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We introduce the first large-scale dataset, MNISQ, for both the Quantum andthe Classical Machine Learning community during the Noisy Intermediate-ScaleQuantum era. MNISQ consists of 4,950,000 data points organized in 9subdatasets. Building our dataset from the quantum encoding of classicalinformation (e.g., MNIST dataset), we deliver a dataset in a dual form: inquantum form, as circuits, and in classical form, as quantum circuitdescriptions (quantum programming language, QASM). In fact, also the MachineLearning research related to quantum computers undertakes a dual challenge:enhancing machine learning exploiting the power of quantum computers, whilealso leveraging state-of-the-art classical machine learning methodologies tohelp the advancement of quantum computing. Therefore, we perform circuitclassification on our dataset, tackling the task with both quantum andclassical models. In the quantum endeavor, we test our circuit dataset withQuantum Kernel methods, and we show excellent results up to $97\%$ accuracy. Inthe classical world, the underlying quantum mechanical structures within thequantum circuit data are not trivial. Nevertheless, we test our dataset onthree classical models: Structured State Space sequence model (S4), Transformerand LSTM. In particular, the S4 model applied on the tokenized QASM sequencesreaches an impressive $77\%$ accuracy. These findings illustrate that quantumcircuit-related datasets are likely to be quantum advantageous, but also thatstate-of-the-art machine learning methodologies can competently classify andrecognize quantum circuits. We finally entrust the quantum and classicalmachine learning community the fundamental challenge to build morequantum-classical datasets like ours and to build future benchmarks from ourexperiments. The dataset is accessible on GitHub and its circuits are easilyrun in qulacs or qiskit.

MNISQ: A Large-Scale Quantum Circuit Dataset for Machine Learning on/for Quantum Computers in the NISQ era