Google Quantum AI's Quest for Error-Corrected Quantum Computers

• URL: http://arxiv.org/abs/2410.00917v1
• Date: Mon, 23 Sep 2024 15:56:14 GMT
• Title: Google Quantum AI's Quest for Error-Corrected Quantum Computers
• Authors: M. AbuGhanem,
• Abstract summary: Google Quantum AI is a leader in driving forward the development of practical quantum computers. This study highlights the transformative impact of Google Quantum AI's initiatives in shaping the future of quantum computing technology.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantum computers stand at the forefront of technological innovation, offering exponential computational speed-ups that challenge classical computing capabilities. At the cutting edge of this transformation is Google Quantum AI, a leader in driving forward the development of practical quantum computers. This article provides a comprehensive review of Google Quantum AI's pivotal role in the quantum computing landscape over the past decade, emphasizing their significant strides towards achieving quantum computational supremacy. By exploring their advancements and contributions in quantum hardware, quantum software, error correction, and quantum algorithms, this study highlights the transformative impact of Google Quantum AI's initiatives in shaping the future of quantum computing technology.

Related papers

• Technology and Performance Benchmarks of IQM's 20-Qubit Quantum Computer [56.435136806763055]
  IQM Quantum Computers is described covering both the QPU and the rest of the full-stack quantum computer. The focus is on a 20-qubit quantum computer featuring the Garnet QPU and its architecture, which we will scale up to 150 qubits. We present QPU and system-level benchmarks, including a median 2-qubit gate fidelity of 99.5% and genuinely entangling all 20 qubits in a Greenberger-Horne-Zeilinger (GHZ) state.
  arXiv  Detail & Related papers  (2024-08-22T14:26:10Z)
• Quantum Computing: Vision and Challenges [16.50566018023275]
  We discuss cutting-edge developments in quantum computer hardware advancement and subsequent advances in quantum cryptography, quantum software, and high-scalability quantum computers. Many potential challenges and exciting new trends for quantum technology research and development are highlighted in this paper for a broader debate.
  arXiv  Detail & Related papers  (2024-03-04T17:33:18Z)
• Quantum computing: principles and applications [3.717431207294639]
  We introduce the basic principles of quantum computing and the multilayer architecture for a quantum computer. Based on a mature experimental platform, the Nuclear Magnetic Resonance (NMR) platform, we introduce the basic steps to experimentally implement quantum computing.
  arXiv  Detail & Related papers  (2023-10-13T20:12:28Z)
• NISQ Computers: A Path to Quantum Supremacy [0.0]
  NISQ (Noisy Intermediate-Scale Quantum) computing has witnessed remarkable advancements. This transformative turning point, known as quantum supremacy, has been achieved amid a series of breakthroughs. We critically examine the quantum supremacy experiments conducted thus far, shedding light on their implications and contributions to the evolving landscape of quantum computing.
  arXiv  Detail & Related papers  (2023-09-29T09:35:15Z)
• 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)
• Optimal Stochastic Resource Allocation for Distributed Quantum Computing [50.809738453571015]
  We propose a resource allocation scheme for distributed quantum computing (DQC) based on programming to minimize the total deployment cost for quantum resources. The evaluation demonstrates the effectiveness and ability of the proposed scheme to balance the utilization of quantum computers and on-demand quantum computers.
  arXiv  Detail & Related papers  (2022-09-16T02:37:32Z)
• Recent Advances for Quantum Neural Networks in Generative Learning [98.88205308106778]
  Quantum generative learning models (QGLMs) may surpass their classical counterparts. We review the current progress of QGLMs from the perspective of machine learning. We discuss the potential applications of QGLMs in both conventional machine learning tasks and quantum physics.
  arXiv  Detail & Related papers  (2022-06-07T07:32:57Z)
• Qsun: an open-source platform towards practical quantum machine learning applications [0.0]
  This paper introduces our quantum virtual machine named Qsun, whose operation is underlined by quantum state wave-functions. We then report two tests representative of quantum machine learning: quantum linear regression and quantum neural network.
  arXiv  Detail & Related papers  (2021-07-22T09:37:31Z)
• Quantum Computing for Location Determination [6.141741864834815]
  We introduce an example for the expected gain of using quantum algorithms for location determination research. The proposed quantum algorithm has a complexity that is exponentially better than its classical algorithm version, both in space and running time. We discuss both software and hardware research challenges and opportunities that researchers can build on to explore this exciting new domain.
  arXiv  Detail & Related papers  (2021-06-11T15:39:35Z)
• Imaginary Time Propagation on a Quantum Chip [50.591267188664666]
  Evolution in imaginary time is a prominent technique for finding the ground state of quantum many-body systems. We propose an algorithm to implement imaginary time propagation on a quantum computer.
  arXiv  Detail & Related papers  (2021-02-24T12:48:00Z)
• 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.