数据链接：
官方服务：
资源简介：
A collection of pre-optimized QV64, 128, 256, 512 and 1024 to be mapped to a line graph.
应用场景：
创建时间：
2025-01-24
相关数据集
Scalable Randomized Benchmarking of Quantum Computers using Mirror Circuits
This is supplemental data and code for: T. Proctor et al., Scalable randomized benchmarking of quantum computers using mirror circuits, arXiv 2112.09853 (2021). This folder contains all the data and
NIAID Data Ecosystem70
Data underlying the publication: Reinforcement learning in compilers for DQC
It becomes increasingly difficult to scale the number of qubits in a single quantum processor. In order to scale to more computational capability, distributed quantum computing (DQC) offers an alterna
DataCite Commons2026-03-25 更新60
Measurement and classical optimization strategies for quantum variational problems
This dataset contains results obtained from simulations of the non-linear Schrödinger equation using quantum networks, alongside a measurement benchmark conducted for a 2-qubit test system. The data w
DataCite Commons2024-04-29 更新40
Quantum benchmark results: XENet.
Results of Core Redesign on the quantum computer with CrystalConv. σ denotes standard deviation. (CSV)
NIAID Data Ecosystem70
The results of k-concurrent community detection on benchmark graphs.
N, E, Ncom, and Mod. are the number of nodes, number of edges, number of communities and modularity respectively. We show results from both the D-Wave quantum annealer (QA) and a Classical annealer (C
Figshare2020-02-13 更新50
