Quantum Computing Applications for Flight Trajectory Optimization

• URL: http://arxiv.org/abs/2304.14445v1
• Date: Thu, 27 Apr 2023 18:09:45 GMT
• Title: Quantum Computing Applications for Flight Trajectory Optimization
• Authors: Henry Makhanov, Kanav Setia, Junyu Liu, Vanesa Gomez-Gonzalez, Guillermo Jenaro-Rabadan
• Abstract summary: Flight path optimization is an essential operation within the aerospace engineering domain with important ecological and economic considerations. In recent years, the quantum computing field has made significant strides, paving the way for improved performance over classical algorithms. We present our results from running the quantum algorithms on IBM hardware and discuss potential approaches to accelerate the incorporation of quantum algorithms within the problem domain.
• Score: 5.858783038624031
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Major players in the global aerospace industry are shifting their focus toward achieving net carbon-neutral operations by 2050. A considerable portion of the overall carbon emission reduction is expected to come from new aircraft technologies, such as flight path optimization. In pursuing these sustainability objectives, we delve into the capacity of quantum computing to tackle computational challenges associated with flight path optimization, an essential operation within the aerospace engineering domain with important ecological and economic considerations. In recent years, the quantum computing field has made significant strides, paving the way for improved performance over classical algorithms. In order to effectively apply quantum algorithms in real-world scenarios, it is crucial to thoroughly examine and tackle the intrinsic overheads and constraints that exist in the present implementations of these algorithms. Our study delves into the application of quantum computers in flight path optimization problems and introduces a customizable modular framework designed to accommodate specific simulation requirements. We examine the running time of a hybrid quantum-classical algorithm across various quantum architectures and their simulations on CPUs and GPUs. A temporal comparison between the conventional classical algorithm and its quantum-improved counterpart indicates that achieving the theoretical speedup in practice may necessitate further innovation. We present our results from running the quantum algorithms on IBM hardware and discuss potential approaches to accelerate the incorporation of quantum algorithms within the problem domain.

Related papers

• Quantum Circuit Synthesis and Compilation Optimization: Overview and Prospects [0.0]
  In this survey, we explore the feasibility of an integrated design and optimization scheme that spans from the algorithmic level to quantum hardware, combining the steps of logic circuit design and compilation optimization. Leveraging the exceptional cognitive and learning capabilities of AI algorithms, one can reduce manual design costs, enhance the precision and efficiency of execution, and facilitate the implementation and validation of the superiority of quantum algorithms on hardware.
  arXiv  Detail & Related papers  (2024-06-30T15:50:10Z)
• MindSpore Quantum: A User-Friendly, High-Performance, and AI-Compatible Quantum Computing Framework [20.585698216552892]
  We introduce MindSpore Quantum, a pioneering hybrid quantum-classical framework with a primary focus on noisy intermediate-scale quantum (NISQ) algorithms. In addition to the core framework, we introduce QuPack, a meticulously crafted quantum computing acceleration engine.
  arXiv  Detail & Related papers  (2024-06-25T03:28:40Z)
• Graph Learning for Parameter Prediction of Quantum Approximate Optimization Algorithm [14.554010382366302]
  Quantum Approximate Optimization (QAOA) stands out for its potential to efficiently solve the Max-Cut problem. We use Graph Neural Networks (GNN) as a warm-start technique to optimize QAOA, using GNN as a warm-start technique. Our findings show GNN's potential in improving QAOA performance, opening new avenues for hybrid quantum-classical approaches in quantum computing.
  arXiv  Detail & Related papers  (2024-03-05T20:23:25Z)
• Quantum Subroutine for Variance Estimation: Algorithmic Design and Applications [80.04533958880862]
  Quantum computing sets the foundation for new ways of designing algorithms. New challenges arise concerning which field quantum speedup can be achieved. Looking for the design of quantum subroutines that are more efficient than their classical counterpart poses solid pillars to new powerful quantum algorithms.
  arXiv  Detail & Related papers  (2024-02-26T09:32:07Z)
• Quantum Algorithm Cards: Streamlining the development of hybrid classical-quantum applications [0.0]
  The emergence of quantum computing proposes a revolutionary paradigm that can radically transform numerous scientific and industrial application domains. The ability of quantum computers to scale computations implies better performance and efficiency for certain algorithmic tasks than current computers provide. To gain benefit from such improvement, quantum computers must be integrated with existing software systems, a process that is not straightforward.
  arXiv  Detail & Related papers  (2023-10-04T06:02:59Z)
• Quantum Annealing for Single Image Super-Resolution [86.69338893753886]
  We propose a quantum computing-based algorithm to solve the single image super-resolution (SISR) problem. The proposed AQC-based algorithm is demonstrated to achieve improved speed-up over a classical analog while maintaining comparable SISR accuracy.
  arXiv  Detail & Related papers  (2023-04-18T11:57:15Z)
• DQC$^2$O: Distributed Quantum Computing for Collaborative Optimization in Future Networks [54.03701670739067]
  We propose an adaptive distributed quantum computing approach to manage quantum computers and quantum channels for solving optimization tasks in future networks. Based on the proposed approach, we discuss the potential applications for collaborative optimization in future networks, such as smart grid management, IoT cooperation, and UAV trajectory planning.
  arXiv  Detail & Related papers  (2022-09-16T02:44:52Z)
• Variational Quantum Algorithms for Computational Fluid Dynamics [0.0]
  Variational quantum algorithms are particularly promising since they are comparatively noise tolerant. We show how variational quantum algorithms can be utilized in computational fluid dynamics. We argue that a quantum advantage over classical computing methods could be achieved by the end of this decade.
  arXiv  Detail & Related papers  (2022-09-11T18:49:22Z)
• Decomposition of Matrix Product States into Shallow Quantum Circuits [62.5210028594015]
  tensor network (TN) algorithms can be mapped to parametrized quantum circuits (PQCs) We propose a new protocol for approximating TN states using realistic quantum circuits. Our results reveal one particular protocol, involving sequential growth and optimization of the quantum circuit, to outperform all other methods.
  arXiv  Detail & Related papers  (2022-09-01T17:08:41Z)
• Quantum algorithms for quantum dynamics: A performance study on the spin-boson model [68.8204255655161]
  Quantum algorithms for quantum dynamics simulations are traditionally based on implementing a Trotter-approximation of the time-evolution operator. variational quantum algorithms have become an indispensable alternative, enabling small-scale simulations on present-day hardware. We show that, despite providing a clear reduction of quantum gate cost, the variational method in its current implementation is unlikely to lead to a quantum advantage.
  arXiv  Detail & Related papers  (2021-08-09T18:00:05Z)
• Space-efficient binary optimization for variational computing [68.8204255655161]
  We show that it is possible to greatly reduce the number of qubits needed for the Traveling Salesman Problem. We also propose encoding schemes which smoothly interpolate between the qubit-efficient and the circuit depth-efficient models.
  arXiv  Detail & Related papers  (2020-09-15T18:17:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.