Setup and install the SDK
Minimum System Requirements
A system with a configuration better than the minimum requirements is advised. Lower configurations may affect the number of qubits that can be supported and may perform poorly.
Operating systems supported:
Windows 11 Pro
Google Colab
Debian GNU/Linux 12 (bookworm)
OpenSUSE Tumbleweed - Version 20240415
Oracle Linux 9.3
Ubuntu 22.04.4 LTS
macOS Sequoia
64-bit x86 CPU (14 cores 20 logical processors recommended) on Windows or Linux platforms
64-bit Intel or Apple Silicon CPUs on Apple PCs
32 GB Installed physical memory
18 GB Available physical memory
64-bit Python version 3.11 on Windows
64-bit Python 3.6, 3.10, 3.11, and 3.12 on various Linux versions
64-bit Python 3.13 on macOS
Installation - Windows
The Quantum Rings SDK can be installed directly using pip. Many users find Anaconda (https://www.anaconda.com/) a good way to install the Quantum Rings SDK and use the Python environment efficiently. From Anaconda, select a Python 3.11 channel and launch CMD.exe Prompt to go to the command prompt and execute the following command.
pip install QuantumRingsLib
If you do not have a Python 3.11 channel, select Environments from the left panel, + Create button from the menu bar at the bottom and select Python 3.11 from the Create New Environment dialog.
Quantum Rings SDK requires a 64-bit version of Python 3.11.
After installation of the SDK, launch Python 3.11 environment by selecting the installed channel, before running your code.
Note
If, in case, pip is not able to find the latest version, please try the following command instead:
pip install –extra-index-url https://pypi.python.org/pypi QuantumRingsLib
You may have to restart the system in some installations for the SDK to work.
Installation - macOS Sequoia
Install Python 3.13 by downloading it from the Python Organization (https://www.python.org/downloads/macos/). Other methods of installing Python are not supported at this time. From the terminal, create a Python 3.13 virtual environment by executing the following command. Activate the environment.
python3.13 -m venv myenv
source myenv/bin/activate
Then execute the following command to install the SDK.
pip install QuantumRingsLib
You may also refer to https://docs.python.org/3/using/mac.html for further help. If you have a Python version that came with a previous version of macOS and if you uninstalled it, you may have broken symlinks. Refer to section 5.5.1 on this webpage for hints on how to fix the symlinks.
Note
Currently, we do not support Conda based Python installations or other methods of installing Python. Support may be added in the future. Only Python 3.13 installation by downloading it from the Python Organization is supported at this time. Former versions of macOS are not supported.
Installation - Google Colab
Open a notebook and execute the following command from a code cell.
pip install QuantumRingsLib
Restart the kernel. You are ready to go!
Installation - Linux
Checking whether your Linux platform is supported
There are several variants of the Linux OS, with varying levels of inbuilt libraries. At present, we are supporting manylinux_2_34_x86_64 platforms based on 64 bit x86 processors. Older platforms and other CPUs are not supported at the moment. If you have a specific requirement, please contact our technical support.
To check whether your platform is supported, execute the following command from Python command line:
import platform
platform.platform()
Watch for the glibc signature at the end. glibc2.34 and above are only supported.
Installing Python 3.11 and creating the virtual environment
Note
Update all packages in your system using the following commands. Note that, this may cause incompatibilities in some installations due to variances in packages and their mutual dependencies. Besides, this step might break existing software packages and make your system unusable. You may refer to your operating system’s manual or seek help from your system adminstrator. Alternatively, you can also use the Software Updater GUI tool, if that was packaged along with your system distribution.
sudo apt update # Fetches the list of available updates
sudo apt upgrade # Installs some updates; does not remove packages
Note
Note that apt command is not available in some Linux variants. You may have to use dnf on Oracle Linux distributions and zypper on Open SUSE distributions.
Check whether your system has Python 3.11 installed by executing python from the terminal. If not, you can install Python 3.11 using the following command.
sudo apt install python3.11
Create a virtual Python 3.11 environment and activate the environment using the following steps.
virtualenv --python=/usr/bin/python3.11 myenv
source myenv/bin/activate
You may have to install virtualenv package if it is not already installed. When not required, you can deactivate the environment using deactivate command.
Note that, virtualenv package is not available on Oracle Linux and Open SUSE Linux. You can use the following command equivalently on these distributions:
python3.11 -m venv myenv
source myenv/bin/activate
Installing Jupyter notebook
Note
Jupyter notebook requires GUI support and an internet browser. Some platforms do not support them readily.
Now, launch the virtual environment, and execute the following command:
sudo su
source myenv/bin/activate
pip install notebook
Ensuring that jupyter notebook launches the correct python version
Check whether your virtual environment launches the correct python version (3.11) by executing the command python –version. On some installations you may have to execute the following commands to ensure that the Jupyter notebook launches the correct python version.
python -m pip install ipykernel
python -m ipykernel install --user
Now, you can launch the Jupyter notebook using the command jupyter-notebook or jupyter notebook. If you are logged in as root, then you may have to append –allow-root. Once the notebook server starts, you can click the local-host link to launch the notebook on the browser.
