hybridlane 0.6.1a1

A frontend library for hybrid CV-DV computation based on Pennylane.

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).

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✨ 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.

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🚀 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.

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⚙️ 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.

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⚡ 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.

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🗺️ 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.

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📚 Documentation

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

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❓ Support

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

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Citing hybridlane

If you use hybridlane in your research, please cite our work:

under preparation, check back soon :)

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📜 License

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

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🙏 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.