arXiv subjects

Minh Chung

Publications and source records attributed to Minh Chung.

8 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

Explainable quantum-compressed machine learning for complex fluid flows

Machine-learning surrogates of physical systems face a paradox: explainable models facing the challenge of expressivity to capture complex nonlinear flows, whereas expressive deep surrogates match high-fidelity simulations only through massive parameterisations that turn the learned dynamics into a black box. Here, we introduce quantum-compressed machine learning (QCML), which resolves this tension by compressing the latent propagator of a flow surrogate from $524{,}288$ trainable parameters to no more than $8$. This parameter reduction brings the learned dynamical law to the parameter scale of a physical constitutive relation rather than a black-box neural network, making the surrogate directly interpretable and controllable without sacrificing expressivity. The compression is realised by a structured quantum circuit whose unitary propagator constrains the latent spectrum to the unit circle exactly and by construction, replacing exponential error growth with linear accumulation over autoregressive rollouts. Classical regularisation only approximates this constraint: even a quantum-inspired classical baseline penalised towards unitarity collapses within one Lyapunov time on turbulent channel flow, whereas QCML remains stable over the full rollout. Shared phase and coupling angles parameterising the circuit correspond directly to modal frequencies and inter-mode interactions, giving the learned dynamics a physical interpretation in spectral space. On two patient-specific cardiovascular benchmarks, the structured QCML propagator matches the predictive accuracy of its classical counterpart on surface pressure spectra, pressure drop, and wall shear stress. These results establish QCML as a working component of scientific machine learning and a concrete contribution towards practical quantum advantage in real-world prediction.

physics.flu-dyn↗

arXiv2026

Practical Quantum Advantage before Fault Tolerance via Quantum-Informed Machine Learning

Early quantum devices can deliver a practical advantage before fault tolerance. The role we identify is a statistical module within a classical scientific workflow: a compressed memory with a collective two-copy readout, evaluated against a verifiable definition of practical quantum advantage. We develop this mechanism in quantum-informed machine learning for chaotic dynamical systems. A family of $k$-indexed higher-order quantum statistical priors (Q-Priors) hosts the $k$-point marginal of the invariant measure on $n_q = kq$ qubits. We prove a two-stage advantage. In the representation stage, superposition and entanglement compactly store non-factorisable spatial correlations of the invariant measure on $n_q$ qubits. In the extraction stage, joint Bell measurements estimate any \emph{post hoc} Pauli functional with a copy-pair count independent of $n_q$, whereas any adaptive single-copy protocol for the corresponding full-Pauli read-out requires $\Omega(2^{n_q})$ copies; this is a provable quantum-classical separation in copy-measurement complexity. The two-copy read-out is realised in simulation and on superconducting processors. Two case studies instantiate the mechanism in workflows of scientific value. In a turbulent channel-flow study, the readout yields the velocity-direction coherence as a named non-diagonal correlator, and the $k = 2$ Q-Prior recovers invariant-measure statistics that the unregularised baseline loses. In a medium-range weather forecasting workflow on the ECMWF ERA5 reanalysis, the diagonal $k \leq 2$ Q-Prior steers a Koopman rollout, improves anomaly correlation skill and stabilises long-horizon rollouts against collapse onto a static mean field. Together, the mechanism and these two case studies satisfy our practical-advantage definition, identifying a candidate route to practical quantum advantage before fault-tolerant hardware.

quant-ph↗

arXiv2026

Evaluating System-Level Fidelity with Peaked Random Circuits

Quantum computing is transitioning from experimental prototypes to commercially available turnkey systems, making architecture-agnostic performance metrics essential for cross-platform comparison. Peaked Random Circuits (PRCs) have recently been proposed as a viable path to demonstrate quantum advantage on NISQ devices: a quantum processor can reliably detect a single, peaked output state amid background noise, yet the circuits' characteristics render classical simulation infeasible. In this paper, we repurpose PRCs as a system-level fidelity benchmark. By successively running a matrix of PRCs with varying qubit counts and circuit depths, we quantify a system's ability to identify the deterministic peak despite cumulative noise, gate errors, and connectivity constraints. We apply the benchmark on IQM's superconducting and AQT's trapped-ion architectures. Our results show that PRCs provide a high-precision metric comparable to Quantum Volume while exhibiting greater sensitivity to interference effects. Consequently, PRCs enable a robust framework for assessing the computational reliability of NISQ hardware across platforms.

quant-ph↗

arXiv2025

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

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↗

arXiv2025

Extending Quantum Computing through Subspace, Embedding and Classical Molecular Dynamics Techniques

The advent of hybrid computing platforms consisting of quantum processing units integrated with conventional high-performance computing brings new opportunities for algorithm design. By strategically offloading select portions of the workload to classical hardware where tractable, we may broaden the applicability of quantum computation in the near term. In this perspective, we review techniques that facilitate the study of subdomains of chemical systems with quantum computers and present a proof-of-concept demonstration of quantum-selected configuration interaction deployed within a multiscale/multiphysics simulation workflow leveraging classical molecular dynamics, projection-based embedding and qubit subspace tools. This allows the technology to be utilised for simulating systems of real scientific and industrial interest, which not only brings true quantum utility closer to realisation but is also relevant as we look forward to the fault-tolerant regime.

quant-ph↗