hybridlane¶

hybridlane is a Python library for designing and manipulating hybrid continuous-variable (CV) and discrete-variable (DV) quantum circuits within the PennyLane ecosystem. It provides a frontend for expressing hybrid quantum algorithms, implementing the concepts from the paper Y. Liu et al, 2026 (PRX Quantum 7, 010201).

✨ Why hybridlane?¶

As quantum computing explores beyond traditional qubit-only models, hybridlane offers a powerful and intuitive framework for researchers and developers to:

Design complex hybrid circuits effortlessly: Seamlessly integrate qubits and qumodes in the same circuit.
Describe large-scale circuits: Define hybrid gate semantics independently of simulation, enabling fast description of wide and deep circuits with minimal memory.
Leverage the PennyLane ecosystem: Integrate with PennyLane’s extensive tools for transformations, resource estimation, and device support.

🚀 Features¶

📃 Hybrid Gate Semantics: Precise, platform-independent definitions for hybrid gates, enabling rapid construction of large-scale quantum circuits.
⚛️ Native Qumode Support: Qumodes are treated as a fundamental wire type, with automatic type inference that simplifies circuit construction and enhances readability.
🤝 PennyLane Compatibility: A familiar interface for PennyLane users. Utilize existing PennyLane gates, build custom hybrid devices, write compilation passes, and perform resource estimation across mixed-variable systems.
💻 Classical Simulation: A built-in device that dispatches to Bosonic Qiskit for simulating small hybrid circuits.
💾 OpenQASM-based IR: An intermediate representation based on an extended OpenQASM, promoting interoperability and enabling advanced circuit manipulations.

⚙️ Installation¶

hybridlane is currently in early preview. We welcome your feedback on our GitHub Issues page to help us improve.

Install the package from PyPI:

pip install hybridlane

Available Extras:

[all]: Installs all extra dependencies.
[bq]: Installs support for the bosonicqiskit.hybrid simulation device.
[qscout]: Installs support for the sandiaqscout.hybrid compilation device.

For detailed instructions, see the Getting Started Guide in our documentation.

⚡ Quick Start¶

import numpy as np
import pennylane as qml
import hybridlane as hqml

# Create a bosonic qiskit simulator with a custom Fock truncation
dev = qml.device("bosonicqiskit.hybrid", max_fock_level=8)

# Define a hybrid circuit with familiar PennyLane syntax
@qml.qnode(dev)
def circuit(n):
    for j in range(n):
        qml.X(0)  # Wire `0` is inferred to be a qubit
        # Use hybrid CV-DV gates from hybridlane
        hqml.JC(np.pi / (2 * np.sqrt(j + 1)), np.pi / 2, [0, "m"])

    # Mix qubit and qumode observables
    return hqml.expval(hqml.N("m") @ qml.Z(0))

# Execute the circuit
expval = circuit(5)
# array(5.)

# Analyze its structure
import hybridlane.sa as sa
res = sa.analyze(circuit._tape)
print(res)
# StaticAnalysisResult(qumodes=Wires(['m']), qubits=Wires([0]), schemas=[...])

For more examples, explore our Documentation.

🗺️ Roadmap¶

hybridlane is under active development. Here are some of our future goals:

Broader measurement support: Including mid-circuit measurements and broader measurement capabilities.
Algorithms and transformations: Implementing popular algorithms and circuit transformations from research papers, including dynamic qumode allocation.
Symbolic Hamiltonians: Introducing support for symbolic bosonic Hamiltonians.
Noisy simulation: Supporting noisy simulations with Bosonic Qiskit.
Pulse-level gates: Allowing pulse-level gates and simulating them in Dynamiqs.
Catalyst/QJIT support: Integrating with PennyLane’s qjit capabilities by developing a custom MLIR dialect.
Community-driven features: Incorporating features requested by the community during usage.

📚 Documentation¶

For comprehensive information on hybridlane’s API, tutorials, and technical background, please visit our official Documentation.

❓ Support¶

For questions, bug reports, or feature requests, please open an issue on our GitHub Issues page.

Citing hybridlane¶

If you find hybridlane useful in your research, please cite our paper:

@misc{furches2026hybridlane,
    title={Hybridlane: A Software Development Kit for Hybrid Continuous-Discrete Variable Quantum Computing},
    author={Jim Furches and Timothy J. Stavenger and Carlos Ortiz Marrero},
    year={2026},
    eprint={2603.10919},
    archivePrefix={arXiv},
    primaryClass={quant-ph},
    url={https://arxiv.org/abs/2603.10919},
}

📜 License¶

This project is licensed under the BSD 2-Clause License - see the LICENSE.txt file for details.

🙏 Acknowledgements¶

This project was supported by the U.S. Department of Energy, Office of Science, Advanced Scientific Computing Research program under contract number DE-FOA-0003265.