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Tensor Network Based Efficient Quantum Data Loading of Images
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Image-based data is a popular arena for testing quantum machine learning algorithms. A crucial factor in realizing quantum advantage for these applications is the ability to efficiently represent images as quantum states. Here we present a novel method for creating quantum states that approximately encode images as amplitudes, based on recently proposed techniques that convert matrix product states to quantum circuits. The numbers of gates and qubits in our method scale logarithmically in the number of pixels given a desired accuracy, which make it suitable for near term quantum computers. Finally, we experimentally demonstrate our technique on 8 qubits of a trapped ion quantum computer for complex images of road scenes, making this the first large instance of full amplitude encoding of an image in a quantum state.
Forward citations
Cited by 2 Pith papers
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Scaling Quantum Machine Learning without Tricks: Full-Resolution and Diverse Image Generation
A single end-to-end quantum generator using an image-tailored circuit and learnable multimodal noise achieves state-of-the-art simulated FID scores on full MNIST and Fashion-MNIST without tricks.
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Hybrid Quantum Neural Networks: Theory, Implementations, and Applications
A balanced review of hybrid quantum neural networks, concluding that quantum layers help on structured, small-scale and quantum-native problems but do not yet beat classical models on generic benchmarks.
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