Future Generation in Computing Technology

Koichi Furukawa

Pravin Ghosekar

The quantum computing paradigm is revolutionizing computation with the use of quantum mechanical principles such as superposition and entanglement. The machine learning (ML) method has also emerged as a cornerstone of modern artificial intelligence. Quantum Machine Learning (QML) combines quantum computing with machine learning to achieve unprecedented computational speed and efficiency. Quantum computing, machine learning, and its potential to transform computing technologies in the future are explored in this paper. Furthermore, it discusses key application areas, advantages, challenges, and future directions for research. QML can significantly improve data processing, optimization, and predictive modeling, paving the way for the next generation of intelligent systems, according to the study.

Nagabhushana Narasappa, Kappa Raghu Ram Mohan Reddy, Vandna Kumari +3 more

This exhaustive quality-research paper covers the field of emerging materials and their intrinsic properties and including how they can be used in the future in quantum technologies. The arrival of quantum mechanics is one of the most important scientific discoveries of the 20th century, with the promise of new computer generations, enhanced information transfer and precision measurement. However, the development of future quantum technology is highly dependent on the discovery of new materials that enable us to control quantum phenomena. This article carefully considers a range of advanced materials that have emerged recently, such as novel superconductors, topological insulators and graphene derivatives, to explore the unique quantum properties of these materials and how they can be exploited in quantum devices. By using advanced experimental techniques, including angle-resolved photoemission spectroscopy (ARPES) and scanning tunneling microscopy (STM), advanced instruments that allow us to probe electronic structures, spin textures and topological properties with atomic electron and spin resolution, the areas with most interesting quantum properties can be discovered, key for the development of quantum technologies. This paper details understanding of quantum coherence and decoherence mechanisms of the advanced materials. Quantum coherence is one of the most important properties for using the quantum features of materials for information processing. Through quantum states and entanglement, the quantum coherence can be manipulated and enhanced, which is also intertwined with the environment. Better understanding of quantum coherence and the mechanisms of decoherence can help to improve future generations of quantum technologies. The results reported here help to explain the mechanisms leading to design and performance of new materials for the quantum technology revolution. Studying advanced materials not only advance the understanding of quantum nature but will also power future quantum computing, quantum communication and quantum sensing devices. Current research can take advantage of all aspects of these new materials and advance the boundaries of science, engineering, computing, information exchange and sensing.

Jim Stikeleather

Jiří Guth Jarkovský, Jayshankar Nath, Björn Pötter +1 more

Earth Observation (EO) satellites generate a growing amount of data every year and highlight the need for scalable algorithms and adequate computational resources. However, the question about how to leverage quantum computing for enhancing the required computational steps is still largely unanswered. The QC4EO study proposes insightful answers and potential solutions to this question. The study has been conducted in the period March 2023 – October 2023 by a consortium led by Forschungszentrum Jülich, with Thales Alenia Space Italy/France, INFN and IQM, and supported by the European Space Agency. The scope of the study covers 12 use cases and a 15-year timeframe, evaluating a potential practical advantage of quantum computing in specific computational tasks and the availability of the required hardware in the near future. This machine roadmap assessment study (WP3) provides a comprehensive overview of existing quantum computing (QC) technologies and assesses their maturity. For each identified technology, we study the current state of the art, assess their risks, and estimate their future development. Based on these assessments, we provide our judgement on current and future quantum technology readiness levels (QTRL) of these platforms and their suitability to different applications in earth observations.

Michelle Yvonne Simmons