HybridSuperQubits 0.1.7

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 can be installed from PyPI. However, SciPy is not included among the mandatory dependencies so that each user can install it in the most suitable way (especially relevant for macOS Apple Silicon).

Choose one of the following:

1. Quick Installation (includes SciPy)

If you do not need to control how SciPy is installed and are fine with default pip wheels:

pip install "HybridSuperQubits[scipy]"

Note for Apple Silicon (M1/M2/M3): If SciPy builds from source or runs slowly, see the Apple Silicon notes below.

2. Manual / Optimized SciPy Installation

If you prefer to handle SciPy yourself (e.g., via conda or compiling from source):

1. Create or activate a Python environment:

   Using conda:

   conda create -n hsq_env python=3.10
   conda activate hsq_env
   

   Using venv:

   python3 -m venv hsq_env
   source hsq_env/bin/activate
   # or on Windows:
   hsq_env\Scripts\activate
   

2. Install SciPy by your chosen method:

• With conda:

  conda install scipy
  

• With pip (possibly with Homebrew for libraries):

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

1. Install HybridSuperQubits (without SciPy extra):

   pip install HybridSuperQubits
   

Apple Silicon (M1/M2/M3) Notes

• Make sure you are using a native Python build (not under Rosetta).
• If SciPy compiles from source and is extremely slow, it might not be linking to Accelerate or OpenBLAS.
• Conda-forge or mambaforge often provides optimized SciPy builds for Apple Silicon.

Virtual Environments (Recommended)

Regardless of your method, installing into an isolated environment prevents dependency conflicts:

conda create -n hsq_env python=3.10
conda activate hsq_env
pip install "HybridSuperQubits[scipy]"

Or if you installed SciPy separately:

conda create -n hsq_env python=3.10
conda activate hsq_env
conda install scipy
pip install HybridSuperQubits

Upgrading

To update:

pip install --upgrade "HybridSuperQubits[scipy]"

(Or just HybridSuperQubits if you're managing SciPy yourself.)

---

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.