Stability of Quantum Computers

• URL: http://arxiv.org/abs/2404.19082v1
• Date: Mon, 29 Apr 2024 19:51:06 GMT
• Title: Stability of Quantum Computers
• Authors: Samudra Dasgupta,
• Abstract summary: This dissertation focuses on the persistent issue of noise in quantum computing, particularly non-stationarity of noise parameters in transmon processors. Quantum computing's potential is immense, promising super-polynomial reductions in execution time, energy use, and memory requirements compared to classical computers.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantum computing's potential is immense, promising super-polynomial reductions in execution time, energy use, and memory requirements compared to classical computers. This technology has the power to revolutionize scientific applications such as simulating many-body quantum systems for molecular structure understanding, factorization of large integers, enhance machine learning, and in the process, disrupt industries like telecommunications, material science, pharmaceuticals and artificial intelligence. However, quantum computing's potential is curtailed by noise, further complicated by non-stationary noise parameter distributions across time and qubits. This dissertation focuses on the persistent issue of noise in quantum computing, particularly non-stationarity of noise parameters in transmon processors.

Related papers

• 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)
• 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)
• 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)
• 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)
• Quantum Simulation on Noisy Superconducting Quantum Computers [0.0]
  Quantum simulation is a potentially powerful application of quantum computing. There is little introductory literature or demonstrations of the topic at a graduate or undergraduate student level. This artificially raises the barrier to entry into the field which already has a limited workforce.
  arXiv  Detail & Related papers  (2022-09-06T19:44:18Z)
• Quantum Noise-Induced Reservoir Computing [0.6738135972929344]
  We propose a framework called quantum noise-induced reservoir computing. We show that some abstract quantum noise models can induce useful information processing capabilities for temporal input data. Our study opens up a novel path for diverting useful information from quantum computer noises into a more sophisticated information processor.
  arXiv  Detail & Related papers  (2022-07-16T12:21:48Z)
• Extending the reach of quantum computing for materials science with machine learning potentials [0.3352108528371308]
  We propose a strategy to extend the scope of quantum computational methods to large scale simulations using a machine learning potential. We investigate the trainability of a machine learning potential selecting various sources of noise. We construct the first machine learning potential from data computed on actual IBM Quantum processors for a hydrogen molecule.
  arXiv  Detail & Related papers  (2022-03-14T15:59:30Z)
• Recompilation-enhanced simulation of electron-phonon dynamics on IBM Quantum computers [62.997667081978825]
  We consider the absolute resource cost for gate-based quantum simulation of small electron-phonon systems. We perform experiments on IBM quantum hardware for both weak and strong electron-phonon coupling. Despite significant device noise, through the use of approximate circuit recompilation we obtain electron-phonon dynamics on current quantum computers comparable to exact diagonalisation.
  arXiv  Detail & Related papers  (2022-02-16T19:00:00Z)
• 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)
• Simulating quantum chemistry in the seniority-zero space on qubit-based quantum computers [0.0]
  We combine the so-called seniority-zero, or paired-electron, approximation of computational quantum chemistry with techniques for simulating molecular chemistry on gate-based quantum computers. We show that using the freed-up quantum resources for increasing the basis set can lead to more accurate results and reductions in the necessary number of quantum computing runs.
  arXiv  Detail & Related papers  (2020-01-31T19:44:37Z)

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.