quanta-sdk 0.6.1

Multi-paradigm quantum computing SDK — Grover, QAOA, VQE, Shor, QSVM, surface code, entity resolution

Quanta SDK

Multi-paradigm quantum computing SDK for Python

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Quanta is a clean, modular quantum computing SDK designed for researchers, engineers and developers. It provides a 3-layer abstraction — from high-level declarative APIs (search(), factor()) to low-level DAG manipulation and QASM export — so you can work at the level that fits your problem.

Key highlights:

• Shor, VQE, QAOA, QSVM, Grover — production-grade quantum algorithms
• DAG-based IR with 3-pass compiler and topology-aware qubit routing
• Statevector simulator up to 27 qubits with optional JAX/CuPy GPU acceleration
• Surface code QEC, BB84 QKD, and error correction primitives
• Real-world demo: quantum entity resolution for customer deduplication

Table of Contents

• Quick Start
• Architecture
• Features
• Algorithms
• Examples & Use Cases
• Quality Benchmark
• Installation
• Documentation
• Author
• License

Quick Start

from quanta import circuit, H, CX, measure, run

@circuit(qubits=2)
def bell(q):
    H(q[0])
    CX(q[0], q[1])
    return measure(q)

result = run(bell, shots=1024)
print(result)

╔══════════════════════════════════════════════════╗
║  Quanta Result: bell                            ║
╠──────────────────────────────────────────────────╣
║  |00>  ████████████████████  50.5%              ║
║  |11>  ███████████████████   49.5%              ║
╠──────────────────────────────────────────────────╣
║  0.71|00> + 0.71|11>                            ║
╚══════════════════════════════════════════════════╝

Architecture

┌──────────────────────────────────────────────────────────┐
│  Layer 3 — Declarative API                               │
│  search() · optimize() · vqe() · factor() · qsvm()      │
│  portfolio_optimize() · resolve() · multi_agent()        │
├──────────────────────────────────────────────────────────┤
│  Layer 2 — Circuit API                                   │
│  @circuit · H · CX · RZ · measure · run · sweep          │
│  custom_gate() · 17 built-in gates                       │
├──────────────────────────────────────────────────────────┤
│  Layer 1 — Physical Layer                                │
│  DAG IR · 3-pass compiler · qubit routing · QASM I/O     │
│  statevector · density matrix · JAX/CuPy acceleration    │
└──────────────────────────────────────────────────────────┘

Features

Core

• 17 built-in gates — H, X, Y, Z, CX, CCX, SWAP, RX, RY, RZ, S, T, and more
• custom_gate(name, matrix) — define your own unitary gates
• @circuit decorator — write quantum circuits as Python functions
• sweep(circuit, params) — parameter scans for variational algorithms

Compiler & IR

• DAG-based intermediate representation — directed acyclic graph for circuit analysis
• 3-pass optimizer — gate cancellation, gate merging, basis translation
• Topology-aware routing — SWAP insertion for linear, ring, and grid topologies
• QASM 2.0/3.0 — import external circuits and export for cross-SDK interop

Simulators

• Statevector — tensor contraction engine, up to 27 qubits (100s, 2GB)
• Density matrix — mixed states + Kraus noise channels, up to 13 qubits
• Accelerated backend — auto-detects JAX-GPU / CuPy; falls back to NumPy on CPU

Error Correction

• Bit-flip, Phase-flip, Steane [[7,1,3]] codes
• Surface code [[d²,1,d]] — logical qubits with configurable distance, threshold ~1%

Security

• BB84 QKD — quantum key distribution with eavesdropper detection (Example →)

Algorithms

Algorithm	Module	Use Case	Example
Grover	layer3.search	Unstructured search (√N speedup)	05 →
QAOA	layer3.optimize	Combinatorial optimization	07 →
VQE	layer3.vqe	Molecular ground-state energy	06 →
Shor	layer3.shor	Integer factoring (RSA)	10 →
QSVM	layer3.qsvm	Quantum kernel classification	10 →
Multi-Agent	layer3.agent	Quantum agent-based modeling	09 →
Portfolio	layer3.finance	Financial portfolio optimization	07 →
Hamiltonian	layer3.hamiltonian	Molecular simulation (H₂, LiH, HeH⁺)	06 →
Entity Resolution	layer3.entity_resolution	Customer deduplication (QAOA)	11 →

