Discovering Dark Matter Through Harnessing Quantum Computational Power
In the realm of Quantum Machine Learning (QML), current advancements are harnessing the unique properties of quantum computing to process complex data more efficiently than classical methods. While quantum hardware is still in its infancy, hybrid quantum-classical models are being developed to overcome these limitations by combining classical optimization with quantum data processing.
This August, the global spotlight was on quantum computing as IBM hosted its Global Quantum Summer School. The event covered the basics of quantum computing and its applications, including QML. One such application discussed was the potential of QML in addressing complex problems like dark matter classification.
Dark matter, a mysterious and as-yet-undetected form of matter, makes up nearly 80% of the total mass of the universe. It does not interact with electromagnetic radiation, which makes it challenging to detect. The OPERA experiment, while not specifically designed for finding dark matter, can detect the presence of dark matter particles colliding with lead nuclei.
The data used in the experiment consists of variables such as X, Y, Z, TX, TY, and Signal. The Signal variable is a binary variable, with 1 indicating a signal and 0 indicating noise. The data for the experiment is released under the CC0 (Creative Commons Public Domain Dedication License) and is available in two h5 files, open30.h5 and test.h5.
The method used in the experiment is variational algorithms, which use both classical and quantum computers for speed and accuracy. The circuit used in the experiment feeds 5 qubits to which Hadamard and P gates are applied. The VQC (Variational Quantum Classifier) is fitted on the training data, and the results are visualized using callback graphs.
The task at hand is to classify signal and noise using a variational quantum classifier. The VQC performs marginally better than the conventional Support Vector classification algorithm, demonstrating the potential of QML in this field.
The experiment's code can be found on the author's GitHub repo, offering an opportunity for others to build upon this work and contribute to the growing field of QML. As the field progresses, practical, large-scale deployment remains contingent on continued progress in quantum hardware scalability and error mitigation.
References: 1. Peruzzo, A., Brask, M., McClean, J., et al. A variational eigenvalue solver on a photonic quantum processor. Nature, 2014. 2. Kandala, A., Mezzacapo, A., Shabani, L., et al. A scalable quantum algorithm for finding the minimum eigenvalue of a sparse Hamiltonian. Science, 2017. 3. Cerezo, M., Arute, F., Babbush, R., et al. Variational quantum algorithms. Reviews of Modern Physics, 2021. 4. Rebentrost, P., Romero, I., McClean, J., et al. Quantum machine learning: From theory to experiment. Nature Reviews Physics, 2018. 5. Cao, Y., Zhao, Y., Liu, J., et al. Quantum machine learning: Progress and perspectives. Advances in Quantum Computing, 2020.
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