Quantum computing in supercomputing centers requires robust tools to analyze calibration datasets, predict hardware performance, and optimize operational workflows. This paper presents a data-driven framework for processing calibration metrics. Our model is based on a real calibration quality metrics dataset from our in-house$\mathbf{2 0}$-qubit NISQ device and for more than 250 days. We apply detailed data analysis to uncover temporal patterns and cross-metric correlations. Using unsupervised clustering, we identify stable and noisy qubits. We also validate our model using GHZ state experiments. Our study provides health indicators as well as hardware-driven maintenance and recalibration recommendations, thus motivating the integration of relevant schedulers with HPCQC workflows.
Quantum computing in supercomputing centers requires robust tools to analyze calibration datasets, predict hardware performance, and optimize operational workflows. This paper presents a data-driven framework for processing calibration metrics. Our model is based on a real calibration quality metrics dataset from our in-house20-qubit NISQ device and for more than 250 days. We apply detailed data analysis to uncover temporal patterns and cross-metric correlations. Using unsupervised clustering, we identify stable and noisy qubits. We also validate our model using GHZ state experiments. Our study provides health indicators as well as hardware-driven maintenance and recalibration recommendations, thus motivating the integration of relevant schedulers with HPCQC workflows.
A tight integration of quantum accelerators with high-performance computers (HPC) necessitates relocating the often highly sensitive quantum devices from controlled laboratory environments to noisy data centers. Various factors impacting fidelity must be tracked and mitigated to ensure the performance of quantum computers in such environments. This paper describes a monitoring system utilizing Internet of Things (IoT) technology incorporating multiple sensors to gather and feed data into a data center-wide database. The data collected is then analyzed to identify correlations between fidelity and environmental factors. Using this data, we propose a novel, adaptive calibration mechanism in which a machine learning model predicts the optimal calibration timing for the quantum computer based on fidelity prediction with varying environmental conditions. In the larger scope, this research aims to enhance our understanding of fidelity-altering factors and develop strategies for maintaining optimal quantum computer performance in real-world, noisy data center environments.
Quantum computing is an emerging paradigm that will soon transform the face of several industries, such as health care. This paper focuses on applying quantum computing in optimizing analytics on healthcare data, especially amidst big data challenges in the digital age. A health system generates tremendous amounts of complex datasets requiring advanced tools to draw meaningful insights from such data. Traditionally, most of these methods fail to meet the demands for real-time processing, predictiveness, and data security. Taking leaps with extraordinary computing power from quantum computing, health analytics can be taken towards leaps of revolutionary precision in medicine, resource allocation, and early detection of diseases. This paper explains how quantum algorithms synergistically find applications in health data. Areas of applicability, technological constraints, and issues related to ethics form other emphases. This, in turn, provides a comprehensive literature review, a method for quantum-enhanced analytics, and an analysis of the results of simulated quantum experiments, which points toward this integration as transformative. Furthermore, an architectural framework for quantum-driven healthcare systems, as depicted in the study, is postulated. The results are illustrated, including figures and corresponding tables for descriptive performance benefits and challenges. Implications to clinical workflows, patient outcomes, and the larger health ecosystem are demonstrated in the discussion. The conclusion presents the limitations and avenues for further research focusing on interdisciplinarity with quantum computing for the best healthcare.
In this work, we aim to solve a practical use-case of unsupervised clustering which has applications in predictive maintenance in the energy operations sector using quantum computers. Using only cloud access to quantum computers, we complete a thorough performance analysis of what some current quantum computing systems are capable of for practical applications involving non-trivial mid-to-high dimensional datasets. We first benchmark how well distance estimation can be performed using two different metrics based on the swap-test, using angle and amplitude data embedding. Next, for the clustering performance analysis, we generate sets of synthetic data with varying cluster variance and compare simulation to physical hardware results using the two metrics. From the results of this performance analysis, we propose a general, competitive, and parallelized version of quantumk-means clustering to avoid some pitfalls discovered due to noisy hardware and apply the approach to a real energy grid clustering scenario. Using real-world German electricity grid data, we show that the new approach improves the balanced accuracy of the standard quantumk-means clustering by 67.8 % with respect to the labeling of the classical algorithm.
Quantum computing is emerging as a transformative technology with significant potential to revolutionize healthcare through accelerated data analysis, medical imaging, drug discovery, and genomic research. Despite these opportunities, practical deployment in clinical environments remains challenging due to hardware limitations of noisy intermediate-scale quantum (NISQ) devices, data encoding complexities, error rates, and integration difficulties with existing electronic health record systems. Moreover, the absence of standardized datasets and reproducible evaluation benchmarks limits the ability to compare quantum models with classical baselines in healthcare applications. This paper identifies specific research gaps and proposing a hybrid quantum-classical framework tailored to medical imaging and genomic workloads. Proof-of-concept experiments are conducted using publicly available datasets to evaluate performance trade-offs in accuracy, runtime, and computational resource requirements. The results demonstrate both the promise and current limitations of quantum approaches, highlighting that while immediate advantages are modest, carefully designed hybrid architectures and error-mitigation strategies can provide practical pathways toward clinical adoption. This paper proposes a structured roadmap with methodological guidelines to accelerate the safe and effective integration of quantum computing into healthcare workflows.
As quantum computers mature, they migrate from laboratory environments to HPC centers. This movement enables large-scale deployments, greater access to the technology, and deep integration into HPC in the form of quantum acceleration. In laboratory environments, specialists directly control the systems' environments and operations at any time with hands-on access, while HPC centers require remote and autonomous operations with minimal physical contact. The requirement for automation of the calibration process needed by all current quantum systems relies on maximizing their coherence times and fidelities and, with that, their best performance. It is, therefore, of great significance to establish a standardized and automatic calibration process alongside unified evaluation standards for quantum computing performance to evaluate the success of the calibration and operation of the system. In this work, we characterize our in-house superconducting quantum computer, establish an automatic calibration process, and evaluate its performance through quantum volume and an application-specific algorithm. We also analyze readout errors and improve the readout fidelity, leaning on error mitigation.
Quantum computers are moving into HPC centers, and the main challenge is now integration rather than pure hardware access. Many current software paths still depend on vendor-specific adapter chains between user SDKs, schedulers, and backend APIs. This pattern makes operations more complex than necessary and slows the transition from pilots to production workflows. We present a practical integration path centered on the Quantum Device Management Interface (QDMI). Using IQM superconducting systems as a hardware case study, we implement an IQM-backed QDMI layer and connect it to two software layers that HPC centers working with quantum computers already care about: Slurm-based job execution and Qiskit-facing user workflows. The implementation is publicly available at https://github.com/iqm-finland/QDMI-on-IQM. The key message is simple: integrating quantum hardware into HPC does not have to be a bespoke engineering effort for each backend. Once the software-hardware boundary is standardized, large parts of the stack become reusable across providers and deployment styles. Our results do not claim that standardization eliminates all HPCQC challenges. They show that this specific boundary can already be standardized today in a way that is practical for users, operators, and vendors.