ZUNTENIUM

Enterprise-Grade Hybrid Quantum Computing SDK, Transpiler & Cloud Platform

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Overview

ZUNTENIUM is a high-performance, full-stack quantum computing platform designed to scale seamlessly from interactive circuit design on laptops to distributed GPU simulation and physical Quantum Processing Unit (QPU) orchestration.

ZUNTENIUM converges advanced circuit compilation, simulation engines, multi-hardware orchestration, and self-hosted cloud microservices into a unified, production-ready ecosystem:

• 1. Multi-Engine Quantum Simulation:
  • Aaronson-Gottesman Stabilizer Tableau: Simulates Clifford circuits up to 10,000+ qubits in $O(n^2)$ time with phase tracking.
  • CuPy GPU Statevector Backend: High-throughput GPU tensor contraction for dense unitary execution.
  • Matrix Product State (MPS) Tensor Network: Simulates 50–100+ weakly entangled qubits with SVD bond-dimension truncation.
  • Exact Statevector Simulator: Big-endian pure-state evolution with full Pauli expectation value contraction $\langle \psi | P | \psi \rangle$.
• 2. Transpiler Engine v2:
  • TranspilerCache: Structural template hashing with $O(1)$ parameter rebinding, cutting 95%+ transpilation latency in VQE and QAOA variational loops.
  • SABRE Qubit Routing: Lookahead distance heuristics, extended successor sets, and decay penalties for heavy-hex and grid topologies.
  • Cartan KAK Lie Algebra Decomposition: Canonical $SU(4)$ two-qubit decomposition minimizing two-qubit gate counts.
  • Solovay-Kitaev $\epsilon$-Net Approximation: Recursive group commutator approximation for arbitrary single-qubit rotations.
  • Circuit Fingerprinting: Parameter-invariant topological and DAG similarity hashing.
• 3. Multi-Hardware Orchestration System (MHOS):
  • Unified adapter interface for physical execution on IBM Quantum (Runtime), IonQ Cloud, Amazon Braket, and Rigetti (QCS) with automatic simulator fallback.
• 4. Zero-Noise Extrapolation (ZNE) & Error Mitigation:
  • Richardson, polynomial, and exponential noise scaling with randomized Pauli / readout mitigation.
• 5. Self-Hostable Cloud Platform & Microservices:
  • FastAPI REST API: Authentication, JWT tokens, API keys, quota management, and public backend discovery.
  • RQ Distributed Worker Cluster: High, default, low, and hardware queues with graceful job execution.
  • Full Observability: PostgreSQL 15 (UUID typed), Redis 7, Prometheus metrics, and Grafana dashboard provisioning.
• 6. Next.js 15 Visual QCanvas Workspace:
  • Standalone web interface with interactive quantum circuit builder, Bloch sphere visualizer, and live cloud job telemetry.

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Architecture

User Code / QCanvas UI  ───►  FastAPI Gateway (Port 8000)
                                     │
                  ┌──────────────────┴──────────────────┐
                  ▼                                     ▼
        Transpiler Engine v2                 Distributed Job Queue
   ├── TranspilerCache (O(1) Rebind)         ├── Redis Task Broker
   ├── SABRE Lookahead Router                ├── RQ Worker Cluster
   ├── Cartan KAK SU(4) Decomposition        └── PostgreSQL 15 (UUID)
   └── Solovay-Kitaev ε-Net
                  │
                  ▼
         Multi-Hardware Orchestrator (MHOS)
   ┌──────────────────────────────────────────────────────────┐
   │                     Execution Backends                   │
   ├────────────────────────────┬─────────────────────────────┤
   │ Local Simulation:          │ Cloud QPUs:                 │
   │ • Stabilizer (10,000+ q)   │ • IBM Quantum (Runtime)     │
   │ • CuPy GPU Statevector     │ • IonQ Cloud (Trapped Ion)  │
   │ • Tensor Network (MPS)     │ • Amazon Braket             │
   │ • C++ / NumPy Statevector  │ • Rigetti QCS (Supercond.)  │
   └────────────────────────────┴─────────────────────────────┘

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Competitive Capability Matrix

How ZUNTENIUM compares with established quantum frameworks:

Capability Domain	Qiskit	PennyLane	TKET	CUDA-Q	ZUNTENIUM
Primary Focus	IBM ecosystem & general SDK	QML & hybrid gradients	High-performance routing	GPU-accelerated C++	Full-Stack Quantum SDK + Cloud + UI
Simulation Diversity	High (Aer)	Via plugins	Via backends	CuQuantum	High (Statevector, Stabilizer $10^4$q, GPU, MPS)
Clifford Stabilizer	Included	External	External	Limited	Top-Tier ($10^4$+ qubits built-in)
Transpiler Caching	Basic pass manager	Tape caching	Structural passes	Compile-time	Top-Tier ($O(1)$ Template Rebinding)
Multi-Hardware Portability	IBM-centric	Provider plugins	Provider plugins	NVIDIA targets	Native MHOS (IBM, IonQ, Braket, Rigetti)
Self-Hosted Cloud Stack	Proprietary	None	None	None	Built-in (FastAPI, RQ, Postgres, Redis, Prometheus)
Visual Canvas Web UI	Web Composer	None	None	None	Built-in (Next.js 15 Standalone QCanvas)
Test Verification	Extensive	Extensive	Extensive	Extensive	477 / 477 passing (100%)

