quantum algorithm benchmark datasets

It has been developed to provide a large-scale set of datasets for the training, benchmarking and competitive development of classical and

The benchmarking problems in the QOBLIB are model-, algorithm-, and hardware-agnostic, meaning you can try your hand at solving them with any quantum or classical ...

We also provide working examples of how to use the QDataSet in practice and its use in benchmarking certain algorithms. Each part below provides in-depth detail on...

Quantum Benchmark Zoo aims to give an overview on the protocols and studies established to evaluate the performance of quantum computers.

The dataset is composed of three multidimensional arrays X (7165 x 23 x 23), T (7165) and P (5 x 1433) representing the inputs (Coulomb matrices), the labels (atom...

The repository tracks the progress of quantum computing algorithms and applications. It is made of libraries that include community-proposed benchmarking problems ...

The framework generates performance ... various problem sizes and illustrates algorithm limitations uncovered by the benchmarking...

Individual metrics are sometimes combined to provide an aggregated metric. For example, qubit stability, quantum gate fidelity and qubit readout f...

In this work we introduce an open source suite of quantum application-oriented performance benchmarks that is designed to measure...

We observed several interesting experimental phenomena: fine-tuning does not always outperform few-shot learning, and LLMs tend to exhibit consistent error patterns. QCircu...


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