Adiabatic Encoding of Pre-trained MPS Classifiers into Quantum Circuits

• URL: http://arxiv.org/abs/2504.09250v1
• Date: Sat, 12 Apr 2025 15:12:46 GMT
• Title: Adiabatic Encoding of Pre-trained MPS Classifiers into Quantum Circuits
• Authors: Keisuke Murota,
• Abstract summary: We propose a framework that encodes pre-trained MPS-classifiers into quantum MPS circuits with postselection, and gradually removes the postselection while retaining performance.<n>We prove that training qMPS-classifiers from scratch on a certain artificial dataset is exponentially hard due to barren plateaus, but our adiabatic encoding circumvents this issue.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Although Quantum Neural Networks (QNNs) offer powerful methods for classification tasks, the training of QNNs faces two major training obstacles: barren plateaus and local minima. A promising solution is to first train a tensor-network (TN) model classically and then embed it into a QNN.\ However, embedding TN-classifiers into quantum circuits generally requires postselection whose success probability may decay exponentially with the system size. We propose an \emph{adiabatic encoding} framework that encodes pre-trained MPS-classifiers into quantum MPS (qMPS) circuits with postselection, and gradually removes the postselection while retaining performance. We prove that training qMPS-classifiers from scratch on a certain artificial dataset is exponentially hard due to barren plateaus, but our adiabatic encoding circumvents this issue. Additional numerical experiments on binary MNIST also confirm its robustness.

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