University of Kwazulu-Natal

Exploring The Application Of Quantum Technologies To Industrial And

Bonisiwe Shabane
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exploring the application of quantum technologies to industrial and

Recent advancements in quantum computing are leading to an era of practical utility, enabling the tackling of increasingly complex problems. The goal of this era is to leverage quantum computing to solve real-world problems in fields such as machine learning, optimization, and material simulation, using revolutionary quantum methods and machines. All this progress has been achieved even while being immersed in the noisy intermediate-scale quantum era, characterized by the current devices’ inability to process medium-scale complex problems efficiently. Consequently, there has been a surge of interest in quantum algorithms in various fields. Multiple factors have played a role in this extraordinary development, with three being particularly noteworthy: (i) the development of larger devices with enhanced interconnections between their constituent qubits, (ii) the development of specialized frameworks,... In this context, this manuscript presents and overviews some recent contributions within this paradigm, showcasing the potential of quantum computing to emerge as a significant research catalyst in the fields of machine learning and...

Avoid common mistakes on your manuscript. Quantum computing (QC) marks a groundbreaking advancement in computational technology leveraging principles from quantum physics to handle data in entirely new manners [1]. By exploiting quantum effects such as entanglement and superposition, purely quantum or hybrid algorithms are anticipated to offer significant improvements in speed and accuracy for system modeling and solving intricate problems. Despite notable progress, quantum devices are still in their early stages compared to classical systems. Presently, they face difficulties related to the limited number of qubits and their instability. Problems like noise, information loss, and decoherence, particularly without error correction, negatively impact their performance.

Furthermore, issues such as gate noise and quantum gate fidelity hinder advancements. Even hybrid algorithms have drawbacks; for instance, the physical separation of quantum and classical hardware introduces latency when they exchange information [2]. Consequently, we are now in the noisy intermediate-scale quantum (NISQ, [3]) era, marked by the inefficiency of current devices in addressing complex problems. Despite these challenges, there has been a growing body of research in recent years focusing on addressing real-world problems using QC. The increasing volume of publications highlights the growing interest of the community in exploring QC applications. Several factors have contributed to this intriguing development:

1]\orgnameTECNALIA, Basque Research and Technology Alliance (BRTA), \orgaddress\streetGeldo Auzoa, 700 Building, \cityDerio, \postcode48160, \countrySpain Recent advancements in quantum computing are leading to an era of practical utility, enabling the tackling of increasingly complex problems. The goal of this era is to leverage quantum computing to solve real-world problems in fields such as machine learning, optimization, and material simulation, using revolutionary quantum methods and machines. All this progress has been achieved even while being immersed in the noisy intermediate-scale quantum era, characterized by the current devices’ inability to process medium-scale complex problems efficiently. Consequently, there has been a surge of interest in quantum algorithms in various fields. Multiple factors have played a role in this extraordinary development, with three being particularly noteworthy: i) the development of larger devices with enhanced interconnections between their constituent qubits, ii) the development of specialized frameworks,...

In this context, this manuscript presents and overviews some recent contributions within this paradigm, showcasing the potential of quantum computing to emerge as a significant research catalyst in the fields of machine learning and... Quantum Computing (QC) marks a groundbreaking advancement in computational technology leveraging principles from quantum physics to handle data in entirely new manners [1]. By exploiting quantum effects such as entanglement and superposition, purely quantum or hybrid algorithms are anticipated to offer significant improvements in speed and accuracy for system modeling and solving intricate problems. Despite notable progress, quantum devices are still in their early stages compared to classical systems. Presently, they face difficulties related to the limited number of qubits and their instability. Problems like noise, information loss, and decoherence, particularly without error correction, negatively impact their performance.

Furthermore, issues such as gate noise and quantum gate fidelity hinder advancements. Even hybrid algorithms have drawbacks; for instance, the physical separation of quantum and classical hardware introduces latency when they exchange information [2]. Consequently, we are now in the noisy intermediate-scale quantum (NISQ, [3]) era, marked by the inefficiency of current devices in addressing complex problems. Despite these challenges, there has been a growing body of research in recent years focusing on addressing real-world problems using QC. The increasing volume of publications highlights the growing interest of the community in exploring QC applications. Several factors have contributed to this intriguing development:

Report by James Andrew Lewis and Georgia Wood Quantum computation (QC) holds out tremendous promise for efficiently solving some of the most difficult problems in computational science, such as integer factorization, discrete logarithms, and quantum modeling that are intractable on any present... New concepts for QC implementations, algorithms, and advances in the theoretical understanding of the physics requirements for QC appear almost weekly in the scientific literature. This rapidly evolving field is one of the most active research areas of modern science, attracting substantial funding that supports research groups at internationally leading academic institutions, National Laboratories and major industrial research centers. Well-organized programs are underway in the United States, the European Union and its member nations, Australia, and in other major industrial nations. Start-up quantum information companies are already in operation.

A diverse range of experimental approaches from a variety of scientific disciplines are pursuing different routes to meet the fundamental quantum mechanical challenges involved. Yet experimental achievements in QC, although of unprecedented complexity in basic quantum physics, are only at the proof-of-principle stage in terms of their abilities to perform QC tasks. It will be necessary to develop significantly more complex quantum-information processing (QIP) capabilities before quantum computer science issues can begin to be experimentally studied. To realize this potential will require the engineering and control of quantummechanical systems on a scale far beyond anything yet achieved in any physics laboratory. This required control runs counter to the tendency of the essential quantum properties of quantum systems to degrade with time ("decoherence"). Yet, it is known that it should be possible to reach the "quantum computer science test-bed regime" — if challenging requirements for the precision of elementary quantum operations and physical scalability can be met.

Although a considerable gap exists between these requirements and any of the experimental implementations today, this gap continues to close. Quantum computing is a global race to conceive and create the ultimate computing machine. If fully-functional quantum computers can be built, and there is still a question that they can, they will be able to rapidly factor extremely large numbers, making them extremely useful for solving certain large... A functional quantum computer would put much of the world's past and present encrypted information at risk of being quickly deciphered. Los Alamos researchers were among the first to make tangible advances in quantum computation. In 1998, Los Alamos scientists used nuclear magnetic resonance techniques to create a prototype liquid-based quantum computer within trichloroethylene molecules.

They went on to build a slightly larger device in 2000, but the technology is far from the desired end state. Please note: Providing information about references and citations is only possible thanks to to the open metadata APIs provided by crossref.org and opencitations.net. If citation data of your publications is not openly available yet, then please consider asking your publisher to release your citation data to the public. For more information please see the Initiative for Open Citations (I4OC). Please also note that there is no way of submitting missing references or citation data directly to dblp. Please also note that this feature is work in progress and that it is still far from being perfect.

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Avoid common mistakes on your manuscript. Quantum computing (QC) marks a groundbreaking advancement in computational technology leveraging principles from quantum physics to handle data in entirely new manners [1]. By exploiting quantum effects such as entanglement and superposition, purely quantum or hybrid algorithms are anticipated to offer significant improvements in speed and accuracy for sys...

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1]\orgnameTECNALIA, Basque Research And Technology Alliance (BRTA), \orgaddress\streetGeldo Auzoa, 700

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In this context, this manuscript presents and overviews some recent contributions within this paradigm, showcasing the potential of quantum computing to emerge as a significant research catalyst in the fields of machine learning and... Quantum Computing (QC) marks a groundbreaking advancement in computational technology leveraging principles from quantum physics to handle data in entirely new mann...