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With the COVID-19 pandemic, our globalised world has engaged faster in digitalisation, and so has healthcare which is becoming one of the main developing sectors for digitalised services. CERN and Be-ys Research have initiated in 2018 the Living Lab project, which aims at developing a network of labs to investigate GDPR-compliant healthcare data systems. In QUANTUMACY we have extended the concept to work on the design and implementation of a Proof-of-Concept of secure data architecture integrating QKD-based implementations of blockchain (for secure transactions), homomorphic encryption (for secure storage and analysis), SMPC (for transfers and analysis), and channel encryption as a means of providing core services requiring key issuance and distribution, and to shape a new approach to making data available and processed under the so-called principle of polymorphic privacy. The project provides a few test use cases of the PoC on simple but realistic conditions in collaboration with healthcare research institutes in the Living Labs network, exploiting existing expertise developed as part of equivalent non-quantum implementations that provide a baseline for comparison.

The recent increased interest in quantum computing has led to the release of many of opensource quantum frameworks by commercial vendors and researchers alike. The implementation details of each framework could result in very different performances when executing some quantum routine, and thus it is important to be aware of the relative performance advantage a framework may have over the other, such that the proper tool is chosen before undergoing a new project or research. The Automated Benchmarking of Algorithms for Quantum Systems (ABAQUS) project targets this exact issue as part of Quantum Technology Initiative (QTI) at CERN, and a prototype has been developed last year. While the developed prototype served well as a proof-of-concept, it had two main issues. First, it was not intuitively extensible. One of the goals of ABAQUS is for it to be community driven, and therefore, the design had to be more intuitive, especially to people coming from non-CS backgrounds. Secondly, the underlying low-level design was not scalable, which does not fit with the Distributed Quantum Computing paradigm, that is also part of the QTI. Accordingly, the prototype has been modified, getting closer to both goals, by making the different components more modular, and adapting the system such that it can be scaled with the use of container technologies and container orchestration tools. This makes the prototype usable in different configurations: locally, in a virtualized environment, or a distributed cluster.

This document outline the plan to produce a public cloud strategy for the IT department

Machine learning enjoys widespread success in High Energy Physics (HEP) analyses at LHC. However the ambitious HL-LHC program will require much more computing resources in the next two decades. Quantum computing may offer speed-up for HEP physics analyses at HL-LHC, and can be a new computational paradigm for big data analyses in High Energy Physics. We have successfully employed three methods (1) Variational Quantum Classifier (VQC) method, (2) Quantum Support Vector Machine Kernel (QSVM-kernel) method and (3) Quantum Neural Network (QNN) method for two LHC flagship analyses: ttH (Higgs production in association with two top quarks) and H->mumu (Higgs decay to two muons, the second generation fermions). We shall address the progressive improvements in performance from method (1) to method (3). We will present our experiences and results of a study on LHC High Energy Physics data analyses with IBM Quantum Simulator and Quantum Hardware (using IBM Qiskit framework), Google Quantum Simulator (using Google Cirq framework), and Amazon Quantum Simulator (using Amazon Braket cloud service). The work is in the context of a Qubit platform (a gate-model quantum computer). Taking into account the present limitation of hardware access, different quantum machine learning methods are studied on simulators and the results are compared with classical machine learning methods (BDT, classical Support Vector Machine and classical Neural Network). Furthermore, we do apply quantum machine learning on IBM quantum hardware to compare performance between quantum simulator and quantum hardware. The work is performed by an international and interdisciplinary collaboration with the Department of Physics and Department of Computer Sciences of University of Wisconsin, CERN Quantum Technology Initiative, IBM Research Zurich, IBM T.J. Watson Research Center, Fermilab Quantum Institute, BNL Computational Science Initiative, State University of New York at Stony Brook, and Quantum Computing and AI Research of Amazon Web Services. This work pioneers a close collaboration of academic institutions with industrial corporations in the High Energy Physics analyses effort. Though the size of event samples in future HL-LHC physics and the limited number of qubits pose some challenges to the Quantum Machine learning studies for High Energy Physics, more advanced quantum computers with larger number of qubits, reduced noise and improved running time (as envisioned by IBM and Google) may outperform classical machine learning in both classification power and in speed. Although the era of efficient quantum computing may still be years away, we have made promising progress and obtained preliminary results in applying quantum machine learning to High Energy Physics. A PROOF OF PRINCIPLE.

This is the intermediate (PM9) project report of the QUNATUMACY Project. It describes the state of the project activity and the planned actions for the rest of the project duration.

Quantum technologies have the potential to revolutionise science and society as early as the next five to ten years but require resources that are not mainstream today. CERN can be at the forefront of this revolution, setting up collaborations within the Member States, as well as international initiatives. Through such endeavours, we can work to develop new computing, detectors, and communications systems, in addition to advancing knowledge of quantum systems and fundamental physics. The Quantum Technology Initiative is based on more than two years of pilot collaborations and investigations — primarily in quantum computing — related to possible applications that are core to activities in high-energy physics (HEP). Over the past few years, knowledge and technologies have evolved considerably; dedicated support for research and development in quantum technologies has become part of national and international research agendas. The time has come for CERN to engage more formally with such activities. Thanks to its role in HEP and more broadly in European and international scientific research, CERN has compelling requirements for its future instruments and infrastructures, which represent very valuable application use cases. This puts CERN in a unique position to catalyse interests and investigations to act as a hub for innovation and knowledge creation and sharing, and to have a long-lasting impact on applying quantum technologies to high-energy physics (HEP) and beyond. The CERN Quantum Technology Initiative has defined a medium- and long-term roadmap and research programme in collaboration with the HEP and quantum-technology research communities. Together, we will establish joint research, educational and training activities, set up the supporting resource infrastructure, and provide dedicated mechanisms for exchange of both knowledge and technology.

