Visual Analytics of Performance of Quantum Computing Systems and Circuit Optimization

• URL: http://arxiv.org/abs/2409.06159v1
• Date: Tue, 10 Sep 2024 02:09:55 GMT
• Title: Visual Analytics of Performance of Quantum Computing Systems and Circuit Optimization
• Authors: Junghoon Chae, Chad A. Steed, Travis S. Humble,
• Abstract summary: We describe a visual analytics approach for analyzing the performance properties of quantum devices and quantum circuit optimization. Our approach allows users to explore spatial and temporal patterns in quantum device performance data. Detailed analysis of the error properties characterizing individual qubits is also supported.
• Score: 0.23213238782019316
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Driven by potential exponential speedups in business, security, and scientific scenarios, interest in quantum computing is surging. This interest feeds the development of quantum computing hardware, but several challenges arise in optimizing application performance for hardware metrics (e.g., qubit coherence and gate fidelity). In this work, we describe a visual analytics approach for analyzing the performance properties of quantum devices and quantum circuit optimization. Our approach allows users to explore spatial and temporal patterns in quantum device performance data and it computes similarities and variances in key performance metrics. Detailed analysis of the error properties characterizing individual qubits is also supported. We also describe a method for visualizing the optimization of quantum circuits. The resulting visualization tool allows researchers to design more efficient quantum algorithms and applications by increasing the interpretability of quantum computations.

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)
• Quantum Circuit Synthesis and Compilation Optimization: Overview and Prospects [0.0]
  In this survey, we explore the feasibility of an integrated design and optimization scheme that spans from the algorithmic level to quantum hardware, combining the steps of logic circuit design and compilation optimization. Leveraging the exceptional cognitive and learning capabilities of AI algorithms, one can reduce manual design costs, enhance the precision and efficiency of execution, and facilitate the implementation and validation of the superiority of quantum algorithms on hardware.
  arXiv  Detail & Related papers  (2024-06-30T15:50:10Z)
• Neural auto-designer for enhanced quantum kernels [59.616404192966016]
  We present a data-driven approach that automates the design of problem-specific quantum feature maps. Our work highlights the substantial role of deep learning in advancing quantum machine learning.
  arXiv  Detail & Related papers  (2024-01-20T03:11:59Z)
• Randomized Benchmarking of Local Zeroth-Order Optimizers for Variational Quantum Systems [65.268245109828]
  We compare the performance of classicals across a series of partially-randomized tasks. We focus on local zeroth-orders due to their generally favorable performance and query-efficiency on quantum systems.
  arXiv  Detail & Related papers  (2023-10-14T02:13:26Z)
• Optimization Applications as Quantum Performance Benchmarks [0.0]
  Combinatorial optimization is anticipated to be one of the primary use cases for quantum computation in the coming years. Inspired by existing methods to characterize classical optimization algorithms, we analyze the solution quality obtained by solving Max-Cut problems. This is used to guide the development of an advanced benchmarking framework for quantum computers.
  arXiv  Detail & Related papers  (2023-02-05T01:56:06Z)
• Interaction graph-based characterization of quantum benchmarks for improving quantum circuit mapping techniques [1.351147045576948]
  We propose to extend the characterization of quantum circuits by including qubit interaction graph properties. Our study reveals a correlation between interaction graph-based parameters and mapping performance metrics for various existing configurations of quantum devices.
  arXiv  Detail & Related papers  (2022-12-13T15:24:37Z)
• Quantum circuit architecture search on a superconducting processor [56.04169357427682]
  Variational quantum algorithms (VQAs) have shown strong evidences to gain provable computational advantages for diverse fields such as finance, machine learning, and chemistry. However, the ansatz exploited in modern VQAs is incapable of balancing the tradeoff between expressivity and trainability. We demonstrate the first proof-of-principle experiment of applying an efficient automatic ansatz design technique to enhance VQAs on an 8-qubit superconducting quantum processor.
  arXiv  Detail & Related papers  (2022-01-04T01:53:42Z)
• Storage properties of a quantum perceptron [0.0]
  We investigate the storage capacity of a particular quantum perceptron architecture. We focus on a specific quantum perceptron model and explore its storage properties in the limit of a large number of inputs.
  arXiv  Detail & Related papers  (2021-11-16T12:32:34Z)
• Variational Quantum Optimization with Multi-Basis Encodings [62.72309460291971]
  We introduce a new variational quantum algorithm that benefits from two innovations: multi-basis graph complexity and nonlinear activation functions. Our results in increased optimization performance, two increase in effective landscapes and a reduction in measurement progress.
  arXiv  Detail & Related papers  (2021-06-24T20:16:02Z)
• 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)
• Quantum ensemble of trained classifiers [2.048335092363436]
  A quantum computer is capable of representing an exponentially large set of states, according to the number of qubits available. Quantum machine learning explores the potential of quantum computing to enhance machine learning algorithms.
  arXiv  Detail & Related papers  (2020-07-18T01:01:33Z)

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.