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QuoNic

Quantum programming, as simple as writing Python.

No QuantumCircuit to learn, no backend to understand, no manual measure.
If you can write Python, you can write quantum programs.

Version Python License Tests
Qiskit Cirq Qulacs CUDA-Q Algorithms Hardware

The Problem

Quantum programming today is unnecessarily complex. Writing a simple Bell state in Qiskit requires 10+ lines, understanding circuit objects, backend selection, and manual measurement. Switching frameworks means rewriting everything.

Too Many Concepts

QuantumCircuit, backend, transpile, measure_all — 8+ new concepts before writing a single gate.

Framework Lock-in

Code written for Qiskit can't run on Cirq. Switching frameworks means rewriting everything.

No Smart Defaults

Choosing the wrong simulation method can be 1000x slower. Users shouldn't need to know internals.


The Solution

QuoNic abstracts away the complexity. Three lines of code, any backend, any hardware.

QuoNic Terminal Preview

from quonic import qgate, qshow
from quonic.gates import H, CX

qgate(H, 0)
qgate(CX, 0, 1)
qshow()
pip install quonic

Features

Feature Description
3-line syntax qgate + qshow — that's it
12+ backends One argument: qshow(backend='qiskit')
77 algorithms Grover, Shor, VQE, QAOA, QFT, error correction, quantum ML
Smart scheduler Auto-selects fastest method (statevector / stabilizer / MPS / density matrix)
GPU acceleration qshow(backend='gpu') — 10x speedup
Real hardware Origin Quantum, AWS Braket, Quantum Inspire verified
Noise simulation Depolarizing, bit-flip, phase-flip, decoherence
Error mitigation ZNE, readout calibration
23 visualizations Circuit diagrams, Bloch sphere, histograms

Tech Stack

Component Technology Description
Core Python 3.9+ IR, scheduler, compiler, noise models
Backends Qiskit · Cirq · Qulacs · TensorCircuit · CUDA-Q · MindQuantum · QPanda3 12+ quantum backends
GPU CuPy · Qulacs GPU · CUDA-Q GPU-accelerated simulation
Hardware Origin Quantum · AWS Braket · Quantum Inspire Real quantum hardware
Visualization Matplotlib 23 chart types, lazy-loaded

Real Hardware

Platform Device Status
Origin Quantum WK_C180 ✅ Verified
AWS Braket Rigetti Cepheus-1-108Q ✅ Verified
Quantum Inspire Tuna-9 / Tuna-17 ✅ Verified
qshow(backend='qpanda', device='WK_C180')
qshow(backend='qi', device='tuna9')

Algorithms

from quonic.algorithms import grover, vqe, qft, qaoa_maxcut

grover("11", 2)                    # Search
vqe(hamiltonian, 2)                # Chemistry
qft(n_qubits=4)                    # Fourier Transform
qaoa_maxcut(edges, n_qubits=3)     # Optimization
Domain Algorithms
Foundational QFT, Deutsch-Jozsa, Bernstein-Vazirani, Simon, QPE
Search & Optimization Grover, QAOA (MaxCut/TSP/MIS/Knapsack), quantum annealing
Chemistry VQE, Hamiltonian simulation, Trotter, Jordan-Wigner
Machine Learning QNN, QSVM, QGAN, QCNN, QGNN, QPCA, QRL
Error Correction Bit/phase flip, Shor code, Steane code, surface code, color code
Communication Teleportation, BB84, E91, superdense coding

Roadmap

  • Core API: qgate, qshow, reset — minimal syntax
  • 12+ backends: Qiskit, Cirq, Qulacs, TensorCircuit, CUDA-Q, MindQuantum, QPanda3
  • 77 algorithm templates: From Grover to quantum ML
  • Smart scheduler: Auto-select fastest simulation method
  • GPU acceleration: CuPy, Qulacs GPU, CUDA-Q
  • Real hardware: Origin Quantum, AWS Braket, Quantum Inspire
  • Noise simulation: Depolarizing, bit-flip, phase-flip, decoherence
  • Error mitigation: ZNE, readout calibration
  • Visualization: 23 chart types with Matplotlib
  • Documentation: 92 examples, bilingual (EN/ZH)
  • More backends: IonQ, Rigetti, Xanadu, QuEra
  • Quantum networking: Multi-node quantum communication
  • Fault-tolerant computing: Logical qubit operations

Docs


Contributing

Fork → branch → PR.

See CONTRIBUTING.md for setup and code style.


License

Apache 2.0 — friendly to commercial use, with patent protection.


Built with ❤️ by Lee LapYuen · 中文文档

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