Minisymposium

We propose a strategy for optimizing job throughput on supercomputers that can provide access to specialist hardware resources (such as a quantum computing device) that must be accessed exclusively. This strategy increases the utilization of this special asset significantly and thus reduces the cost related to idle time. We show examples of efficiently executing a hybrid HPC-QC workflow using state-of-the-art frameworks such as the message passing interface and the Slurm job scheduler on an HPE-Cray EX supercomputer.

In this talk I present progress on programming models which treat quantum processing units (QPUs) as accelerator devices in the HPC setting. In particular, I present CQ, a C-like specification for interacting with a quantum computer or more precisely, with a classical co-processor connected directly to a quantum computer. The specification is designed with strictly and strongly typed languages in mind, and prioritises application developers in the HPC space. I will discuss some of the design decisions behind CQ, and report progress on reference implementations.

The Qoqo QuantumProgram simplifies executing complex quantum algorithms by streamlining both input preparation and post-process analysis. It enables users to easily specify parameters and configurations for quantum circuits while offering straightforward serialization and deserialization for storing and transferring algorithms, which is particularly advantageous in collaborative or cloud-based environments. By directly yielding processed results, it minimizes communication overhead when the caller manages post-processing, enhancing overall efficiency. In our presentation, we detail its functionality and highlight its benefits through real-world examples such as HQS’s work on the QRydDemo project, where the qoqo-qryd library demonstrates the flexibility of the underlying serialization capabilities for specialized operations. Moreover, the QuantumProgram supports obtaining results from multiple circuit runs using a configured backend—whether on a simulator or a quantum processing unit (QPU)—thus addressing technological limitations, including native gate operations, and handling compilation-specific tasks like qubit shuttling via Pragma operations. Overall, qoqo effectively manages input configurations and result processing, offering users a seamless and optimized workflow for quantum computing applications, which is vital for advancing practical implementations and collaborative research in the field.

The variational quantum algorithms have a special importance in the research on quantum computing applications, for their applicability on current noisy intermediate-scale quantum (NISQ) devices. The main building blocks of these algorithms (e.g., the definition of the Hamiltonian and of the ansatz, the parameter optimization) define a relatively large parameter space, making the comparison of results and performance between different software simulators cumbersome and prone to errors. In this work, we employ a generic description of the problem, in terms of both Hamiltonian and ansatz to port a problem definition consistently among different simulators. Three use cases of relevance for current quantum hardware (ground state calculation for H_2 molecule, MaxCut, Travelling Salesman Problem) have been run on a set of HPC systems (shared-memory, multi-node, GPU-accelerated). The results show that, for some of the most used QC simulation packages, simple variational workloads present performance and scaling challenges. The presented parser allows a fair comparison of the performance while ensuring that problem definition and physical results stay consistent.