A learnable scaling parameter called Quantum Measurement Temperature (QMT) is introduced to rescale quantum measurement outputs in hybrid quantum neural networks. This approach mitigates measurement-induced logit contraction, enhancing gradient magnitude and stability during training without altering the quantum circuit or measurement operators. Experiments show improved logit separation, gradient strength, and classification accuracy in protein and image classification tasks.