quantumrings-nvidia-gpu 0.12.2000

Welcome to the Quantum Rings SDK for NVIDIA GPU

This document serves as a guide for installing the Quantum Rings SDK in the GPU-enabled mode. Before starting the installation process, please review the sections that outline the minimum system requirements and supported GPU architectures.

Additionally, the section titled “Feedback and Getting Support” provides links for submitting feedback or obtaining further assistance.

Installation Procedure

The Quantum Rings SDK now supports Nvidia GPUs in both native mode and with the Qiskit toolkit. The following steps outline the installation procedure.

STEP - 1

To obtain your license for the Quantum Rings SDK, register by selecting the Login option from the menu.

If you are already registered, you can skip this step.

Next, log in to the Quantum Rings portal. To download your access keys, select the Manage Keys option in the left sidebar.

STEP - 2

Update the NVIDIA drivers for your system. For some Linux distributions, you may need to install the NVIDIA drivers directly from the distribution. Please search for the documentation from your Linux operating system provider and follow their recommendation.

If you are installing on WSL-based Linux, you must update the NVIDIA driver in Windows. It will automatically apply to the WSL Linux instance.

Note down the driver version by running the nvidia-smi command in the terminal and observing the version displayed in the top panel. You will need to know the driver version to install the CUDA Toolkit later.

STEP - 3

Create a virtual environment for your Python version using the following example.

virtualenv --python=/usr/bin/python3.11 myenv311
source myenv31/bin/activate

In some installations, the virtual environment can be created as follows:

python3.11 -m venv myenv311
source myenv311/bin/activate

Note that installing a Python virtual environment may require additional steps.

You can optionally install Jupyter Notebook using the following command.

pip install notebook

STEP - 4

Choose the appropriate CUDA Toolkit (CTK) for your driver version. Section 2.2 CUDA Driver in Release notes outlines the CUDA Driver version range and the CUDA Toolkit (CTK) you could install. Install the CUDA Toolkit (CTK) by following the instructions in the link: CUDA Toolkit Follow the instructions on the screen after installing and setting up the CUDA Toolkit, and set the paths as directed.

If environment variables are not set correctly, linker errors will occur, and the SDK will not load. If you are a Windows user, see additional instructions in Section 6 to set up the DLL search path with Python.

If you are using your university supercomputer or a cloud environment with NVIDIA GPUs, your system administrator may already have installed the necessary runtime components optimized for your hardware platform. You may only need to select the CUDA Toolkit module. Selecting the CUDA Toolkit is typically done using a module loader, as shown below. Browse the modules installed in your system and choose the most recent CUDA Toolkit. Note that your system may use a different way of loading runtime components.

module load cuda-12.6.1-gcc-12.1.0

STEP - 5

To install the Quantum Rings SDK, use the command appropriate for your CUDA Toolkit version:

If you are using CUDA Toolkit version 13.x:

pip install quantumrings-nvidia-gpu

or

pip install quantumrings[cuda13x]

If you are using CUDA Toolkit version 12.x:

pip install quantumrings[cuda12x]

STEP - 6

Try running the following Python program in your Jupyter notebook to ensure everything is functioning correctly.

# For Windows users. Linux users may skip this.
# Setup the CUDA Toolkit path with Python
import os
import platform

if platform.system() == "Windows":
    cuda_path = os.getenv("CUDA_PATH", "")
    if "" == cuda_path:
        #set a hard-coded path
        cuda_path = "C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v13.0\\bin\\x64"
    else:
        #create from the environment
        if "13" in cuda_path:
            cuda_path += "\\bin\\x64"
        else:
            cuda_path += "\\bin"

    os.add_dll_directory(cuda_path)
#
#

import QuantumRingsLib
from QuantumRingsLib import QuantumRegister, AncillaRegister, ClassicalRegister, QuantumCircuit
from QuantumRingsLib import QuantumRingsProvider
from QuantumRingsLib import job_monitor
from QuantumRingsLib import JobStatus
from QuantumRingsLib import OptimizeQuantumCircuit
from matplotlib import pyplot as plt
import numpy as np
import math

provider = QuantumRingsProvider(token =<YOUR_TOKEN_HERE>, name=<YOUR_ACCOUNT_NAME_HERE>)
backend = provider.get_backend("amber_quantum_rings")
numberofqubits = 50
shots = 100

q = QuantumRegister(numberofqubits , 'q')
c = ClassicalRegister(numberofqubits , 'c')
qc = QuantumCircuit(q, c)

qc.h(0)
for i in range (qc.num_qubits - 1):
    qc.cnot(i, i + 1)

qc.measure_all()

job = backend.run(qc, shots=shots)
job_monitor(job)

result = job.result()
counts = result.get_counts()
print(counts)

backend = provider.get_backend("amber_quantum_rings")

backend = provider.get_backend("scarlet_quantum_rings")

provider = QuantumRingsProvider()
backend = provider.get_backend()

backend = provider.get_backend("serin_quantum_rings")

Feedback and getting support

We love to hear from you! Please join our Discord community to discuss anything quantum computing.

SDK Documentation

FAQ (Requires you to login)

Managing your license keys (Requires you to login)

Need more qubits? Request here (Requires you to login)