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This paper proposes three changes to quantum recurrent neural networks: input preprocessing that improves generalization on two financial datasets, an approximate feature map that replaces costly exact state preparation, and a circuit layout that cuts depth while matching the original model on ideal hardware. The gains are evaluated on specific financial datasets and do not establish quantum advantage. Readers should note that the depth reduction holds only for noiseless processors, so its practical value depends on how these circuits perform on real devices.
Improving Quantum Recurrent Neural Networks with Amplitude Encoding
arXiv:2508.16784v4 Announce Type: replace Abstract: Recurrent Neural Networks (RNNs) are widely used to model complex temporal dependencies in sequential data. Quantum Recurrent Neural Networks (QRNNs) extend this idea to quantum machine learning by using quantum circuits inspired by classical recurrent architectures. In this paper, we propose three modifications to existing QRNN implementations that improve performance, generalization, and circuit efficiency. First, we introduce a simple preprocessing technique that augments amplitude encoded inputs with their prenormalized magnitudes, improving generalization on two financial data sets. Second, we show that a trained model can achieve comparable results with an approximate feature map rather than an expensive exact state preparation circuit. Third, we present a novel QRNN circuit architecture that is mathematically equivalent to the original model on ideal processors, but significantly reduces circuit depth. We demonstrate how these techniques can be put together to improve the performance of a QRNN when making predictions on real world financial data sets.
Abstract:Recurrent Neural Networks (RNNs) are widely used to model complex temporal dependencies in sequential data. Quantum Recurrent Neural Networks (QRNNs) extend this idea to quantum machine learning by using quantum circuits inspired by classical recurrent architectures. In this paper, we propose three modifications to existing QRNN implementations that improve performance, generalization, and circuit efficiency. First, we introduce a simple preprocessing technique that augments amplitude encoded inputs with their prenormalized magnitudes, improving generalization on two financial data sets. Second, we show that a trained model can achieve comparable results with an approximate feature map rather than an expensive exact state preparation circuit. Third, we present a novel QRNN circuit architecture that is mathematically equivalent to the original model on ideal processors, but significantly reduces circuit depth. We demonstrate how these techniques can be put together to improve the performance of a QRNN when making predictions on real world financial data sets.
arXiv:2508.16784 [quant-ph]
(or arXiv:2508.16784v4 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2508.16784 Focus to learn more arXiv-issued DOI via DataCite
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