantum SDK v{qpiai_quantum.__version__} installed successfully")'- Python 3.10 or higher
- Dependencies are automatically installed with pip
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!")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"
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}")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)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()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|- 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/
Experiment with quantum logic gates and circuits, develop new quantum computing approaches, and prototype novel quantum applications.
Learn quantum computing concepts with practical, hands-on implementations. Perfect for students, educators, and quantum computing enthusiasts.
Build quantum applications and integrate quantum computing capabilities into your projects with a clean, intuitive API.
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
This project is licensed under the Apache License 2.0 - see the LICENSE file for details.
- PyPI Package: https://pypi.org/project/qpiai-quantum/
- Documentation: https://docs.qcloud.qpiai.tech/
- QpiAI QCloud Platform: https://qcloud.qpiai.tech/
- Support Email: support@qcloud.qpiai.tech
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/}}
}Copyright © 2026 QpiAI. All rights reserved.