arXiv subjects

Jorge Echavarria

Publications and source records attributed to Jorge Echavarria.

7 recordsLinked to original sources

arXiv2026

Enabling Hybrid HPCQC Workflows with a Heterogeneous Software Stack

In this work, we demonstrate hybrid High Performance Computing-Quantum Computing (HPCQC) workflows on a production petascale system. The demonstration combines three components: the SuperMUC-NG supercomputer at the Leibniz Supercomputing Centre (LRZ), a 20-qubit superconducting quantum processor provided by IQM Quantum Computers (IQM), and Munich Quantum Valley (MQV)'s Munich Quantum Software Stack (MQSS). Integrating quantum processors into High Performance Computing (HPC) systems requires a heterogeneous software stack capable of orchestrating classical and quantum resources within established supercomputing workflows. MQSS treats Quantum Processing Units (QPUs) as scheduler-managed accelerators and it performs resource coordination following a two-level scheduling scheme. Slurm performs system-level allocation by exposing QPUs as Generic RESources (GRES), while the MQSS Quantum Resource Manager & Compiler Infrastructure (QRM&CI) performs just-in-time compilation and subsequent dispatch of quantum circuits. To integrate with existing HPC operations without modifying the scheduler core, MQSS introduces an open-source SLURM Plugin Suite based on Prolog/Epilog scripts and SPANK modules. Experimental results show that hybrid HPCQC workflows can be executed without significant latency overhead compared to conventional workloads. The presented architecture provides a portable integration model for quantum accelerators on large-scale HPC systems and is directly applicable to next-generation Hewlett Packard Enterprise (HPE) Cray platforms, including LRZ's upcoming 'Blue Lion' supercomputer.

quant-ph↗

arXiv2026

MQSS Client: Interface for Decoupling Quantum Programming Interfaces

Quantum Computing (QC) is an emerging technology that requires customized tools, such as software stacks and programming interfaces. However, currently, the tools are generally tightly coupled and exhibit limited interoperability. This, in particular, affects High Performance Computing (HPC) facilities and data centers, which are required to support multiple programming interfaces. In this paper, we introduce MQSS Client, a unifying, context-aware access layer and programming library that decouples the programming interfaces and the underlying compilation and runtime stack. MQSS Client aims to support all existing programming interfaces by providing abstractions for resources, jobs, and results. It provides two access modes to accommodate the varied needs of remote and HPC users. Thus, interoperability between software stacks and programming interfaces increases.

cs.ET↗

arXiv2026

The Munich Quantum Software Stack: Connecting End Users, Integrating Diverse Quantum Technologies, Accelerating HPC

Quantum computing is advancing rapidly in hardware and algorithms, but broad accessibility demands a comprehensive, efficient, unified software stack. Such a stack must flexibly span diverse hardware and evolving algorithms, expose usable programming models for experts and non-experts, manage resources dynamically, and integrate seamlessly with classical High-Performance Computing (HPC). As quantum systems increasingly act as accelerators in hybrid workflows -- ranging from loosely to tightly coupled -- few full-featured implementations exist despite many proposals. We introduce the Munich Quantum Software Stack (MQSS), a modular, open-source, community-driven ecosystem for hybrid quantum-classical applications. MQSS's multi-layer architecture executes high-level applications on heterogeneous quantum back ends and coordinates their coupling with classical workloads. Core elements include front-end adapters for popular frameworks and new programming approaches, an HPC-integrated scheduler, a powerful MLIR-based compiler, and a standardized hardware abstraction layer, the Quantum Device Management Interface (QDMI). While under active development, MQSS already provides mature concepts and open-source components that form the basis of a robust quantum computing software stack, with a forward-looking design that anticipates fault-tolerant quantum computing, including varied qubit encodings and mid-circuit measurements.

quant-ph↗

arXiv2025

Tackling the Challenges of Adding Pulse-level Support to a Heterogeneous HPCQC Software Stack: MQSS Pulse

