Abstract Current federal approaches to accelerating quantum adoption were designed prior to the broad implementation of modern technologies such as cloud computing and machine learning. The impact of increased expenditures in quantum computing research may be negated by slow adoption. A research-based approach that incorporates lessons learned from recent technology adoption challenges has the potential to dramatically improve quantum adoption rates.
Abstract Current federal approaches to accelerating quantum adoption were designed prior to the broad implementation of modern technologies such as cloud computing and machine learning. The impact of increased expenditures in quantum computing research may be negated by slow adoption. A research-based approach that incorporates lessons learned from recent technology adoption challenges has the potential to dramatically improve quantum adoption rates.
The U.S. Federal Government is currently engaged in multiple major modernization initiatives.Cloud computing is largely implemented across the federal government, but artificial intelligence (machine learning), cybersecurity, and quantum computing are all competing for resources and management attention.Artificial intelligence adoption is further along than quantum computing and can serve as a framework for predicting and preventing quantum adoption issues.This paper provides an analysis of the federal government's implementation history for the four major technology areas.It outlines an approach for leveraging artificial intelligence adoption in the federal government to help predict quantum adoption challenges, including the creation of a time series analysis to leverage the Federal Government's AI investment, progress, and challenges to help predict federal quantum adoption challenges.
The emergence of quantum computing represents a paradigm shift in the world of technology, promising unprecedented computational power to tackle complex problems that were once considered insurmountable. As quantum computing technologies continue to advance, organizations across various industries are eager to harness this potential. However, the transition to quantum computing is not a mere plug-and-play affair. It requires a systematic and thoughtful approach to seamlessly integrate quantum capabilities into existing systems, processes, and workflows.
Healthcare organizations have a high volume of sensitive data and traditional technologies have limited storage capacity and computational resources. The prospect of sharing healthcare data for machine learning is more arduous due to government regulations related to patient privacy. In recent years, federated learning has offered a solution to accelerate distributed machine learning addressing concerns related to data privacy and governance. Currently, the blend of quantum computing and machine learning has experienced significant attention from academic institutions and research communities. The ultimate objective of this work is to develop a federated quantum machine learning framework (FQML) to tackle the optimization, security, and privacy challenges in the healthcare industry for medical imaging tasks. In this work, we propose a new methodology based on federated quantum convolutional neural networks (QCNNs) with distributed training across edge devices. To demonstrate the feasibility of the proposed FQML framework, we performed extensive experiments on two benchmark medical datasets (Pneumonia MNIST, and CT kidney disease analysis), which are non-independently and non-identically partitioned among the healthcare institutions/clients. The proposed framework is validated and assessed via large-scale simulations. Based on our results, the quantum simulation experiments achieve performance levels on par with well-known classical CNN models, 86.3% accuracy on the pneumonia dataset and 92.8% on the CT-kidney dataset, while requiring fewer model parameters and consuming less data. Moreover, a client selection mechanism is proposed to reduce the computation overhead at each communication round, which effectively improves the convergence rate.
This study examines the transformative role of Quantum Artificial Intelligence (Quantum AI) in advancing computational intelligence through quantum-enhanced machine learning frameworks. Using a quantitative, descriptive, and analytical research design, primary data were collected from 400 respondents across academic, research, and industry domains via a structured questionnaire. The analysis applied mean, percentile, and rank-based techniques to evaluate key constructs, including transformational impact, adoption challenges, industry applications, and future potential. Findings indicate that Quantum AI enhances optimization efficiency, predictive analytics, and decision-making, outperforming conventional models. However, challenges such as hardware scalability, high implementation costs, and skill shortages limit widespread adoption. The study also highlights its impact across healthcare, finance, cybersecurity, and data analytics. Overall, Quantum AI demonstrates strong strategic potential for next-generation intelligent systems and competitive advantage.
Recently, there has been increasing awareness of the tremendous opportunities inherent in quantum computing (QC). Specifically, the speed and efficiency of QC will significantly impact the Internet of Things, cryptography, finance, and marketing. Accordingly, there has been increased QC research funding from national and regional governments and private firms. However, critical concerns regarding legal, political, and business-related policies germane to QC adoption exist. A few relevant studies are currently available, and those few focus heavily on the technical side of QC. Thus, this study offers a multidisciplinary review of QC, drawing on the expertise of scholars from a wide range of disciplines whose insights coalesce into a framework that simplifies the understanding of QC. This study offers a timely contribution to both practitioners and academia, as it explores use cases in business, addresses fundamental legal and political issues undermining QC’s adoption, and highlights several research gaps critical to advancing knowledge within the field.
Quantum computing is emerging as a revolutionary field with the potential to redefine modern technology. Unlike classical computers, which rely on binary computation, quantum computers leverage quantum bits (qubits) to perform complex calculations at unprecedented speeds. This paper explores the transformative impact of quantum computing across various technological domains, including cryptography, artificial intelligence, data processing, and optimization problems. In cryptography, quantum algorithms such as Shor’s algorithm pose challenges to traditional encryption methods, necessitating the development of quantum-resistant cryptographic techniques. In artificial intelligence and machine learning, quantum computing enhances pattern recognition and accelerates data analysis. Furthermore, industries such as pharmaceuticals, material science, and logistics benefit from quantum-driven advancements in molecular simulations, material design, and supply chain optimization. Despite its immense potential, challenges such as hardware limitations, error correction, and high implementation costs hinder widespread adoption. However, continuous research and advancements in quantum algorithms, superconducting qubits, and quantum networking are expected to overcome these barriers, paving the way for a new era of technological innovation.