Titlebook: Quantum Computing; Circuits, Systems, A Himanshu Thapliyal,Travis Humble Book 2024 The Editor(s) (if applicable) and The Author(s), under e

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Machine Learning Reliability Assessment from Application to Pulse Level,hese quantum devices. One of the primary challenges in current and near-term quantum computers is the noise in the quantum hardware. To unlock the power of quantum computers noise should be suppressed. Error mitigation approaches at the software level play a major role in reducing errors in quantum

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Queuing Theory Models for (Fault-Tolerant) Quantum Circuits: Analysis and Optimization,nsidered being the most resource-efficient method to implement surface code computations. We discuss how to apply queuing theory models for the efficient compilation of lattice surgery quantum circuits. We use queuing theory to optimize the footprint of quantum addition circuits and the depths of qu

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Quantum Annealing for Real-World Machine Learning Applications,trategy is quantum annealing, which is an emerging computing paradigm that has shown potential in optimizing the training of a machine learning model. The implementation of a physical quantum annealer has been realized by D-Wave systems and is available to the research community for experiments. Rec

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ions of quantum computing.Includes resource consumption esti.This book provides readers with the current state-of-the-art research and technology on quantum computing. The authors provide design paradigms of quantum computing. Topics covered include multi-programming mechanisms on near-term quantum

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Machine Learning Reliability Assessment from Application to Pulse Level,is chapter, we review our recent works in machine learning for quantum circuit reliability assessment and extend our analysis to the pulse level. Furthermore, we provide both qualitative and quantitative comparisons across the ML models for different design abstractions.

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Structure-Aware Minor-Embedding for Machine Learning in Quantum Annealing Processors,ed without losing overall functionality. These methods for model “pruning” can provide better resource utilization because the reduction of qubit chain length have a direct impact on the quality of samples obtained from the QAP.

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Software for Massively Parallel Quantum Computing,or hybrid quantum workloads. Our software provides the capability to distribute quantum workloads across multiple quantum accelerators hosted by nodes of a locally-networked cluster, via the industry-standard MPI (Message Passing Interface) protocol, or to distribute workloads across a large number of cloud-hosted quantum accelerators.