Advanced Examples: Algorithms - dynexcoin/DynexSDK GitHub Wiki
Advanced Examples (Algorithms)
Explore our examples demonstrating how to solve NP problems using quantum algorithms, including number partitioning, vertex cover, and more. These examples showcase the power of our quantum computing platform in tackling complex computational challenges efficiently. Dive into these practical applications to understand how quantum algorithms can revolutionize problem-solving in various fields.
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Example Grover Integer Factorisation Circuit | The circuit returns the probabilities of each possible combination of factors after the Grover iterations. This process leverages the power of quantum parallelism and Grover's algorithm to factorize the integer
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Example: Shor Integer Factorisation Circuit | Shor’s algorithm leverages quantum parallelism and the QFT to efficiently factorize large integers, a problem that is classically hard but feasible with quantum computing. This circuit is a practical implementation of Shor’s algorithm, targeting specific numbers like 𝑁=35 with a randomly chosen base 𝑎=12. The algorithm iteratively searches for the period 𝑟, and once found, uses it to derive the prime factors of 𝑁.
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Example: Grover Quantum Search on Dynex | Scientific background: Grover, L.K. From Schrödinger’s equation to the quantum search algorithm. Pramana - J Phys 56, 333–348 (2001). https://doi.org/10.1007/s12043-001-0128-3
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Example: Quantum Multi-Vehicle Routing on Dynex | Dynex's Multi-Vehicle Routing solution harnesses the power of quantum computing to optimize the routing and scheduling of multiple vehicles within logistics, transportation, and delivery services. By utilizing advanced quantum algorithms, this use case addresses complex logistical challenges, such as determining the most efficient routes and schedules for a fleet of vehicles to minimize travel time and fuel consumption while maximizing service delivery efficiency. This innovative approach not only enhances operational efficiency but also significantly reduces costs and environmental impact. The Multi-Vehicle Routing solution is ideal for industries that rely on fleet management, offering a robust, scalable, and intelligent system to improve overall logistical performance.
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Example: Quantum Workforce Scheduling | This advanced solution leverages the power of quantum algorithms to optimize the scheduling and allocation of workforce resources, ensuring maximum efficiency and productivity. By addressing complex scheduling challenges, such as shift assignments, task allocation, and resource management, Dynex's Workforce Scheduling application enhances operational performance, reduces costs, and improves employee satisfaction. This innovative use case is ideal for industries that require precise and dynamic workforce management, offering a robust and scalable solution to meet the demands of modern business operations.
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Example: Quantum Flow Shop Scheduling | Job shop scheduling (JSS) is an optimization challenge focused on scheduling jobs with varied processing orders on multiple machines, aiming to minimize the makespan or the completion time of the final task. Flow shop scheduling (FSS), a specialized form of JSS, requires every job to be processed in the same order across all machines. This demo showcases the application of Dynex's quantum computing technology to efficiently solve both JSS and FSS problems. By leveraging the power of quantum optimization, Dynex significantly reduces the makespan, demonstrating superior scheduling efficiency and operational performance. This capability is crucial for industries requiring precise and optimized scheduling of tasks across different machines and processes.
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Example: Enhancing MaxCut Solutions: Dynex’s Benchmark Performance on G70 Using Quantum Computing
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Example: Quantum Single Image Super-Resolution | Scientific background: Choong HY, Kumar S, Van Gool L. Quantum Annealing for Single Image Super-Resolution. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2023 (pp. 1150-1159).
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Example: Quantum Integer Factorization | Scientific background: Jiang, S., Britt, K.A., McCaskey, A.J. et al. Quantum Annealing for Prime Factorization. Sci Rep 8, 17667 (2018)
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Example: Quantum n Queen Problem | Max Bezzel; published in 1848
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Example: Quantum Sudoku Algorithm | Scientific background: Timothy Resnick, Sudoku at the Intersection of Classical and Quantum Computing, University of Auckland, NZ, Centre for Discrete Mathematics and Theoretical Computer Science