Quantum Computing for Automotive Applications

• URL: http://arxiv.org/abs/2409.14183v2
• Date: Wed, 25 Dec 2024 11:32:02 GMT
• Title: Quantum Computing for Automotive Applications
• Authors: Carlos A. Riofrío, Johannes Klepsch, Jernej Rudi Finžgar, Florian Kiwit, Leonhard Hölscher, Marvin Erdmann, Lukas Müller, Chandan Kumar, Youssef Achari Berrada, Andre Luckow,
• Abstract summary: This chapter investigates state-of-the-art quantum algorithms to enhance efficiency, accuracy, and scalability across the automotive value chain. We identify and discuss key challenges in near-term and fault-tolerant algorithms and their practical use in industrial applications.
• Score: 1.9377229617107175
• License:
• Abstract: Quantum computing could impact various industries, with the automotive industry with many computational challenges, from optimizing supply chains and manufacturing to vehicle engineering, being particularly promising. This chapter investigates state-of-the-art quantum algorithms to enhance efficiency, accuracy, and scalability across the automotive value chain. We explore recent advances in quantum optimization, machine learning, and numerical and chemistry simulations, highlighting their potential and limitations. We identify and discuss key challenges in near-term and fault-tolerant algorithms and their practical use in industrial applications. While quantum algorithms show potential in many application domains, current noisy intermediate-scale quantum hardware limits scale and, thus, business benefits. In the long term, fault-tolerant systems promise theoretical speedups; however, they also require further progress in hardware and software (e.\,g., related to error correction and data loading). We expect that with this progress, significant practical benefits will emerge eventually.

Related papers

• How to Build a Quantum Supercomputer: Scaling from Hundreds to Millions of Qubits [3.970891204847277]
  Small-scale demonstrations have become possible for quantum algorithmic primitives on hundreds of physical qubits. We show how the road to scaling could be paved by adopting existing semiconductor technology to build much higher-quality qubits. We argue that, to tackle industry-scale classical optimization and machine learning problems, heterogeneous quantum-probabilistic computing with custom-designed accelerators should be considered.
  arXiv  Detail & Related papers  (2024-11-15T18:22:46Z)
• The curse of random quantum data [62.24825255497622]
  We quantify the performances of quantum machine learning in the landscape of quantum data. We find that the training efficiency and generalization capabilities in quantum machine learning will be exponentially suppressed with the increase in qubits. Our findings apply to both the quantum kernel method and the large-width limit of quantum neural networks.
  arXiv  Detail & Related papers  (2024-08-19T12:18:07Z)
• Utilizing Resource Estimation for the Development of Quantum Computing Applications [4.726372592887009]
  We show how to utilize Resource Estimation to improve the development and assessment of real-world quantum computing applications. Overall, this enables end-users already today to check out the promises of possible future quantum computing applications.
  arXiv  Detail & Related papers  (2024-02-19T19:00:01Z)
• Quantum algorithms: A survey of applications and end-to-end complexities [90.05272647148196]
  The anticipated applications of quantum computers span across science and industry. We present a survey of several potential application areas of quantum algorithms. We outline the challenges and opportunities in each area in an "end-to-end" fashion.
  arXiv  Detail & Related papers  (2023-10-04T17:53:55Z)
• A Practitioner's Guide to Quantum Algorithms for Optimisation Problems [0.0]
  NP-hard optimisation problems are common in industrial areas such as logistics and finance. This paper aims to provide a comprehensive overview of the theory of quantum optimisation techniques. It focuses on their near-term potential for noisy intermediate scale quantum devices.
  arXiv  Detail & Related papers  (2023-05-12T08:57:36Z)
• Quantum Machine Learning: from physics to software engineering [58.720142291102135]
  We show how classical machine learning approach can help improve the facilities of quantum computers. We discuss how quantum algorithms and quantum computers may be useful for solving classical machine learning tasks.
  arXiv  Detail & Related papers  (2023-01-04T23:37:45Z)
• Near-Term Quantum Computing Techniques: Variational Quantum Algorithms, Error Mitigation, Circuit Compilation, Benchmarking and Classical Simulation [5.381727213688375]
  We are still a long way from reaching the maturity of a full-fledged quantum computer. An outstanding challenge is to come up with an application that can reliably carry out a nontrivial task. Several near-term quantum computing techniques have been proposed to characterize and mitigate errors.
  arXiv  Detail & Related papers  (2022-11-16T07:53:15Z)
• Assessing requirements to scale to practical quantum advantage [56.22441723982983]
  We develop a framework for quantum resource estimation, abstracting the layers of the stack, to estimate resources required for large-scale quantum applications. We assess three scaled quantum applications and find that hundreds of thousands to millions of physical qubits are needed to achieve practical quantum advantage. A goal of our work is to accelerate progress towards practical quantum advantage by enabling the broader community to explore design choices across the stack.
  arXiv  Detail & Related papers  (2022-11-14T18:50:27Z)
• Prospects and challenges of quantum finance [5.545791216381869]
  We consider quantum speedups for Monte Carlo methods, portfolio optimization, and machine learning. The near-term relevance of these quantum finance algorithms varies widely across applications. We describe powerful ways to bring these speedups closer to experimental feasibility.
  arXiv  Detail & Related papers  (2020-11-12T17:02:11Z)
• Quantum circuit architecture search for variational quantum algorithms [88.71725630554758]
  We propose a resource and runtime efficient scheme termed quantum architecture search (QAS) QAS automatically seeks a near-optimal ansatz to balance benefits and side-effects brought by adding more noisy quantum gates. We implement QAS on both the numerical simulator and real quantum hardware, via the IBM cloud, to accomplish data classification and quantum chemistry tasks.
  arXiv  Detail & Related papers  (2020-10-20T12:06:27Z)
• An Application of Quantum Annealing Computing to Seismic Inversion [55.41644538483948]
  We apply a quantum algorithm to a D-Wave quantum annealer to solve a small scale seismic inversions problem. The accuracy achieved by the quantum computer is at least as good as that of the classical computer.
  arXiv  Detail & Related papers  (2020-05-06T14:18:44Z)

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.