Experimental quantum pattern recognition in IBMQ and diamond NVs

Abstract

One of the most promising applications of quantum computing is the processing of graphical data like images. Here, we investigate the possibility of realizing a quantum pattern recognition protocol based on swap test, and use the IBMQ noisy intermediate-scale quantum (NISQ) devices to verify the idea. We find that with a two-qubit protocol, swap test can efficiently detect the similarity between two patterns with good fidelity, though for three or more qubits the noise in the real devices becomes detrimental. To mitigate this noise effect, we resort to destructive swap test, which shows an improved performance for three-qubit states. Due to limited cloud access to larger IBMQ processors, we take a segment-wise approach to apply the destructive swap test on higher dimensional images. In this case, we define an average overlap measure which shows faithfulness to distinguish between two very different or very similar patterns when simulated on real IBMQ processors. As test images, we use binary images with simple patterns, greyscale MNIST numbers and MNIST fashion images, as well as binary images of human blood vessel obtained from magnetic resonance imaging (MRI). We also present an experimental set up for applying destructive swap test using the nitrogen vacancy centre (NVs) in diamond. Our experimental data show high fidelity for single qubit states. Lastly, we propose a protocol inspired from quantum associative memory, which works in an analogous way to supervised learning for performing quantum pattern recognition using destructive swap test.

15 pages, 15 figures

References in corpus (14)

• Supplementary information for "Quantum supremacy using a programmable superconducting processor"
• Quantum algorithm for solving linear systems of equations
• Quantum computational advantage using photons
• An introduction to quantum machine learning
• Strong quantum computational advantage using a superconducting quantum processor
• Photonic Boson Sampling in a Tunable Circuit
• Quantum-enhanced machine learning
• Phase-Programmable Gaussian Boson Sampling Using Stimulated Squeezed Light
• Experimental Realization of Quantum Artificial Intelligence
• Quantum Image Processing and Its Application to Edge Detection: Theory and Experiment
• NMR quantum simulation of localization effects induced by decoherence
• Quantum pattern recognition with liquid-state nuclear magnetic resonance
• Experimental implementation of adiabatic passage between different topological orders
• New Trends in Quantum Machine Learning

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• Quantum-Noise-Driven Generative Diffusion Models
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• Swap Test-based Characterization of Quantum Processes in Universal Quantum Computers
• Machine-learning based noise characterization and correction on neutral atoms NISQ devices
• Analysis of Quantum Image Representations for Supervised Classification