Accelerated Quantum Supercomputing: A Hands-On Tutorial on Quantum-Classical Hybrid Workflows Executed on QPUs and GPUs
Monday, June 22, 2026 2:00 PM to 6:00 PM · 4 hr. (Europe/Berlin)
Hall X4 - 1st Floor
Tutorial
Integration of Quantum Computing and HPCQuantum Program Development and OptimizationQuantum Computing - Technologies and ArchitecturesQuantum Computing - Use CasesQuantum Machine Learning
Information
Accelerated quantum supercomputing (AQSC) requires engineers to consider the hardware, soft-
ware, and infrastructure challenges that arise when integrating quantum processing units (QPUs)
into an HPC environment. Fortunately, most of these challenges are hidden from application users,
who can leverage these advances to deliver meaningful results. This tutorial will provide attendees
with hands-on experience to learn about and utilize this AQSC infrastructure that is being deployed
at supercomputing centers around the world.
Participants will gain hands-on experience with the Python API of CUDA-Q, NVIDIA’s open-
source quantum SDK designed to unify CPU, GPU, and QPU (Quantum Processing Unit) comput-
ing. Attendees will have access to GPUs and IQM QPUs hosted at Leibniz Supercomputing Center
(LRZ). Live demonstrations and hands-on exercises on how to scale workflows, from a simple pip
install to deploying multi-GPU, multi-node, and QPU jobs will be given. This will all be tied un-
der 2 key quantum algorithms: Sample-Based Krylov Quantum Diagonalization (SKQD: quantum
& HPC) and Generative Quantum Eigensolver (GQE: AI & quantum), both of which have been
selected to align with the ISC audience and require QPU and GPU computing, emphasizing the
need for HPC and AI in quantum computing workflows.
Attendees will also learn about current challenges in scaling quantum hardware and how advance-
ments in quantum error mitigation (QEM) and quantum error correction (QEC) are best suited to
address them. They will learn about commonly used QEM and QEC techniques in superconducting
devices, execute them in workflows, and perform GPU-accelerated decoding of quantum measure-
ments.
Participants will leave with practical skills in building hybrid applications locally and the oppor-
tunities and hands-on experience in scaling them on AQSC infrastructure. Dedicated compute at
LRZ for GPUs and IQM QPUs will be provided. Jupyter notebooks, Python files, and detailed
instructions on how to connect to the cluster will be made public in a GitHub repository a week
before the tutorial which attendees can utilize during and after the tutorial
ware, and infrastructure challenges that arise when integrating quantum processing units (QPUs)
into an HPC environment. Fortunately, most of these challenges are hidden from application users,
who can leverage these advances to deliver meaningful results. This tutorial will provide attendees
with hands-on experience to learn about and utilize this AQSC infrastructure that is being deployed
at supercomputing centers around the world.
Participants will gain hands-on experience with the Python API of CUDA-Q, NVIDIA’s open-
source quantum SDK designed to unify CPU, GPU, and QPU (Quantum Processing Unit) comput-
ing. Attendees will have access to GPUs and IQM QPUs hosted at Leibniz Supercomputing Center
(LRZ). Live demonstrations and hands-on exercises on how to scale workflows, from a simple pip
install to deploying multi-GPU, multi-node, and QPU jobs will be given. This will all be tied un-
der 2 key quantum algorithms: Sample-Based Krylov Quantum Diagonalization (SKQD: quantum
& HPC) and Generative Quantum Eigensolver (GQE: AI & quantum), both of which have been
selected to align with the ISC audience and require QPU and GPU computing, emphasizing the
need for HPC and AI in quantum computing workflows.
Attendees will also learn about current challenges in scaling quantum hardware and how advance-
ments in quantum error mitigation (QEM) and quantum error correction (QEC) are best suited to
address them. They will learn about commonly used QEM and QEC techniques in superconducting
devices, execute them in workflows, and perform GPU-accelerated decoding of quantum measure-
ments.
Participants will leave with practical skills in building hybrid applications locally and the oppor-
tunities and hands-on experience in scaling them on AQSC infrastructure. Dedicated compute at
LRZ for GPUs and IQM QPUs will be provided. Jupyter notebooks, Python files, and detailed
instructions on how to connect to the cluster will be made public in a GitHub repository a week
before the tutorial which attendees can utilize during and after the tutorial
Format
on-site
Targeted Audience
The tutorial’s exercises and algorithms are tailored to ISC’s HPC, AI, and quantum audience. Its stepwise structure builds competence progressively, ensuring students, early-stage scientists, and active researchers all gain valuable insights. Each session delivers relevant, up-to-date developments, enabling participants across experience levels to learn new concepts and practical techniques.
Beginner Level
40%
Intermediate Level
60%
Prerequesites
• Proficiency in Python programming is required, but no prior experience with CUDA or MPI is needed.
• Basic awareness of quantum computing concepts, such as qubits, quantum gates, and measurements.
• Interest in hybrid quantum-classical computing, scalable quantum applications, and quantum error correction.
• Laptop with the ability to connect to the internet and a terminal window.
