Mitigating Measurement-Induced Training Instability in Hybrid Quantum Neural Networks for Protein Classification
Hybrid Quantum Neural Network (QNN) classifiers produce logits as expectation values of quantum measurement operators. For standard Pauli measurements, these outputs are intrinsically bounded to the interval [-1,1]. When such bounded logits are used directly with the cross-entropy loss applied to softmax-normalized logits for multi-class classification, the loss function operates in a regime of weak sensitivity to logit differences. As a consequence, parameter gradients are suppressed, leading to unstable optimization in variational quantum classifiers (VQCs). In this work, we identify this effect as measurement-induced logit contraction, a previously uncharacterized source of trainabilit...
| Authors | Milton Mondal, Sushovan Chanda, Mohamad Mahdi Alawieh et al. |
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| Primary category | cs.LG |
| Published | 21 June 2026 |
| Last updated at source | 21 June 2026 |
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