QONTOS Simulators

Simulation, digital twin, and tensor-network modeling for the QONTOS platform.

Public validation and planning tools for the software stack today and the modular hardware roadmap ahead.

Overview · Installation · Quick Start · Docs Hub · Simulators · Digital Twin · Tensor Engine · Related Packages

QONTOS Simulators provides the simulation and digital-twin layer for the QONTOS platform. It includes local simulators, noisy simulation, modular architecture models, and tensor-network tools used for validation, planning, and large-scale system studies. This repository supports both present-day software workflows and future native QONTOS hardware design work.

Start with docs/index.md for the lightweight docs hub and links into the broader public QONTOS ecosystem. For the canonical install and release policy across the public repos, use the shared policy.

It provides three simulation backends:

1. qontos_sim — Qiskit Aer-based simulators (noiseless and noisy)
2. qontos_twin — Modular hardware digital twin for architecture studies
3. qontos_tensor — Pure NumPy tensor network engine (MPS, MPO, DMRG)

The QONTOS packages are not yet published to PyPI. Install from pinned release tags:

pip install "qontos-sim[all] @ git+https://github.com/qontos/[email protected]"

This automatically installs the pinned qontos SDK dependency (v0.2.0).

pip install "qontos-sim[sim] @ git+https://github.com/qontos/[email protected]"
pip install "qontos-sim[twin] @ git+https://github.com/qontos/[email protected]"
pip install "qontos-sim[tensor] @ git+https://github.com/qontos/[email protected]"
pip install "qontos-sim[all] @ git+https://github.com/qontos/[email protected]"
pip install "qontos-sim[dev] @ git+https://github.com/qontos/[email protected]"

Note: Once published to PyPI, these simplify to pip install qontos-sim[sim], etc.

Requires Python 3.10+.

The simulator package is designed to work alongside the flagship qontos SDK because it consumes the public CircuitIR and PartitionResult schemas from that repo.

from qontos.circuit import CircuitNormalizer
from qontos_sim import LocalSimulatorExecutor

normalizer = CircuitNormalizer()
circuit_ir = normalizer.normalize(input_type="openqasm", source=qasm_source)
executor = LocalSimulatorExecutor()
result = executor.submit(circuit_ir, shots=8192)
print(result.counts)

from qontos.circuit import CircuitNormalizer
from qontos_sim import NoisySimulatorExecutor

normalizer = CircuitNormalizer()
circuit_ir = normalizer.normalize(input_type="openqasm", source=qasm_source)
executor = NoisySimulatorExecutor()
result = executor.submit(circuit_ir, shots=8192)

from qontos_twin import ModularSimulator, SystemConfig

config = SystemConfig(
    num_modules=4,
    transduction_efficiency=0.15,
)
sim = ModularSimulator(config)
workload = sim.simulate_workload(circuit_depth=250)
print(f"Estimated fidelity: {workload.estimated_fidelity:.4f}")
print(f"Bell pairs required: {workload.bell_pairs_needed}")

from qontos_tensor import GateInstruction, TNSimulator

# Simulate bounded-entanglement circuits with an MPS backend
sim = TNSimulator(n_qubits=2, chi_max=256)
result = sim.run(
    [
        GateInstruction(name="H", qubits=[0]),
        GateInstruction(name="CNOT", qubits=[0, 1]),
    ],
    n_shots=1024,
)
print(result.measurements[:5])

The digital twin simulates workloads on modular architecture candidates. For a given system configuration, it estimates:

• Total gate count (intra-module and inter-module)
• Circuit fidelity (based on gate fidelity, transduction, and decoherence)
• Runtime in microseconds
• Bell pairs required for inter-module operations
• Effective circuit depth increase from serialization

Pure NumPy implementation — zero external tensor network dependencies.

• MPS (Matrix Product State) — Bond dimension up to 4096
• MPO (Matrix Product Operator) — Heisenberg, Ising, Hubbard, molecular Hamiltonians
• DMRG — Variational ground-state search for 100+ site systems
• Circuit simulation — Full circuit evolution via MPS

Apache License 2.0

Built by Zhyra Quantum Research Institute (ZQRI) — Abu Dhabi, UAE