IEEE International Conference on Quantum Computing and Engineering (QCE)
Quantum Computer Metrics and HPC Center Environmental Sensor Data Analysis Towards Fidelity Prediction
Hossam Ahmed, Xiaolong Deng, Helmut Heller, Carla Guillén, Asim Zulfiqar, Martin Ruefnacht, Amit Jamadagni, Matthew Tovey, Martin Schulz, Laura Brandon Schulz
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.
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 systems have traditionally operated within highly controlled laboratory environments to minimize internal noise and maximize stability. However, when these systems are integrated into High Performance Computing (HPC) environments and setup for continuous operation, they encounter significantly higher levels of external noise and instability coupled with higher demands. This paper introduces a comprehensive framework to counter the challenges of maintaining the stability of operational quantum computers in such noisy HPC settings. We propose a novel system architecture that monitors and manages the environmental conditions of quantum computing systems using a broad set of sensors, enhancing their robustness and reliability. Our architecture is open and can be extended to other setups. It can serve as a blueprint for other sites as it spans quantum technologies and offers broad coverage. Ultimately, this data will be essential to facilitate advancements in machine learning to drive real-time error mitigation.
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 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.
Revolutionize your IoT infrastructure with this guide to mastering quantum-enhanced machine learning algorithms and theoretical frameworks that are shattering the boundaries of classical computing to deliver unprecedented network performance and security. In a world increasingly reliant on interconnected devices and data-driven insights, the limitations of classical computing become ever more apparent. The convergence of quantum computing, machine learning, and the Internet of Things (IoT) heralds a new era of technological advancement, one where the boundaries of computational possibility are continually redefined. This book offers an in-depth examination of how quantum algorithms are utilized to improve the performance, security, and efficiency of IoT devices and networks. It connects theoretical concepts with practical applications, providing a comprehensive look at fundamental principles and advanced techniques in this rapidly growing field. Using case studies and real-world insights, this book gives readers the latest developments in quantum machine learning, artificial intelligence, and the smart Internet of Things, and their potential to create an accessible pathway to the future. Readers will find the volume: Demonstrates how to seamlessly integrate quantum computing and machine learning for next-gen IoT solutions; Explores the emerging field of quantum machine learning and its various applications for the AI-driven Internet of Things; Provides real-world examples and case studies demonstrating the power of quantum machine learning in smart IoT environments; Comprehensively covers a wide range of topics. Audience Researchers and engineers in machine learning, quantum computing, data science, the Internet of Things.
Adaptive quantum computing control is important for the stability and efficiency of quantum systems, especially in the presence of dynamic environmental noise and hardware fluctuations. We present a unified strategy for machine learning (ML) techniques to improve adaptive quantum control in two main directions: optimization speed and out-of-sample error. Here, we propose a hybrid learning method combining reinforcement learning (RL), deep neural networks (DNNs), and quantum-inspired models to actively and adaptively update quantum gate parameters and error mitigation strategies on the fly. Embedding of quantum noise models directly into the learning allows the system to predict and correct for decoherence, gate errors and measurement inaccuracy before they affect computational fidelity. Using extensive simulations as well as experimental validation on noisy intermediate-scale quantum (NISQ) processors, our improved ML model achieves a substantially higher fidelity and robustness compared to standard model-free and static control approaches. We introduce a novel feedback-loop mechanism based on online learning algorithms enabling the system to adjust for changing noise profiles and hardware drift without retraining. The optimization part uses metaheuristic methods to quickly converge to control parameters that perform well, and the error prevention mechanism features anomaly detection and prediction correction layers to minimize the effects of cumulative decoherence. The results demonstrate that our adaptive control framework can be used to not only speed up the convergence in training quantum algorithms, but also sustain the performance of gates against error at a variety of error rates with the reduction of the real-time impact of the error up to 35 %. This work highlights the promise of machine learningaugmented quantum control for improving reliability and scalability in quantum computing and serves as a stepping stone for future incorporation with fault-tolerant designs.
Use of AI/ML is associated with more and more computing power, which often necessitates the application to guzzle energy and take time to train. The paper presents a Quantum-Enhanced High-Performance Computing (Q-HPC) system that will combine traditional HPC units with quantum-assisted optimization to enhance the model training in addition to the predictive accuracy and energy efficiency. The framework that could be used to work with such large volumes of data is multi- GPU/CPU parallelization, and the optimization of parameters and hyperparameters can be implemented with the help of quantum-inspired algorithms. This will lead to a hybrid computation balancing dynamically the classical and quantum computations. Q-HPC was experimented with multiple AI/ML model types, such as convolutional networks, transformer models, graph neural networks and reinforcement learning agents, in which cases it was observed to traverse to the solution faster, more accurately and used less energy than the traditional HPC. It can also be said that the framework is dynamically adaptable and sustainable, i.e. it could be deployed to a massive diversifying range of tasks of AI/ML. The suggested model is a combination of the performance and scalability of the classical HPC with the optimization performance of quantum computing in order to create a new and valuable approach to the next generation AI. It deals with the issues of performance and environmental concerns of high-performance computing.