Quantum computing services at URI

Quantum computing can be performed on physical quantum computers (devices or QPUs), or these quantum calculations can be simulated on classical computers. Below we discuss available options. 

Quantum simulators on UNITY

While our UNITY HPC/AI/Data cluster does not include quantum devices it supports frameworks designed for simulating and executing quantum circuits on CPUs or GPUs. Here are a few details.

Qiskit (IBM)

  • General-purpose quantum computing with a mix of simulation and hardware execution.
  • Easiest for beginners with Python-based circuit construction. Well-documented, broad community support.
  • CPU-based simulator (Aer) good for small circuits (~20-30 qubits). GPU acceleration available with Aer’s cuQuantum integration.
  • Supports state vector, density matrix, and tensor network simulators.
  • If you are just starting out we recommend that you create a free account on the IBM Quantum Platform and explore the graphical quantum composer there.

cuQuantum (NVIDIA)

  • Works as a backend accelerator for other quantum frameworks such as Qiskit on NVIDIA GPUs.
  • Simulation of larger circuits (30-40) Qubits may require ~10 GPUs with NVLink.

For instructions on how to run quantum simulators on UNITY please see this page.

Access to physical quantum computers via AWS Braket 

If your research requires the use of real quantum computers, we can offer access to AWS Braket (Amazon). If you just want to use quantum circuit simulators please use one of the frameworks available on UNITY as discussed above — these are simpler to access and use and don’t incur additional costs. For access to AWS Braket please arrange a consultation with us by sending an inquiry to hpc@etal.uri.edu.

Here is a summary of the main features of the AWS Braket platform.

  • Quantum Circuit Simulators – Use Amazon’s SV1 (state vector), TN1 (tensor network), and DM1 (density matrix) simulators for efficient debugging and testing.
  • Quantum Hardware Access – Run quantum algorithms on real QPUs from IQM and Rigetti (superconducting qubits), QuEra (Rydberg atoms), and IonQ (trapped ions).
  • Hybrid Quantum-Classical Workflows – Utilize Braket Hybrid Jobs to optimize variational algorithms with cloud-based classical resources.
  • PennyLane & TensorFlow Integration – Run quantum machine learning (QML) and variational quantum eigensolvers (VQEs) with PennyLane.
  • Cost Management & Scalability – Pay-per-use pricing model with AWS cost estimation tools.
  • Managed Jupyter Notebooks – Pre-configured development environment with built-in quantum SDKs.

To get a sense of how the platform can be used take a look at this tutorial on running a simple circuit in a managed notebook.