HybridSuperQubits 0.9.0

Package to simulate hybrid superconducting qubits

HybridSuperQubits 🌀⚡

A Python framework for simulating hybrid semiconductor-superconductor quantum circuits.

Key Features ✨

• Hybrid Circuit Simulation 🔬
  Unified framework for semiconductor-superconductor systems.

• Advanced Noise Analysis 📉

  • Capacitive losses (t1_capacitive).
  • Inductive losses (t1_inductive).
  • Flux noise (tphi_1_over_f_flux).
  • Coherence Quantum Phase Slip (tphi_CQPS).

• Professional Visualization 📊

  • Wavefunction plotting (plot_wavefunction).
  • Matrix element analysis (plot_matelem_vs_paramvals).
  • Spectrum vs parameter sweeps.

• SC-Qubits Compatibility 🔄
  API-inspired interface for users familiar with scqubits

---

🚀 Installation

HybridSuperQubits is available on PyPI.
SciPy is kept as an optional dependency to let users install it optimally (especially on Apple Silicon).

1. Quick Installation (includes SciPy)

If you do not need to manage SciPy installation yourself, simply:

pip install "HybridSuperQubits[scipy]"

Apple Silicon (M1/M2/M3): If SciPy compiles from source or runs slowly, check the notes below.

2. Manual / Optimized SciPy Installation

If you prefer to install SciPy independently (for example, via conda or building from source):

1. (Optional) Create or activate a Python environment:

• Conda example:

    conda create -n hsq_env python=3.10
    conda activate hsq_env
  

• venv example:

    python3 -m venv hsq_env
    source hsq_env/bin/activate     # macOS/Linux
    hsq_env\Scripts\activate        # Windows
  

2. Install SciPy by your preferred approach:

• Conda:

     conda install scipy
  

• pip + Homebrew (if compiling from source):

     brew install openblas gcc
     pip install --upgrade pip setuptools wheel
     pip install scipy
  

3. Install HybridSuperQubits (without [scipy]):

    pip install HybridSuperQubits
   

Apple Silicon Notes (M1/M2/M3)

• Use a native Python build (not under Rosetta).

• If SciPy or HybridSuperQubits tries to compile from source and you do not get a precompiled wheel, you may need OpenBLAS and environment variables:

  conda install -c conda-forge openblas
  export LDFLAGS="-L/opt/homebrew/opt/openblas/lib"
  export CFLAGS="-I/opt/homebrew/opt/openblas/include"
  export BLAS=~/opt/homebrew/opt/openblas/lib
  pip install HybridSuperQubits
  

• Installing SciPy via conda-forge or mambaforge typically provides optimized builds automatically.

Upgrading

To upgrade HybridSuperQubits:

pip install --upgrade "HybridSuperQubits[scipy]"

(Or just HybridSuperQubits if handling SciPy separately.)

---

Basic Usage 🚀

Supported Qubit Types

1. Andreev
2. Gatemon
3. Gatemonium
4. Fermionic bosonic qubit

Initialize a hybrid qubit

from HybridSuperQubits import Andreev, Gatemon, Gatemonium, Ferbo

# Fermionic-Bosonic Qubit (Ferbo)
qubit = Ferbo(
    Ec=1.2,          # Charging energy [GHz]
    El=0.8,          # Inductive energy [GHz]
    Gamma=5.0,       # Coupling strength [GHz]
    delta_Gamma=0.1, # Asymmetric coupling [GHz]
    er=0.05,         # Fermi detuning [GHz]
    phase=0.3,       # External phase (2 pi Φ/Φ₀)
    dimension=100    # Hilbert space dimension
)

# Andreev Pair Qubit
andreev_qubit = Andreev(
    EJ=15.0,        # Josephson energy [GHz]
    EC=0.5,         # Charging energy [GHz]
    delta=0.1,      # Superconducting gap [GHz]
    ng=0.0,         # Charge offset
    dimension=50
)

# Gatemonium
gatemonium = Gatemonium(
    EJ=10.0,        # Josephson energy [GHz]
    EC=1.2,         # Charging energy [GHz]
    ng=0.0,         # Charge offset
    dimension=100
)

Documentation 📚

Full API reference and theory background available at: hybridsuperqubits.readthedocs.io

Contributing 🤝

We welcome contributions! Please see:

CONTRIBUTING.md for development guidelines

License

This project is licensed under the MIT License. However, it includes portions of code derived from scqubits, which is licensed under the BSD 3-Clause License.

For more details, please refer to the LICENSE file.

📖 Citation

If you use this software in your research, please cite it using the following BibTeX entry

@software{joan_j_caceres_2025_15315785,
  author       = {Joan J. Cáceres},
  title        = {joanjcaceres/HybridSuperQubits},
  month        = may,
  year         = 2025,
  publisher    = {Zenodo},
  version      = {v0.8.2},
  doi          = {10.5281/zenodo.15315785},
  url          = {https://doi.org/10.5281/zenodo.15315785},
}

or using the Citation tool at HybridSuperQubits' Zenodo