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 later are supported.

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 recommended:

    • Ubuntu 22.04.4 LTS

    • Ubuntu 24.04.4 LTS

  • 64-bit Intel i9 x86 CPU (14 cores 20 logical processors recommended) or equivalent

  • DDR5 or better memory channels recommended

  • 32 GB Installed physical memory

  • 18 GB Available physical memory

  • 64-bit Python version 3.11 or later

Supported GPU Architectures

  • Amphere, compute capabilities 8.0, 8.6

  • Hopper, compute capability 9.0

  • or later

A minimum of 4 GB public memory on the GPU is required to run the SDK. The actual amount of memory required depends upon the number of qubits used and the gate operations involved.

The following steps outline the installation procedure.

Step-1 Obtain the license

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 NVidia Drivers

Warning

This step my be challenging and you may need professional help. Installing device drivers can make your installation unstable. Always proceed with a backup and read all documentation carefully. Ensure that you are installing the correct drivers for your hardware platform. Installing open source drivers may not be a good idea.

Update the NVIDIA drivers for your system using the following link: https://www.nvidia.com/en-us/drivers/ For some linux installations, you may be required to install the NVIDIA drivers directly from the linux distribution. Search for the documentation from your linux operating system provider and go by their recommendation.

Step-3 Install the CUDA-Q platform in a python virtual environment

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 its dependencies by following the link: https://nvidia.github.io/cuda-quantum/latest/using/quick_start.html

Step-4 Install the Quantum Rings SDK

Install the Quantum Rings SDK using the following command:

pip install quantumrings-toolkit-cudaq

Note

This installs the SDK for the latest version of the CUDA-Q platform (0.10.0) . To install the SDK for the CUDA-Q platform 0.9.1, use the following command.

pip install quantumrings-toolkit-cudaq==0.1.91

Step-5 Store the credentials locally

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 Example Program

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 complex entanglements and a large number of gate 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.