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Quick Start & Code Examples

1. Massive Clifford Simulation (10,000 Qubits)

Simulate 10,000-qubit entangled states in seconds using the Aaronson-Gottesman binary stabilizer tableau:

import zuntenium as zn

# Create a 10,000-qubit GHZ state circuit
qc = zn.QuantumCircuit(10000)
qc.h(0)
for i in range(9999):
    qc.cx(i, i + 1)
qc.measure_all()

# Execute on the stabilizer simulator
result = zn.execute(qc, backend="stabilizer", shots=1000)
print(result.counts)
# Output: {'00...0': 503, '11...1': 497}

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2. $O(1)$ Variational Acceleration with TranspilerCache

Eliminate 95%+ transpilation overhead during VQE, QAOA, and QML parameter update loops:

from zuntenium import QuantumCircuit
from zuntenium.transpiler import TranspilerCache

# Initialize structural template cache
cache = TranspilerCache(max_entries=1000)

# Build a parameterized variational ansatz
ansatz = QuantumCircuit(4)
ansatz.rx(0, theta=0.1).ry(1, theta=0.2).cx(0, 1).cx(2, 3)

# Hash topological structure invariant to parameter values
template_hash = cache.compute_template_hash(ansatz)

# In optimization loop: rebind in O(1) time without recompilation
for epoch in range(100):
    new_angles = [0.05 * epoch, 0.12 * epoch]
    fast_circuit = cache.rebind(template_hash, new_angles)

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3. Hardware-Aware SABRE Routing

Map arbitrary quantum circuits onto physical chip coupling graphs with lookahead distance heuristics:

from zuntenium.transpiler.passes.mapping.sabre_router import SABRERouter
from zuntenium.transpiler.coupling_map import CouplingMap
from zuntenium import QuantumCircuit

# Define a 3x3 grid hardware topology
coupling = CouplingMap.from_grid(3, 3)
router = SABRERouter(coupling_map=coupling, lookahead_weight=0.5, decay_rate=0.001)

# Route unmapped circuit onto physical architecture
routed_circuit, stats = router.run(unmapped_circuit)
print(f"Inserted SWAPs: {stats['swaps_added']}, Final Depth: {routed_circuit.depth()}")

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4. Self-Hosted Cloud Client Execution

Submit circuits to your private Zuntenium Cloud cluster:

from zuntenium.api.cloud import CloudClient
from zuntenium import QuantumCircuit

# Initialize client with API key
client = CloudClient(base_url="http://localhost:8000", api_key="zn_live_...")

# Discover available hardware and simulators
backends = client.list_backends()
print("Available:", [b["name"] for b in backends])

# Submit asynchronous job with error mitigation
job = client.submit(
    circuit=qc,
    backend="auto",
    shots=2048,
    mitigation=True,
    optimization_mode="rule",
)

result = job.wait_for_result(timeout=60)
print(result.counts)

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Installation

From PyPI

pip install zuntenium

With Optional Accelerators

# GPU acceleration (CuPy / CUDA)
pip install "zuntenium[gpu]"

# Cloud microservices stack (FastAPI, Redis, RQ, SQLAlchemy)
pip install "zuntenium[cloud]"

# Full installation with all extras
pip install "zuntenium[all]"

From Source

git clone https://github.com/Vnnie-Mun/zuntenium.git
cd zuntenium
pip install -e ".[dev,cloud,gpu]"

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Deploying Zuntenium Cloud

Launch the full cloud infrastructure (API, multi-priority RQ workers, PostgreSQL, Redis, Prometheus, and Grafana):

cd zuntenium_cloud
docker compose up -d

• API Gateway: http://localhost:8000 (Docs at /docs)
• Prometheus Telemetry: http://localhost:9090
• Grafana Dashboard: http://localhost:3000
• RQ Worker Dashboard: http://localhost:9181

Running the QCanvas Web Workspace

cd zuntenium-ui
npm install
npm run build
npm run start

Interactive visual workspace available at http://localhost:3000.

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Test Suite & Verification

ZUNTENIUM is rigorously verified with 100% test pass rates:

# Run complete test suite (477 tests)
pytest tests/

# Run unit tests only (279 tests)
pytest tests/unit/

# Run end-to-end integration verification (25 subsystems)
python debug_all.py

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License

ZUNTENIUM is licensed under the Apache License 2.0. See LICENSE for details.