Quantum Reservoir Computing
Assess whether quantum reservoirs (with PCA‑encoded inputs) can outperform classical ones and how entanglement changes feature mapping.
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Problem Statement
I want to know if there is a quantum advantage in Quantum Reservoir Computing for image classification. If we take an image, and compute its PCA decomposition, and I feed the first X components to the reservoir as inputs (e.g. angle encoding), is there an advantage to using a quantum reservoir? What is the role of entanglement? How does the reservoir transform the features with and without entanglement? As a reservoir, we can take a simple transverse Ising model and angle encoding, and measuring a subset of pauli operators. You can try the MNIST dataset for digit classification, although I feel that the PCA is already so discriminatory (the only information in the picture is the digit, so *any* substantial difference between pictures immediately translates to a different classificaiton) that it will be hard to improve anything with QRC. Would other datasets be better test cases? papers: https://arxiv.org/pdf/2509.06873 https://arxiv.org/pdf/2409.00998…Read more
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Related work
- https://arxiv.org/pdf/2509.06873arXivhttps://arxiv.org/pdf/2509.06873
- https://arxiv.org/pdf/2409.00998arXivhttps://arxiv.org/pdf/2409.00998
- https://arxiv.org/pdf/2602.14677arXivhttps://arxiv.org/pdf/2602.14677
- https://arxiv.org/pdf/2008.08605arXivhttps://arxiv.org/pdf/2008.08605
- https://arxiv.org/pdf/2101.11020arXivhttps://arxiv.org/pdf/2101.11020