Quantum Computing & Quantum Technology

Abstract: Quantum-Enhanced Artificial Intelligence for Affordable Precision Oncology and Early Cancer Detection

Cancer remains one of the most significant global healthcare challenges, with early diagnosis, treatment personalization, and accessibility of advanced medical technologies continuing to be critical priorities. Although Artificial Intelligence (AI) and Machine Learning (ML) have demonstrated remarkable potential in improving cancer detection, medical imaging analysis, biomarker identification, and therapeutic decision-making, the increasing complexity and volume of biomedical data present significant computational challenges. Quantum-Enhanced Artificial Intelligence (QAI), which integrates quantum computing principles with advanced AI methodologies, represents an emerging technological paradigm with the potential to accelerate healthcare innovation and enable more efficient, affordable, and personalized oncology solutions. This presentation explores the transformative potential of Quantum Machine Learning (QML) and quantum-enhanced AI approaches in precision oncology, with a focus on early cancer detection, cost-effective healthcare delivery, and improved clinical decision support. Quantum computing capabilities, including superposition, quantum parallelism, and entanglement, may provide new opportunities to analyze complex biological datasets that exceed the practical limitations of traditional computational approaches. By combining quantum algorithms with deep learning, data analytics, and computational biology, researchers are developing innovative frameworks for identifying hidden patterns within genomic, molecular, imaging, and clinical datasets. One of the most promising applications of quantum-enhanced AI is early cancer detection, where timely diagnosis can significantly improve patient outcomes and survival rates. Modern oncology increasingly relies on high-dimensional data sources, including medical imaging, liquid biopsy results, genomic profiles, and molecular signatures. Quantum-assisted machine learning models have the potential to enhance pattern recognition, improve predictive accuracy, and support the identification of early disease indicators. These advancements may contribute to the development of faster and more reliable diagnostic systems that can support clinicians in detecting cancer at earlier and more treatable stages. Furthermore, quantum-enhanced AI may play a significant role in reducing healthcare costs by enabling more efficient computational processes, optimizing diagnostic workflows, and supporting personalized treatment strategies. Affordable and scalable AI-driven healthcare solutions are particularly important for reducing disparities in access to advanced cancer screening technologies. By improving diagnostic efficiency and enabling targeted therapies, quantum-powered approaches may help minimize unnecessary procedures, optimize resource utilization, and support value-based healthcare models. This presentation also examines the integration of quantum AI with precision medicine and computational oncology. Quantum-enhanced algorithms may improve molecular simulation, accelerate drug discovery, identify potential therapeutic targets, and enable more personalized treatment recommendations based on individual patient characteristics. Such capabilities could transform cancer care from a generalized treatment approach toward a more precise, data-driven, and patient-centered model. Despite its significant promise, quantum-enhanced AI remains an evolving field with challenges related to quantum hardware limitations, algorithm development, data integration, scalability, and real-world clinical implementation. Hybrid quantum-classical approaches are currently being explored as practical pathways for combining existing AI infrastructure with emerging quantum technologies. In conclusion, Quantum-Enhanced Artificial Intelligence represents a promising frontier for advancing affordable precision oncology and early cancer detection. Through the convergence of quantum computing, artificial intelligence, and biomedical innovation, this emerging field has the potential to create more accessible, efficient, and impactful healthcare solutions while addressing some of the most complex challenges in modern cancer research and treatment.

Abstract: Comparing the Pre-Early and Early Stages of Quantum Computing Adoption: A Qualitative Study Based on Expert Interviews

Quantum computing is an innovative technology with the potential to fundamentally extend existing computational paradigms by leveraging the principles of quantum mechanics. However, there is limited systematic understanding of how adoption logic evolves over time and across stages. In particular, few studies have closely examined the diffusion of emerging technologies such as quantum computing by focusing on the transition from the pre-early stage to the early stage of adoption. Accordingly, this study aims to distinguish between the pre-early and early stages of quantum computing adoption and to identify differences in the key factors and perceptual structures that influence firms’ adoption intentions at each stage. Building on prior research that examined determinants of firms’ intentions to adopt quantum computing, this study conducts a longitudinal investigation by carrying out follow-up interviews and surveys with the same participants two years after the initial study. Grounded in innovation diffusion theory and prior literature on emerging technology adoption, the study analyzes how firms’ technological perceptions, expectation levels, and assessments of technological maturity—as well as organizational and environmental factors—exhibit both change and persistence over time. In addition, by examining the gap between initial adoption intentions and actual levels of adoption readiness, this study seeks to elucidate the evolving decision-making mechanisms underlying the adoption of quantum computing.

Abstract: Tars Cloud: An Agentic Framework for Automated Orchestration of Heterogeneous Quantum-Classical Workloads

It is a cloud-native, agent-driven orchestration framework designed to automate the execution and management of heterogeneous quantum–classical workloads in the Noisy Intermediate-Scale Quantum (NISQ) era. The framework addresses key limitations of existing quantum cloud platforms, including manual workload decomposition, static resource allocation, and provider-specific execution models. Tars Cloud integrates artificial intelligence–based agents to enable end-to-end automation across the quantum workload lifecycle, from code validation and resource estimation to backend selection, scheduling, execution monitoring, and result post-processing. The system incorporates automated static analysis to extract quantum kernels and workload characteristics from user-submitted programs, followed by bounded simulation to detect execution inconsistencies prior to deployment on real quantum hardware. Resource estimation and backend profiling are performed using multi-objective utility functions that account for qubit capacity, circuit depth, noise parameters, queue latency, execution cost, and fidelity constraints. An agent-based scheduling mechanism dynamically maps hybrid workloads to appropriate classical and quantum resources, while a plugin-based abstraction layer enables interoperability across gate- based and annealing paradigms. Execution monitoring agents support backend-aware error mitigation and structured result aggregation with full provenance metadata. Tars Cloud is designed with scalability, security, and usability as core principles, enabling provider-agnostic orchestration and reproducible execution workflows. The framework provides a practical engineering foundation for Quantum-as-a-Service platforms and demonstrates how agent- driven automation can lower the barrier to quantum experimentation while optimizing resource utilization in cloud-based quantum environments.

Abstract: Federated Learning for Secure and Efficient Network Slicing in 6G Edge-Cloud Continuum

Privacy risks and scalability limitations in centralized AI training pose significant challenges for optimizing next-generation 6G networks. To address these issues, this paper proposes a federated learning–based framework for secure and intelligent network slicing across the edge–cloud continuum, integrated with Software-Defined Networking (SDN) and Network Function Virtualization (NFV) for dynamic orchestration. The proposed approach enables distributed model training on slice-specific data without sharing raw information, supporting heterogeneous 6G service requirements such as ultra-reliable low-latency communications (URLLC) and massive machine-type communications (mMTC). Key challenges, including model aggregation under heterogeneous network conditions and robustness against poisoning attacks, are systematically addressed. Simulation results demonstrate improved slicing efficiency, reduced end-to-end latency, and enhanced privacy preservation, highlighting the suitability of the proposed framework for AI-native 6G deployments with massive device connectivity.