Novel Approaches for Quantum Computing and Quantum Information Processing
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Quantum Science and Technology".
Deadline for manuscript submissions: closed (31 December 2024) | Viewed by 723
Special Issue Editor
Interests: quantum computing; quantum technologies; nanoelectronics; optoelectronics
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The fields of quantum computing and quantum information processing have thoroughly advanced during the last decade due to potential applications in many scientific fields ranging from computer science and communications to biology and AI. Recently developed quantum algorithms are capable of performing better than their classical counterparts; although this was demonstrated decades ago via the work of Deutsch and Shor, it has recently received new attention after the development of algorithms that can perform pattern recognition, machine learning tasks, and computations that involve core aspects of a physical system. These methodologies offer the potential to perform very complex, currently intractable tasks, ranging from computing solutions to the protein folding problem.
As we are currently in the NISQ-era of quantum computation, the development of techniques for encoding information using the minimal number of qubits, the design of hardware-efficient quantum circuits, and the adoption of appropriate noise suppression and mitigation techniques are very important for designing algorithms that can have practical applications in the near future.
In this light, this Special Issue aims to publish work that exhibits novel work regarding both new quantum algorithms and solutions based on new techniques, such as transpilation and custom noise handling, which allow for NISQ-efficient implementations of existing quantum algorithms.
Prof. Dr. Nikos Konofaos
Guest Editor
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Keywords
- quantum computation
- hardware-efficient quantum circuits
- quantum algorithms
- quantum pattern recognition
- quantum noise suppression
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