Examples & Use Cases

Run any example with python -m quanta.examples.<name>:

01 Bell State

EPR pair — the simplest entanglement demonstration.

python -m quanta.examples.01_bell_state

02 GHZ State

Multi-qubit entanglement: all-or-nothing correlations.

python -m quanta.examples.02_ghz_state

03 Quantum Teleportation

Transfer an unknown quantum state using entanglement + classical bits.

python -m quanta.examples.03_teleportation

04 Deutsch-Jozsa

Determine if a function is constant or balanced in one query.

python -m quanta.examples.04_deutsch_jozsa

05 Grover Search

Quadratic speedup for unstructured search — finds target with 99.9% probability.

python -m quanta.examples.05_grover

06 Molecular Energy

H₂ and HeH⁺ ground state via VQE + Hamiltonian time evolution.

python -m quanta.examples.06_molecule_energy

07 Portfolio Optimization

Quantum-optimized stock portfolios — tech vs crypto, conservative vs aggressive.

python -m quanta.examples.07_portfolio_optimization

08 QKD BB84

Quantum key distribution — detect eavesdroppers via ~25% error rate.

python -m quanta.examples.08_qkd_bb84

09 Full Demo

All SDK features in one script — circuits, custom gates, VQE, Grover, noise, routing, QASM.

python -m quanta.examples.09_full_demo

10 Quantum Benchmark

8-test quality litmus test — Bell fidelity, CHSH, teleportation, Grover, VQE, Shor, QSVM, surface code.

python -m quanta.examples.10_quantum_benchmark

11 Entity Resolution

Real-world use case: OTA customer deduplication with QAOA vs classical greedy.
25 records, 8 columns, Turkish name handling, 3-layer blocking pipeline.
Result: QAOA 86% accuracy vs Greedy 64%.

python -m quanta.examples.11_entity_resolution

Quality Benchmark

Turnusol Test — 8/8 🏆

#	Test	Result	Metric
1	Bell State Fidelity	✅	F = 1.0000
2	CHSH Inequality	✅	S = 2.8284 (Tsirelson bound)
3	Quantum Teleportation	✅	Unitarity preserved
4	Grover Amplification	✅	99.9% target probability
5	VQE Convergence (H₂)	✅	0.000054 Ha error
6	Shor Factoring	✅	15 = 3 × 5
7	QSVM Classification	✅	100% accuracy
8	Surface Code QEC	✅	0% logical error rate

QASMBench — 10/10

All standard QASMBench circuits import, compile, and simulate correctly:
bell · ghz · qft · teleportation · deutsch-jozsa · grover · adder · vqe_ansatz · swap_test · random

Large circuit support: GHZ-20 (710ms), QFT-20 (3.5s), Random-24 (12s).

Benchpress Compatible

Includes QuantaBenchpressBackend adapter for cross-SDK benchmarking alongside Qiskit, Cirq, and Braket using the Benchpress framework.

Installation

# Clone and install
git clone https://github.com/ONMARTECH/quanta-sdk.git
cd quanta-sdk
pip install -e ".[dev]"

# Run tests
pytest

# Run benchmark
python -m quanta.examples.10_quantum_benchmark

Optional GPU acceleration:

pip install jax jaxlib   # JAX GPU backend
pip install cupy          # NVIDIA CUDA backend

Documentation

Document	Description
Architecture (EN)	System design, DAG IR, compiler pipeline
Architecture (TR)	Türkçe mimari dokümanı
Features (EN)	Complete feature list
Comparison (EN)	vs Qiskit, Cirq, Braket
CHANGELOG	Version history

Project Stats

Files:       86          Languages:   Python
Lines:       11,770      Tests:       150+
Algorithms:  9           Examples:    11
Simulators:  3           QEC Codes:   4
QASM:        2.0 + 3.0   Max Qubits:  27

Author

Abdullah Enes SARI — ONMARTECH

[e-mail]

Contributing

Contributions, issues, and feature requests are welcome!
Feel free to check issues page.

License

Apache License 2.0

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