Quantum Architecture Search: A Survey

• URL: http://arxiv.org/abs/2406.06210v1
• Date: Mon, 10 Jun 2024 12:17:46 GMT
• Title: Quantum Architecture Search: A Survey
• Authors: Darya Martyniuk, Johannes Jung, Adrian Paschke,
• Abstract summary: The application of quantum computing to solve real-world problems is still hampered by hardware limitations and a relatively under-explored landscape of quantum algorithms. Research on the automated generation of quantum circuits (PQCs) has gained considerable interest.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantum computing has made significant progress in recent years, attracting immense interest not only in research laboratories but also in various industries. However, the application of quantum computing to solve real-world problems is still hampered by a number of challenges, including hardware limitations and a relatively under-explored landscape of quantum algorithms, especially when compared to the extensive development of classical computing. The design of quantum circuits, in particular parameterized quantum circuits (PQCs), which contain learnable parameters optimized by classical methods, is a non-trivial and time-consuming task requiring expert knowledge. As a result, research on the automated generation of PQCs, known as quantum architecture search (QAS), has gained considerable interest. QAS focuses on the use of machine learning and optimization-driven techniques to generate PQCs tailored to specific problems and characteristics of quantum hardware. In this paper, we provide an overview of QAS methods by examining relevant research studies in the field. We discuss main challenges in designing and performing an automated search for an optimal PQC, and survey ways to address them to ease future research.

Related papers

• 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 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)
• QNEAT: Natural Evolution of Variational Quantum Circuit Architecture [95.29334926638462]
  We focus on variational quantum circuits (VQC), which emerged as the most promising candidates for the quantum counterpart of neural networks. Although showing promising results, VQCs can be hard to train because of different issues, e.g., barren plateau, periodicity of the weights, or choice of architecture. We propose a gradient-free algorithm inspired by natural evolution to optimize both the weights and the architecture of the VQC.
  arXiv  Detail & Related papers  (2023-04-14T08:03:20Z)
• 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)
• Evolutionary Quantum Architecture Search for Parametrized Quantum Circuits [7.298440208725654]
  We introduce EQAS-PQC, an evolutionary quantum architecture search framework for PQC-based models. We show that our method can significantly improve the performance of hybrid quantum-classical models.
  arXiv  Detail & Related papers  (2022-08-23T19:47:37Z)
• Evolution of Quantum Computing: A Systematic Survey on the Use of Quantum Computing Tools [5.557009030881896]
  We conduct a systematic survey and categorize papers, tools, frameworks, platforms that facilitate quantum computing. We discuss the current essence, identify open challenges and provide future research direction. We conclude that scores of frameworks, tools and platforms are emerged in the past few years, improvement of currently available facilities would exploit the research activities in the quantum research community.
  arXiv  Detail & Related papers  (2022-04-04T21:21:12Z)
• Quantum Phase Recognition via Quantum Kernel Methods [6.3286116342955845]
  We explore the power of quantum learning algorithms in solving an important class of Quantum Phase Recognition problems. We numerically benchmark our algorithm for a variety of problems, including recognizing symmetry-protected topological phases and symmetry-broken phases. Our results highlight the capability of quantum machine learning in predicting such quantum phase transitions in many-particle systems.
  arXiv  Detail & Related papers  (2021-11-15T06:17:52Z)
• Variational Quantum Algorithms [1.9486734911696273]
  Quantum computers promise to unlock applications such as large quantum systems or solving large-scale linear algebra problems. Currently available quantum devices have serious constraints, including limited qubit numbers and noise processes that limit circuit depth. Variational Quantum Algorithms (VQAs), which employ a classical simulation to train a parametrized quantum circuit, have emerged as a leading strategy to address these constraints.
  arXiv  Detail & Related papers  (2020-12-16T21:00:46Z)
• 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)
• Differentiable Quantum Architecture Search [15.045985536395479]
  We propose a general framework of differentiable quantum architecture search (DQAS) DQAS enables automated designs of quantum circuits in an end-to-end differentiable fashion.
  arXiv  Detail & Related papers  (2020-10-16T18:00:03Z)
• 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.