Documentation
This library extends Google's Cirq framework to support qudits.
Features
- Qudit Support: Work with qudits of any dimension.
-
Generalized Qudit Gates: Implementations of qudit
versions of common quantum gates:
- Pauli-X Gate
- Pauli-Z Gate
- Hadamard Gate
- Controlled-NOT Gate
- Pi-Over-Eight Gate (or T Gate)
- Phase Gate
- Controlled-Z Gate
-
Circuit Utilities: Functions to build and
manipulate circuits with qudits, including a versatile
create_circuitfunction. - Measurement and Simulation: Support for measuring qudits and simulating qudit circuits using Cirq's simulator.
Installation
Prerequisites
- Python 3.7 or higher: Ensure you have a compatible Python version installed.
- pip: The Python package installer.
Installing Cirq
-
Install Cirq via pip:
pip install cirq -
Verify the installation:
Ensure the version is 0.10.0 or higher for qudit support.python -c "import cirq; print('Cirq version:', cirq.__version__)"
Installing the Qudit Cirq Library via Anaconda
Prerequisites
- Anaconda or Miniconda installed on your system.
- Git installed for cloning repositories.
Steps
-
Create a New Anaconda Environment
Replace 3.8 with your preferred Python version (3.7 or higher).conda create -n qudit-env python=3.8 -
Activate the Environment
On Windows:conda activate qudit-envactivate qudit-env -
Install Cirq and Dependencies
Alternatively, use pip:conda install -c conda-forge cirq numpy matplotlibpip install cirq numpy matplotlib -
Verify the Installation
Ensure the version is 0.10.0 or higher.python -c "import cirq; print('Cirq version:', cirq.__version__)" -
Clone the Qudit Library Repository
git clone https://github.com/svtsv01/qudit.git -
Navigate to the Library Directory
cd qudit-cirq-library -
Install the Qudit Library
pip install -e .
Dependencies
The Qudit Cirq Library depends on:
- Cirq (>=0.10.0)
- NumPy
- Matplotlib (optional)
Ensure these dependencies are installed:
pip install cirq numpy matplotlibFixing Common Dependency Issues
Version Conflicts
Use the Anaconda environment (qudit-env) to manage
dependencies and avoid conflicts with other Python packages.
conda activate qudit-envList environments:
conda info --envsUpgrading Packages
If you have older versions of the packages, upgrade them:
pip install --upgrade cirq numpy matplotlibConstraints and Limitations
Qudit circuits are computationally intensive. Memory usage and simulation time increase significantly with larger dimensions and qudit counts.
Cloud-Computing Services
Consider using cloud services like AWS, GCP, or Azure for scalable quantum simulations.