Single-Qubit Cross Platform Comparison of Quantum Computing Hardware

• URL: http://arxiv.org/abs/2108.11334v1
• Date: Wed, 25 Aug 2021 16:47:20 GMT
• Title: Single-Qubit Cross Platform Comparison of Quantum Computing Hardware
• Authors: Adrien Suau, Jon Nelson, Marc Vuffray, Andrey Y. Lokhov, Lukasz Cincio, Carleton Coffrin
• Abstract summary: This work proposes a single-qubit protocol for measuring some basic performance characteristics of individual qubits in both models of quantum computation. The proposed protocol scales to large quantum computers with thousands of qubits and provides insights into the distribution of qubit properties within a particular hardware device and across families of devices.
• Score: 11.33951192243728
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As a variety of quantum computing models and platforms become available, methods for assessing and comparing the performance of these devices are of increasing interest and importance. Despite being built of the same fundamental computational unit, radically different approaches have emerged for characterizing the performance of qubits in gate-based and quantum annealing computers, limiting and complicating consistent cross-platform comparisons. To fill this gap, this work proposes a single-qubit protocol (Q-RBPN) for measuring some basic performance characteristics of individual qubits in both models of quantum computation. The proposed protocol scales to large quantum computers with thousands of qubits and provides insights into the distribution of qubit properties within a particular hardware device and across families of devices. The efficacy of the Q-RBPN protocol is demonstrated through the analysis of more than 300 gate-based qubits spanning eighteen machines and 2000 annealing-based qubits from one machine, revealing some unexpected differences in qubit performance. Overall, the proposed Q-RBPN protocol provides a new platform-agnostic tool for assessing the performance of a wide range of emerging quantum computing devices.

Related papers

• Extending Quantum Perceptrons: Rydberg Devices, Multi-Class Classification, and Error Tolerance [67.77677387243135]
  Quantum Neuromorphic Computing (QNC) merges quantum computation with neural computation to create scalable, noise-resilient algorithms for quantum machine learning (QML) At the core of QNC is the quantum perceptron (QP), which leverages the analog dynamics of interacting qubits to enable universal quantum computation.
  arXiv  Detail & Related papers  (2024-11-13T23:56:20Z)
• Supervised binary classification of small-scale digits images with a trapped-ion quantum processor [56.089799129458875]
  We show that a quantum processor can correctly solve the basic classification task considered. With the increase of the capabilities quantum processors, they can become a useful tool for machine learning.
  arXiv  Detail & Related papers  (2024-06-17T18:20:51Z)
• A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelity [50.387179833629254]
  We introduce a collaborative classical-quantum architecture called co-TenQu. Co-TenQu enhances a classical deep neural network by up to 41.72% in a fair setting. It outperforms other quantum-based methods by up to 1.9 times and achieves similar accuracy while utilizing 70.59% fewer qubits.
  arXiv  Detail & Related papers  (2024-02-23T14:09:41Z)
• Characterizing the Inter-Core Qubit Traffic in Large-Scale Quantum Modular Architectures [2.465579331213113]
  We present a pioneering characterization of the era of monolithic-temporal inter-core qubit traffic in large-scale circuits. The programs are executed on an all-to-all connected-core architecture that supports up to around 1000 qubits. Based on the showcased results, we provide a set of guidelines to improve mapping quantum circuits to multi-core processors, and lay the foundations of benchmarking large-scale multi-core architectures.
  arXiv  Detail & Related papers  (2023-10-03T09:54:41Z)
• Majorization-based benchmark of the complexity of quantum processors [105.54048699217668]
  We numerically simulate and characterize the operation of various quantum processors. We identify and assess quantum complexity by comparing the performance of each device against benchmark lines. We find that the majorization-based benchmark holds as long as the circuits' output states have, on average, high purity.
  arXiv  Detail & Related papers  (2023-04-10T23:01:10Z)
• Characterizing quantum processors using discrete time crystals [0.0]
  We present a method for characterizing the performance of noisy quantum processors using discrete time crystals. We construct small sets of qubit layouts that cover the full topology of a target system, and execute our metric over these sets on a wide range of IBM Quantum processors.
  arXiv  Detail & Related papers  (2023-01-18T16:08:50Z)
• Decomposition of Matrix Product States into Shallow Quantum Circuits [62.5210028594015]
  tensor network (TN) algorithms can be mapped to parametrized quantum circuits (PQCs) We propose a new protocol for approximating TN states using realistic quantum circuits. Our results reveal one particular protocol, involving sequential growth and optimization of the quantum circuit, to outperform all other methods.
  arXiv  Detail & Related papers  (2022-09-01T17:08:41Z)
• Measuring NISQ Gate-Based Qubit Stability Using a 1+1 Field Theory and Cycle Benchmarking [50.8020641352841]
  We study coherent errors on a quantum hardware platform using a transverse field Ising model Hamiltonian as a sample user application. We identify inter-day and intra-day qubit calibration drift and the impacts of quantum circuit placement on groups of qubits in different physical locations on the processor. This paper also discusses how these measurements can provide a better understanding of these types of errors and how they may improve efforts to validate the accuracy of quantum computations.
  arXiv  Detail & Related papers  (2022-01-08T23:12:55Z)
• Distributed Quantum Computing with QMPI [11.71212583708166]
  We introduce an extension of the Message Passing Interface (MPI) to enable high-performance implementations of distributed quantum algorithms. In addition to a prototype implementation of quantum MPI, we present a performance model for distributed quantum computing, SENDQ.
  arXiv  Detail & Related papers  (2021-05-03T18:30:43Z)
• Higgs analysis with quantum classifiers [0.0]
  We have developed two quantum classifier models for the $tbartH(bbarb)$ classification problem. Our results serve as a proof of concept that Quantum Machine Learning (QML) methods can have similar or better performance.
  arXiv  Detail & Related papers  (2021-04-15T18:01:51Z)
• Single-Qubit Fidelity Assessment of Quantum Annealing Hardware [10.728339674268788]
  This work proposes a Quantum Annealing Single-qubit Assessment protocol for quantifying the performance of individual qubits. The proposed protocol scales to large quantum annealers with thousands of qubits and provides unique insights into the distribution of qubit properties.
  arXiv  Detail & Related papers  (2021-04-07T18:12:05Z)

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.