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Quantum Software Engineering Supremacy in Intelligent Robotics

V. Korenkov, Andrey G. Reshetnikov, Sergey Victorovich Ulyanov

Published Oct 27, 2020• 5 citations
Publisher

Abstract

A new approach to a circuit implementation design of quantum algorithmic gates for the realization of quantum massive parallel computing presented. The main attention focused on the development of design method of fast quantum algorithm operators as superposition, entanglement and interference which are in general time-consuming operations due to the number of products that have to be performed. SW&HW support toolkit of supercomputing accelerator of quantum algorithm simulation on classical computer described. The method for performing Grover's interference without product operations introduced. New quantum genetic and quantum fuzzy inference algorithm gate design for intelligent robotic control considered. Quantum software engineering supremacy on Benchmarks demonstrated.

DOI
10.1109/monetec49726.2020.9258000

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A new approach to a circuit implementation design of quantum algorithmic gates for the realization of quantum massive parallel computing presented. The main attention focused on the development of design method of fast quantum algorithm operators as superposition, entanglement and interference which are in general time-consuming operations due to the number of products that have to be performed. SW&HW support toolkit of supercomputing accelerator of quantum algorithm simulation on classical computer described. The method for performing Grover's interference without product operations introduced. New quantum genetic and quantum fuzzy inference algorithm gate design for intelligent robotic control considered. Quantum software engineering supremacy on Benchmarks demonstrated.
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A new approach for implementing quantum massive parallel computations is presented, using methods of circuit implementation of quantum algorithmic gates. Methods for designing fast quantum operators such as superposition, entanglement, andinterference are considered. The presented methodsallow you to reduce the number of actions that must be performed. The implementation is presented as a support tool for SW&HW supercomputer accelerator for modeling quantum algorithms. In particular, a newquantum-genetic and quantum-fuzzy inference algorithm for intelligent robotic control has been implemented. Also, a new method for performing Grover's inference without operations with the productis presented.
Principles and methodologies of quantum algorithmic gates design for master course and PhD students in computer science, control engineering and intelligent robotics described. The possibilities of quantum algorithmic gates simulation on classical computers discussed. Applications of quantum gate of nanotechnology in intelligent quantum control introduced. Anew approach to a circuit implementation design of quantum algorithm gates for fast quantum massive parallel computing presented. The main attention focused on the development of design method of fast quantum algorithm operators as superposition, entanglement and interference, which are in general time-consuming operations due to the number of products that have performed. SW & HW support sophisticated smart toolkit of supercomputing accelerator of quantum algorithm simulation on small quantum programmable computer algorithm gate (that can program in SW to implement arbitrary quantum algorithms by executing any sequence of universal quantum logic gates) described. As example, the method for performing Grover’s interference operator without product operations introduced. The background of developed information technology is the "Quantum / Soft Computing Optimizer" (QSCOptKBTM) SW based on soft and quantum computational intelligence toolkit.
Principles and methodologies of quantum algorithmic gate-based design on small quantum computer described. The possibilities of quantum algorithmic gates simulation on classical computers discussed. A new approach to a circuit implementation design of quantum algorithm gates for fast quantum massive parallel computing presented. SW & HW support sophisticated smart toolkit of supercomputing accelerator of quantum algorithm simulation on small quantum programmable computer algorithm gate (that can program in SW to implement arbitrary quantum algorithms by executing any sequence of universal quantum logic gates) described
Quantum computing is poised to revolutionize computational paradigms by leveraging quantum mechanics principles such as superposition and entanglement.However, the full-scale deployment of quantum applications remains constrained by hardware limitations, including high error rates and quantum decoherence.Quantum Software Engineering (QSE) emerges as a critical field addressing these challenges by optimizing algorithm design, error mitigation, and compiler strategies to enhance fault tolerance.Algorithm design in QSE focuses on developing quantum algorithms that efficiently exploit quantum parallelism while minimizing resource overhead.Key advancements include quantum variational algorithms, hybrid quantum-classical frameworks, and novel quantum heuristics tailored for optimization and cryptographic problems.Error mitigation techniques play a pivotal role in extending quantum circuit reliability without requiring full quantum error correction.Methods such as zero-noise extrapolation, probabilistic error cancellation, and quantum embedding techniques help reduce computational inaccuracies.Additionally, compiler optimization ensures efficient quantum program execution by minimizing gate depth, optimizing qubit mapping, and leveraging noise-adaptive scheduling to enhance quantum hardware performance.This paper explores the synergy between these three pillars of QSE, analyzing their impact on improving the feasibility of fault-tolerant quantum computing.It also examines emerging trends, including AI-driven quantum compilers, adaptive error mitigation techniques, and hardware-aware quantum software development.By bridging the gap between theoretical advancements and practical implementations, QSE provides a structured approach to accelerating quantum computing adoption across domains such as cryptography, materials science, and artificial intelligence.The findings underscore the necessity of interdisciplinary collaboration in developing robust quantum software solutions that maximize computational efficiency while mitigating inherent quantum hardware limitations.