Overview of the QUANTUMACY project presented at the2021 Quantum Security Technology Global Cooperation and Industry Revitalization Strategy Conference in Seoul, South Korea (https://www.youtube.com/watch?v=WMtK7geHmmY)&nbsp

QUANTUMACY is a project funded through an open call of the EC-funded openQKD project. QUANTUMACY ambition is to support the move towards confidential computing and informational self-determination by investigating the integrationof relevant technologies into core QKD-enabled services requiring key issuance and distribution, and thus lead a new approach to making data available and processed under the so-called principle of polymorphic privacy. To do so, QUANTUMACY has deployed a small-scale, proof-of-concept platform, where encryption techniques, such as FHE, ML/DL models and distributed computing frameworsk, such as openFL (Federated Learning)are combined and extended to use a openQKD infrastructure across the different layers of the platform and test on concrete healthcare use-cases can be performed. The initial implementation is based on simulated QKD, but tests with real QKD links are planned by the end of the project in 2022. This document is the first report on the definition of the services, the use cases and the deployment resources at PM 2 (April 2021).

CERN openlab and Intel have partnered to organize the 2017 edition of the Intel Code Modernization Challenges. This presentation highlights CERN and CERN openlab contributions to innovation and technology to enable advanced scientific research and in particular the programmes and impact on education.

Throughout its 16-year history, CERN openlab has worked to develop and test new ICT technologies and techniques that help to make the ground-breaking physics discoveries at CERN possible. CERN openlab runs in three-year phases, with around 20 projects — addressing a wide range of IT topics — being run in its current, fifth phase. With CERN openlab’s sixth three-year phase set to begin at the start of 2018, work has been carried out throughout the first half of 2017 to identify key areas for future collaboration. A series of workshops and discussions was held to discuss the ICT challenges faced by the LHC research community — and other ‘big science’ projects over the coming years. This white paper is the culmination of these investigations, and sets out specific challenges that are ripe for tackling through collaborative R&D projects.

CERN openlab works with industry to tackle the ICT challenges of the high-luminosity LHC.

p>Presented at MEDAMI 2016/p> p>The transformations that have taken place in Information and Communication Technology in the past 20 years have given rise to a new form of scientific research paradigm where data-intensive, large-scale projects combine experiment, theory and computing to address fundamental questions about ourselves and our universe. The large-scale computing and data analysis infrastructure set up by the High Energy Physics community to support the research of the LHC Experiments at CERN and in the hundreds of collaborating facilities worldwide is one of the foremost examples of this paradigm./p> p>Today the HEP community is not anymore the only place where increasingly large amounts of data are produced. Biomedical and healthcare research and practice could benefit from a broader use of big data analysis and simulation platforms. However, the biomedical domain requires a new focus on careful governance and use of data and information in the respect of the social and human value of such data and the design and deployment of collaborative frameworks where medical research, clinical practice and modern information technologies can constructively interact with each other to deliver personalized care./p> p>This talk briefly describes the state of the art of large-scale data analytics platforms as used in the HEP community and the ongoing work to adapt and extend such platforms for the benefit of medical applications./p>

Overview presentation of the CERN and LHC Experiments computing and data infrastructure and the technology evolution strategies being currently evaluated. Presented at the HPC & Big Data Conference, London on 4 Feb 2016

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Scientific Research is one of the strongest drivers behind innovation, but innovation cannot be fully exploited without providing a fertile ground for ideas to develop and mature. What can be done at CERN to maximize the human and technological assets that naturally gather together to work on the next frontiers of Physics research. The Innovation and Entrepreneurship Day is the first event co-organized by CERN openlab, the CERN KT group, IdeaSquare and Intel, a CERN openla industrial partner, to present ongoing activities, share expertise and collect ideas.

The growing availability of large quantities of data from medical devices, laboratory notes, digital simulations, doctors' notes, images, and even social networks are opening new opportunities for medical research and clinical practice. A proper understanding of data, tools and platforms and of how they related to the objective of the research are a necessary first step. The availability of platforms with clear separation of roles, ownership and responsibilities plays a fundamental role in enabling efficient use of data at large scales. What is the state of the art and the possible applications? What can be learned from existing activities in high energy physics?

A high-leveloverview of the relationship between science and technology at CERN and the impact of technology on research with a focus on ICT technologies. Presented as a 12-minute "power-talk" at CIOCity 2015, Brussels

Keynote presentation at the HEPTech Conference on Big Data, Budapest, 30 March 2015. A personal definition of Big Data, its technological challenges and impact on society.

This whitepaper describes the major IT challenges in scientific research at CERN and several other European and international research laboratories and projects. Each challenge is exemplified through a set of concrete use cases drawn from the requirements of large-scale scientific programs. The paper is based on contributions from many researchers and IT experts of the participating laboratories and also input from the existing CERN openlab industrial sponsors. The views expressed in this document are those of the individual contributors and do not necessarily reflect the view of their organisations and/or affiliates.