Artificial Intelligence for Quantum Computing

• URL: http://arxiv.org/abs/2411.09131v1
• Date: Thu, 14 Nov 2024 02:11:16 GMT
• Title: Artificial Intelligence for Quantum Computing
• Authors: Yuri Alexeev, Marwa H. Farag, Taylor L. Patti, Mark E. Wolf, Natalia Ares, Alán Aspuru-Guzik, Simon C. Benjamin, Zhenyu Cai, Zohim Chandani, Federico Fedele, Nicholas Harrigan, Jin-Sung Kim, Elica Kyoseva, Justin G. Lietz, Tom Lubowe, Alexander McCaskey, Roger G. Melko, Kouhei Nakaji, Alberto Peruzzo, Sam Stanwyck, Norm M. Tubman, Hanrui Wang, Timothy Costa,
• Abstract summary: Quantum computing is a prime candidate for AI's data-driven learning capabilities. Bringing leading techniques from AI to QC requires drawing on expertise from arguably two of the most advanced and esoteric areas of computer science. This paper reviews how state-of-the-art AI techniques are already advancing challenges across the hardware and software stack needed to develop useful QC.
• Score: 30.639337493477242
• License:
• Abstract: Artificial intelligence (AI) advancements over the past few years have had an unprecedented and revolutionary impact across everyday application areas. Its significance also extends to technical challenges within science and engineering, including the nascent field of quantum computing (QC). The counterintuitive nature and high-dimensional mathematics of QC make it a prime candidate for AI's data-driven learning capabilities, and in fact, many of QC's biggest scaling challenges may ultimately rest on developments in AI. However, bringing leading techniques from AI to QC requires drawing on disparate expertise from arguably two of the most advanced and esoteric areas of computer science. Here we aim to encourage this cross-pollination by reviewing how state-of-the-art AI techniques are already advancing challenges across the hardware and software stack needed to develop useful QC - from device design to applications. We then close by examining its future opportunities and obstacles in this space.

Related papers

• Overview of Current Challenges in Multi-Architecture Software Engineering and a Vision for the Future [0.0]
  The presented system architecture is based on the concept of dynamic, knowledge graph-based WebAssembly Twins. The resulting systems are to possess advanced autonomous capabilities, with full transparency and controllability by the end user.
  arXiv  Detail & Related papers  (2024-10-28T13:03:09Z)
• Quantum Artificial Intelligence: A Brief Survey [0.3495246564946556]
  Quantum Artificial Intelligence (QAI) is the intersection of quantum computing and AI. We provide a brief overview of what has been achieved in QAI so far and point to some open questions for future research.
  arXiv  Detail & Related papers  (2024-08-20T10:55:17Z)
• Quantum Computing and Visualization: A Disruptive Technological Change Ahead [0.753179862869346]
  The focus of this article is to explore ideas related to how visualization helps in understanding Quantum Computing (QC) QC is emerging as a promising pathway to overcome the growth limits in classical computing. visualization has played a role in QC by providing the means to show representations of the quantum state of single-qubits in superposition states and multiple-qubits in entangled states.
  arXiv  Detail & Related papers  (2023-10-07T22:57:04Z)
• Brain-Inspired Computational Intelligence via Predictive Coding [89.6335791546526]
  Predictive coding (PC) has shown promising performance in machine intelligence tasks. PC can model information processing in different brain areas, can be used in cognitive control and robotics.
  arXiv  Detail & Related papers  (2023-08-15T16:37:16Z)
• A new solution and concrete implementation steps for Artificial General Intelligence [4.320142895840622]
  In areas that need to interact with the actual environment, such as elderly care, home nanny, agricultural production, vehicle driving, trial and error are expensive. In this paper, we analyze the limitations of the technical route of large models, and by addressing these limitations, we propose solutions.
  arXiv  Detail & Related papers  (2023-08-12T13:31:02Z)
• Reliable AI: Does the Next Generation Require Quantum Computing? [71.84486326350338]
  We show that digital hardware is inherently constrained in solving problems about optimization, deep learning, or differential equations. In contrast, analog computing models, such as the Blum-Shub-Smale machine, exhibit the potential to surmount these limitations.
  arXiv  Detail & Related papers  (2023-07-03T19:10:45Z)
• Selected Trends in Artificial Intelligence for Space Applications [69.3474006357492]
  This chapter focuses on differentiable intelligence and on-board machine learning. We discuss a few selected projects originating from the European Space Agency's (ESA) Advanced Concepts Team (ACT)
  arXiv  Detail & Related papers  (2022-12-10T07:49:50Z)
• Enabling Automated Machine Learning for Model-Driven AI Engineering [60.09869520679979]
  We propose a novel approach to enable Model-Driven Software Engineering and Model-Driven AI Engineering. In particular, we support Automated ML, thus assisting software engineers without deep AI knowledge in developing AI-intensive systems.
  arXiv  Detail & Related papers  (2022-03-06T10:12:56Z)
• Edge-Cloud Polarization and Collaboration: A Comprehensive Survey [61.05059817550049]
  We conduct a systematic review for both cloud and edge AI. We are the first to set up the collaborative learning mechanism for cloud and edge modeling. We discuss potentials and practical experiences of some on-going advanced edge AI topics.
  arXiv  Detail & Related papers  (2021-11-11T05:58:23Z)
• The Why, What and How of Artificial General Intelligence Chip Development [0.0]
  The intelligent sensing, automation, and edge computing applications have been the market drivers for AI chips. The generalisation, performance, robustness, and scalability of the AI chip solutions are compared with human-like intelligence abilities.
  arXiv  Detail & Related papers  (2020-12-08T02:36:04Z)
• Empowering Things with Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things [98.10037444792444]
  We show how AI can empower the IoT to make it faster, smarter, greener, and safer. First, we present progress in AI research for IoT from four perspectives: perceiving, learning, reasoning, and behaving. Finally, we summarize some promising applications of AIoT that are likely to profoundly reshape our world.
  arXiv  Detail & Related papers  (2020-11-17T13:14:28Z)

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.