Installing Curl
On some installations, we found that curl is not installed. You can check whether curl is installed on your system by executing the command curl from the terminal. If curl is not installed, you may use the following command:
sudo apt install curl
Installing gcc
On some installations, we got libgomp.so not found error and it required installation of gcc, as follows:
zypper install gcc
Finally, installing the Quantum Rings SDK
Now, you can install the Quantum Rings SDK as follows:
pip install QuantumRingsLib
Installing the toolkit for qiskit
Quantum Rings SDK now offers a toolkit for seamless integration with qiskit. To install, follow the procedure outlined below.
If you do not have qiskit installed, you can install qiskit by following the instructions outlined at https://docs.quantum.ibm.com/guides/install-qiskit.
Supported qiskit version
Quantum Rings toolkit for qiskit supports qiskit version 1.3.1. Earlier versions are not supported. You can check your current installation by executing the following command:
import qiskit
print (qiskit.__version__)
If your qiskit installation is not 1.3.1, follow the instructions in the link https://docs.quantum.ibm.com/guides/install-qiskit to uninstall older qiskit versions and install version 1.3.1.
Installing the toolkit
Quantum Rings toolkit for qiskit requires QuantumRingsLib version 0.9.0 or later. You can check your current installation using the following command.
import QuantumRingsLib
print (QuantumRingsLib.__version__)
If your version is not 0.9.0 or later, you can update QuantumRingsLib using the following command:
pip install QuantumRingsLib --upgrade
After getting the correct version of QuantumRingsLib installed, execute the following command:
pip install quantumrings-toolkit-qiskit
You should be good to get started!
Installing the Quantum Rings SDK for NVIDIA CUDA-Q
Quantum Rings SDK now supports CUDA-Q platform in the Python programming mode. CUDA-Q platform 0.9.1 or above are supported.
The following steps outline the installation procedure.
STEP - 1
Obtain your license to the Quantum Rings SDK by registering at: https://www.quantumrings.com/
Skip this step, if you are already registered.
Login to the Quantum Rings portal. Download your access keys by selecting the “Manage Keys” menu in the left-hand side bar.
STEP - 2
Update the NVIDIA drivers for your system using the following link: https://www.nvidia.com/en-us/drivers/
STEP - 3
Create a virtual environment for your python version using the following example.
virtualenv --python=/usr/bin/python3.11 myenv
source myenv/bin/activate
You can use Python 3.12 or another Python version of your choice. However, remember that the tool kits used by the CUDA-Q tutorials may not be compatible with your choice of Python version.
If you have not installed Jupyter notebook, you can install it at this time using the following command.
pip install notebook
Install the CUDA-Q platform and it dependencies by following the link: https://nvidia.github.io/cuda-quantum/latest/using/quick_start.html
STEP - 4
Download the Quantum Rings SDK for CUDA-Q from the following link: Click here
Now from your terminal install the SDK using the following command:
bash install_quantum_rings_0.11.0_cudaq_0.9.1.run
STEP - 5
Store your credentials to the Quantum Rings SDK locally using the following commands from your terminal. Edit the <…> fields and enter the values provided for you.
If you are using the Jupyter notebook, open the termnial as follows:
From your Jupyter notebook, select File->New->Terminal. This launches the terminal window.
From this terminal window, create the configuration file holding the license key as follows:
mkdir ~/.config/quantumrings
echo -e '[default]\nemail = <YOUR_ACCOUNT_ID>\nkey = <YOUR_ACCESS_KEY>\nbackend = scarlet_quantum_rings' > ~/.config/quantumrings/quantumrings.conf
STEP - 6
Now, try the following program from your Jupyter notebook to ensure that everything is working fine.
import cudaq
import numpy as np
cudaq.set_target("QuantumRingsLib")
num_of_qubits = 3
kernel = cudaq.make_kernel()
qubits = kernel.qalloc(num_of_qubits)
kernel.x(qubits)
kernel.h(qubits)
print(cudaq.draw(kernel))
state = cudaq.get_state(kernel)
print(np.array(state))
Note
Use cudaq.set_target(“QuantumRingsLib”) to select the Quantum Rings engine.
Using the GPU Mode
Certain programs with large number of qubits (> 22) with dense operations can benefit from using a GPU.
To switch to the GPU mode, edit the configuration file using the following command
vi ~/.config/quantumrings/quantumrings.conf
Change the backend from scarlet_quantum_rings to amber_quantum_rings
Note
Programs with a few qubits and gate operations usually run faster on the cpu mode with the scarlet_quantum_rings backend.
Supported GPU Architectures
Turing, compute capability 7.5 (limited support)
Amphere, compute capabilities 8.0, 8.6
Hopper, compute capability 9.0
or later