HamilToniQ: An Open-Source Benchmark Toolkit for Quantum Computers
- URL: http://arxiv.org/abs/2404.13971v1
- Date: Mon, 22 Apr 2024 08:27:14 GMT
- Title: HamilToniQ: An Open-Source Benchmark Toolkit for Quantum Computers
- Authors: Xiaotian Xu, Kuan-Cheng Chen, Robert Wille,
- Abstract summary: HamilToniQ is an open-source, application-oriented benchmarking toolkit for the comprehensive evaluation of Quantum Processing Units (QPUs)
It incorporates a methodological framework assessing QPU types, topologies, and multi-QPU systems.
HamilToniQ's standardized score, H-Score, quantifies the fidelity and reliability of QPUs.
- Score: 4.795321943127061
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In this paper, we introduce HamilToniQ, an open-source, and application-oriented benchmarking toolkit for the comprehensive evaluation of Quantum Processing Units (QPUs). Designed to navigate the complexities of quantum computations, HamilToniQ incorporates a methodological framework assessing QPU types, topologies, and multi-QPU systems. The toolkit facilitates the evaluation of QPUs' performance through multiple steps including quantum circuit compilation and quantum error mitigation (QEM), integrating strategies that are unique to each stage. HamilToniQ's standardized score, H-Score, quantifies the fidelity and reliability of QPUs, providing a multidimensional perspective of QPU performance. With a focus on the Quantum Approximate Optimization Algorithm (QAOA), the toolkit enables direct, comparable analysis of QPUs, enhancing transparency and equity in benchmarking. Demonstrated in this paper, HamilToniQ has been validated on various IBM QPUs, affirming its effectiveness and robustness. Overall, HamilToniQ significantly contributes to the advancement of the quantum computing field by offering precise and equitable benchmarking metrics.
Related papers
- 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) - QKSAN: A Quantum Kernel Self-Attention Network [53.96779043113156]
A Quantum Kernel Self-Attention Mechanism (QKSAM) is introduced to combine the data representation merit of Quantum Kernel Methods (QKM) with the efficient information extraction capability of SAM.
A Quantum Kernel Self-Attention Network (QKSAN) framework is proposed based on QKSAM, which ingeniously incorporates the Deferred Measurement Principle (DMP) and conditional measurement techniques.
Four QKSAN sub-models are deployed on PennyLane and IBM Qiskit platforms to perform binary classification on MNIST and Fashion MNIST.
arXiv Detail & Related papers (2023-08-25T15:08:19Z) - Potential and limitations of quantum extreme learning machines [55.41644538483948]
We present a framework to model QRCs and QELMs, showing that they can be concisely described via single effective measurements.
Our analysis paves the way to a more thorough understanding of the capabilities and limitations of both QELMs and QRCs.
arXiv Detail & Related papers (2022-10-03T09:32:28Z) - 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) - QSAN: A Near-term Achievable Quantum Self-Attention Network [73.15524926159702]
Self-Attention Mechanism (SAM) is good at capturing the internal connections of features.
A novel Quantum Self-Attention Network (QSAN) is proposed for image classification tasks on near-term quantum devices.
arXiv Detail & Related papers (2022-07-14T12:22:51Z) - Open Source Variational Quantum Eigensolver Extension of the Quantum
Learning Machine (QLM) for Quantum Chemistry [0.0]
We introduce a novel open-source QC package, denoted Open-VQE, providing tools for using and developing chemically-inspired adaptive methods.
It is able to use the Atos Quantum Learning Machine (QLM), a general programming framework enabling to write, optimize simulate computing programs.
Along with OpenVQE, we introduce myQLMFermion, a new open-source module (that includes the key QLM ressources that are important for QC developments)
arXiv Detail & Related papers (2022-06-17T14:24:22Z) - QPack Scores: Quantitative performance metrics for application-oriented
quantum computer benchmarking [1.0323063834827415]
This paper presents the benchmark score definitions of QPack, an application-oriented cross-platform benchmarking suite for quantum computers and simulators.
A comparison is made between various quantum computer simulators, running both locally and on vendors' remote cloud services.
arXiv Detail & Related papers (2022-05-24T15:18:24Z) - 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) - QPack: Quantum Approximate Optimization Algorithms as universal
benchmark for quantum computers [1.1602089225841632]
We present QPack, a universal benchmark for Noisy Intermediate-Scale Quantum (NISQ) computers.
QPack evaluates the maximum problem size a quantum computer can solve, the required runtime, as well as the achieved accuracy.
arXiv Detail & Related papers (2021-03-31T16:20:51Z) - Benchmarking quantum co-processors in an application-centric,
hardware-agnostic and scalable way [0.0]
We introduce a new benchmark, dubbed Atos Q-score (TM)
The Q-score measures the maximum number of qubits that can be used effectively to solve the MaxCut optimization problem.
We provide an open-source implementation of Q-score that makes it easy to compute the Q-score of any quantum hardware.
arXiv Detail & Related papers (2021-02-25T16:26:23Z) - 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)
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.