Evaluating Three Levels of Quantum Metrics on Quantum-Inspire Hardware
- URL: http://arxiv.org/abs/2310.01120v1
- Date: Mon, 2 Oct 2023 11:54:12 GMT
- Title: Evaluating Three Levels of Quantum Metrics on Quantum-Inspire Hardware
- Authors: Ward van der Schoot, Robert Wezeman, Pieter Thijs Eendebak, Niels M.
P. Neumann, Frank Phillipson
- Abstract summary: This begs the question of which device excels at which tasks and how to compare these different quantum devices with one another.
Different metrics focus on different aspects of (quantum) devices and choosing the right metric to benchmark one device against another is a difficult choice.
In this paper we aim to give an overview of this zoo of metrics by grouping established metrics in three levels: component level, system level and application level.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: With the rise of quantum computing, many quantum devices have been developed
and many more devices are being developed as we speak. This begs the question
of which device excels at which tasks and how to compare these different
quantum devices with one another. The answer is given by quantum metrics, of
which many exist today already. Different metrics focus on different aspects of
(quantum) devices and choosing the right metric to benchmark one device against
another is a difficult choice. In this paper we aim to give an overview of this
zoo of metrics by grouping established metrics in three levels: component
level, system level and application level. With this characterisation we also
mention what the merits and uses are for each of the different levels. In
addition, we evaluate these metrics on the Starmon-5 device of Quantum-Inspire
through the cloud access, giving the most complete benchmark of a quantum
device from an user experience to date.
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) - 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) - QuAS: Quantum Application Score for benchmarking the utility of quantum computers [0.0]
This paper presents a revised holistic scoring method called the Quantum Application Score (QuAS)
We discuss how to integrate both and thereby obtain an application-level metric that better quantifies the practical utility of quantum computers.
We evaluate the new metric on different hardware platforms such as D-Wave and IBM as well as quantum simulators of Quantum Inspire and Rigetti.
arXiv Detail & Related papers (2024-06-06T09:39:58Z) - Quantum Information Processing with Molecular Nanomagnets: an introduction [49.89725935672549]
We provide an introduction to Quantum Information Processing, focusing on a promising setup for its implementation.
We introduce the basic tools to understand and design quantum algorithms, always referring to their actual realization on a molecular spin architecture.
We present some examples of quantum algorithms proposed and implemented on a molecular spin qudit hardware.
arXiv Detail & Related papers (2024-05-31T16:43:20Z) - Multimodal deep representation learning for quantum cross-platform
verification [60.01590250213637]
Cross-platform verification, a critical undertaking in the realm of early-stage quantum computing, endeavors to characterize the similarity of two imperfect quantum devices executing identical algorithms.
We introduce an innovative multimodal learning approach, recognizing that the formalism of data in this task embodies two distinct modalities.
We devise a multimodal neural network to independently extract knowledge from these modalities, followed by a fusion operation to create a comprehensive data representation.
arXiv Detail & Related papers (2023-11-07T04:35:03Z) - Comparing Quantum Service Offerings [0.22369578015657962]
We compare several devices based on different hardware technologies and provided through different offerings.
By documenting the lessons learned from our experiments, we aim to simplify the usage of quantum-specific offerings.
arXiv Detail & Related papers (2023-04-25T11:06:22Z) - Extending the Q-score to an Application-level Quantum Metric Framework [0.0]
evaluating the performance of quantum devices is an important step towards scaling quantum devices and eventually using them in practice.
A prominent quantum metric is given by the Q-score metric of Atos.
We show that the Q-score defines a framework of quantum metrics, which allows benchmarking using different problems, user settings and solvers.
arXiv Detail & Related papers (2023-02-01T18:03:13Z) - Assessing requirements to scale to practical quantum advantage [56.22441723982983]
We develop a framework for quantum resource estimation, abstracting the layers of the stack, to estimate resources required for large-scale quantum applications.
We assess three scaled quantum applications and find that hundreds of thousands to millions of physical qubits are needed to achieve practical quantum advantage.
A goal of our work is to accelerate progress towards practical quantum advantage by enabling the broader community to explore design choices across the stack.
arXiv Detail & Related papers (2022-11-14T18:50:27Z) - Iterative Qubits Management for Quantum Index Searching in a Hybrid
System [56.39703478198019]
IQuCS aims at index searching and counting in a quantum-classical hybrid system.
We implement IQuCS with Qiskit and conduct intensive experiments.
Results demonstrate that it reduces qubits consumption by up to 66.2%.
arXiv Detail & Related papers (2022-09-22T21:54:28Z) - Scalable Benchmarks for Gate-Based Quantum Computers [5.735035463793008]
We develop and release an advanced quantum benchmarking framework.
It measures the performance of universal quantum devices in a hardware-agnostic way.
We present the benchmark results of twenty-one different quantum devices from IBM, Rigetti and IonQ.
arXiv Detail & Related papers (2021-04-21T18:00:12Z) - Some Size and Structure Metrics for Quantum Software [1.7704011486040847]
This paper proposes some basic metrics for quantum software.
These metrics are defined at different abstraction levels to represent various size and structure attributes.
The proposed metrics can be used to evaluate quantum software from various viewpoints.
arXiv Detail & Related papers (2021-03-16T02:53:17Z)
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.