> ## Documentation Index
> Fetch the complete documentation index at: https://docs.qbraid.com/llms.txt
> Use this file to discover all available pages before exploring further.

# GPUs

<div style={{ display: "flex", justifyContent: "center", alignItems: "center" }}>
  <img src="https://mintcdn.com/qbraidco/MWMLuPZDp_BcNo9C/lab/_static/gpu_nvidia_banner.png?fit=max&auto=format&n=MWMLuPZDp_BcNo9C&q=85&s=d30f25a81506fd0ac2a731b1aa4b136a" width="150%" data-path="lab/_static/gpu_nvidia_banner.png" />
</div>

The qBraid Lab GPU server is tailored for researchers and developers requiring enhanced computational capabilities. This high-performance Lab instance allows users to leverage GPUs for accelerated circuit simulation, to explore quantum machine learning applications with GPU-enabled quantum gradients, and more.

* **Wide GPU access**: V100, A100, H100, GH200, and B200 class GPUs, available in various configurations. Billing is in credits/minute with rates shown in your account launcher.
* **Pre-configured Python environment**: Activate the default environment by running `qbraid envs activate default` in a terminal or simply open a notebook and make sure the `default` kernel is selected. This environment includes GPU-optimized versions of qiskit / qiskit-aer, cudaq, pennylane / pennylane-lightning, PyTorch, TensorFlow, and JAX.

## Launch GPU instance

Use the drop-down at the top of your [account page](https://account.qbraid.com/) to select the GPU Lab image, and then click
**Launch New Instance**.

<div style={{ display: "flex", justifyContent: "center", alignItems: "center" }}>
  <img src="https://storage.googleapis.com/qbraid-static-assets/qbraid-docs/lab/gpu-starting.gif" alt="gpu_image" width="150%" />
</div>

You will first see a "Starting GPU Server" panel while your server is being prepared -

{" "}

<img src="https://storage.googleapis.com/qbraid-static-assets/qbraid-docs/lab/launching-gpu.png" width="150%" />

Once the GPU instance is ready, the status will change to "Running". Click **Open Running Server** to access your GPU instance -

{" "}

<img src="https://storage.googleapis.com/qbraid-static-assets/qbraid-docs/lab/gpu-running.png" width="150%" />

<Note>
  The GPU servers may take up to 15 minutes to launch compared to standard Lab
  instances, as the GPU resources are provisioned on-demand.
</Note>

## Available GPU Configurations

qBraid offers a variety of GPU configurations to meet different computational needs. See [pricing](/v2/home/pricing#gpu-instances) for a complete list.

Further information can be retrieved using the
[NVIDIA System Management Interface](https://developer.nvidia.com/nvidia-system-management-interface) (`nvidia-smi`) and
[NVIDIA CUDA Toolkit](https://developer.nvidia.com/cuda-toolkit) (`nvcc`) command line utilities.

<Note>
  Visit your [account page](https://account.qbraid.com/) to see the full list of
  GPU options including real-time availability.
</Note>

## GPU-enabled environments

The GPU Lab image comes pre-configured with the [NVIDIA cuQuantum SDK](https://developer.nvidia.com/cuquantum-sdk) GPU simulator
library, and includes GPU integrations with other popular quantum softwares packages such as Pennylane-Lightning, Qiskit Aer, and
Qsim-Cirq.

<div style={{ display: "flex", justifyContent: "center", alignItems: "center" }}>
  <img src="https://mintcdn.com/qbraidco/MWMLuPZDp_BcNo9C/lab/_static/gpu_lab_launcher.png?fit=max&auto=format&n=MWMLuPZDp_BcNo9C&q=85&s=a55acacacfbe7590fddbecb61f5ea0ce" width="150%" data-path="lab/_static/gpu_lab_launcher.png" />
</div>

### Pennylane-Lightning

[PennyLane](https://docs.pennylane.ai/) is a cross-platform Python library for quantum machine learning,
automatic differentiation, and optimization of hybrid quantum-classical computations. The
[PennyLane-Lightning-GPU](https://github.com/PennyLaneAI/pennylane-lightning-gpu) plugin extends the
[Pennylane-Lightning](https://github.com/PennyLaneAI/pennylane-lightning) state-vector simulator written in C++,
and offloads to the NVIDIA cuQuantum SDK for GPU accelerated circuit simulation.

The `lightning.gpu` device is an extension of PennyLane's built-in `lightning.qubit` device. It extends the
CPU-focused Lightning simulator to run using the NVIDIA cuQuantum SDK, enabling GPU-accelerated simulation of
quantum state-vector evolution.

A `lightning.gpu` device can be loaded using:

```python theme={null}
import pennylane as qml

dev = qml.device("lightning.qubit", wires=2)
```

The above device will allow all operations to be performed on the pre-configured CUDA capable GPU. If not used inside the
qBraid GPU instance, or if the cuQuantum libraries are not installed in the given environment, the device will fall-back
to `lightning.qubit` and perform all simulation on the CPU.

### Qiskit Aer

Qiskit is an open-source framework for working with noisy quantum computers at the level of pulses, circuits, and algorithms.
The [Qiskit Aer](https://github.com/Qiskit/qiskit-aer) library provides high-performance quantum computing simulators with
realistic noise models.

On qBraid, the Qiskit Aer GPU environment comes with the `qiskit-aer-gpu` package, extending the same functionality of the
canonical `qiskit-aer` package, plus the ability to run the GPU supported simulators: statevector, density matrix, and unitary.

Here is a basic example:

```python theme={null}
import qiskit
from qiskit_aer import AerSimulator

# Generate 3-qubit GHZ state
circ = qiskit.QuantumCircuit(3)
circ.h(0)
circ.cx(0, 1)
circ.cx(1, 2)
circ.measure_all()

# Construct an ideal simulator
aersim = AerSimulator(method='statevector', device='GPU')

# Perform an ideal simulation
result_ideal = qiskit.execute(circ, aersim).result()
counts_ideal = result_ideal.get_counts(0)
print('Counts(ideal):', counts_ideal)
# Counts(ideal): {'000': 493, '111': 531}
```

## What's Next

We're actively expanding GPU capabilities on qBraid. Here's what to expect:

<Note>
  **Expanding environment support**: You can create and persist local environments in GPU sessions. Shareable environments and pre-packaged environments from standard qBraid are on the roadmap.

  **Growing capacity**: We're continuously adding more GPU resources to meet demand. If capacity is temporarily unavailable, check back periodically as capacity becomes available.

  **Improving startup reliability**: If you encounter any NVIDIA setup issues after starting a GPU, run `nvidia-smi` in a terminal session. If you see an error, simply restart the same instance.
</Note>