We study the problem of adding native pulse-level control to heterogeneous High Performance Computing-Quantum Computing (HPCQC) software stacks, using the Munich Quantum Software Stack (MQSS) as a case study. The goal is to expand the capabilities of HPCQC environments by offering the ability for low-level access and control, currently typically not foreseen for such hybrid systems. For this, we need to establish new interfaces that integrate such pulse-level control into the lower layers of the software stack, including the need for proper representation. Pulse-level quantum programs can be fully described with only three low-level abstractions: ports (input/output channels), frames (reference signals), and waveforms (pulse envelopes). We identify four key challenges to represent those pulse abstractions at: the user-interface level, at the compiler level (including the Intermediate Representation (IR)), and at the backend-interface level (including the appropriate exchange format). For each challenge, we propose concrete solutions in the context of MQSS. These include introducing a compiled (C/C++) pulse Application Programming Interface (API) to overcome Python runtime overhead, extending its LLVM support to include pulse-related instructions, using its C-based backend interface to query relevant hardware constraints, and designing a portable exchange format for pulse sequences. Our integrated approach provides an end-to-end path for pulse-aware compilation and runtime execution in HPCQC environments. This work lays out the architectural blueprint for extending HPCQC integration to support pulse-level quantum operations without disrupting state-of-the-art classical workflows.

quant-ph↗

arXiv2025

First Practical Experiences Integrating Quantum Computers with HPC Resources: A Case Study With a 20-qubit Superconducting Quantum Computer

Incorporating Quantum Computers into High Performance Computing (HPC) environments (commonly referred to as HPC+QC integration) marks a pivotal step in advancing computational capabilities for scientific research. Here we report the integration of a superconducting 20-qubit quantum computer into the HPC infrastructure at Leibniz Supercomputing Centre (LRZ), one of the first practical implementations of its kind. This yielded four key lessons: (1) quantum computers have stricter facility requirements than classical systems, yet their deployment in HPC environments is feasible when preceded by a rigorous site survey to ensure compliance; (2) quantum computers are inherently dynamic systems that require regular recalibration that is automatic and controllable by the HPC scheduler; (3) redundant power and cooling infrastructure is essential; and (4) effective hands-on onboarding should be provided for both quantum experts and new users. The identified conclusions provide a roadmap to guide future HPC center integrations.

quant-ph↗

arXiv2025

Q-BEAST: A Practical Course on Experimental Evaluation and Characterization of Quantum Computing Systems

Quantum computing (QC) promises to be a transformative technology with impact on various application domains, such as optimization, cryptography, and material science. However, the technology has a sharp learning curve, and practical evaluation and characterization of quantum systems remains complex and challenging, particularly for students and newcomers from computer science to the field of quantum computing. To address this educational gap, we introduce Q-BEAST, a practical course designed to provide structured training in the experimental analysis of quantum computing systems. Q-BEAST offers a curriculum that combines foundational concepts in quantum computing with practical methodologies and use cases for benchmarking and performance evaluation on actual quantum systems. Through theoretical instruction and hands-on experimentation, students gain experience in assessing the advantages and limitations of real quantum technologies. With that, Q-BEAST supports the education of a future generation of quantum computing users and developers. Furthermore, it also explicitly promotes a deeper integration of High Performance Computing (HPC) and QC in research and education.

physics.ed-ph↗

arXiv2021

On the Approximation of Accuracy-configurable Sequential Multipliers via Segmented Carry Chains

In this paper, we present a multiplier based on a sequence of approximated accumulations. According to a given splitting point of the carry chains, the technique herein introduced allows varying the quality of the accumulations and, consequently, the overall product. Our approximate multiplier trades-off accuracy for a reduced latency (with respect to an accurate sequential multiplier) and exploits the inherent area savings of sequential over combinatorial approaches. We implemented multiple versions with different bit-width and accuracy configurations, targeting an FPGA and a 45nm ASIC to estimate resources, power consumption, and latency. We also present two error analyses of the proposed design based on closed-form analysis and simulations.

cs.AR↗