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Related Experiment Videos

MNISQ: A Large-Scale Quantum Circuit Dataset for Machine Learning in the NISQ Era.

Leonardo Placidi^1,2, Ryuichiro Hataya³, Toshio Mori^4,5,6

¹Graduate School of Engineering Science, The University of Osaka, 1-3 Machikaneyama, Toyonaka, 560-0043, Osaka, Japan. u770335b@ecs.osaka-u.ac.jp.

Scientific Data

|May 26, 2026

Summary

We introduce MNISQ, a large dataset for quantum and classical machine learning in the Noisy Intermediate-Scale Quantum (NISQ) era. This resource aids in developing natural language processing and deep learning models for quantum computing tasks.

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Area of Science:

Quantum Computing
Machine Learning
Data Science

Background:

The Noisy Intermediate-Scale Quantum (NISQ) era presents unique challenges and opportunities for machine learning.
Developing large-scale datasets is crucial for advancing both quantum and classical machine learning models.
Bridging the gap between quantum computation and classical data analysis requires novel resources.

Purpose of the Study:

Introduce MNISQ, the first large-scale dataset for quantum and classical machine learning tailored for the NISQ era.
Provide a foundational resource for developing natural language processing (NLP) and deep learning models applicable to quantum computing.
Facilitate research into the impact of noise on quantum machine learning and the development of error-mitigation strategies.

Main Methods:

Generated a dataset of 4.95 million quantum circuits with 10 qubits and up to 100 two-qubit gates.
Derived data from quantum-encoded classical datasets like MNIST.
Made the dataset available in quantum circuit and Quantum Assembly Language (QASM) formats.

Main Results:

Quantum Kernel methods achieved up to 97% accuracy in multiclass circuit classification.
Classical NLP models (S4, Transformer, LSTM) were applied to QASM files.
The S4 model demonstrated 77% accuracy (81% with data augmentation) in classifying quantum circuits.

Conclusions:

MNISQ serves as a foundational resource for advancing quantum and classical machine learning research.
Quantum Kernel methods show high potential for circuit classification tasks.
Modern NLP models can effectively analyze and classify quantum circuits, paving the way for new computational approaches.