Decohering Tensor Network Quantum Machine Learning Models

TL;DR

This paper investigates how adding ancillas to tensor network quantum machine learning models can mitigate the effects of qubit decoherence, showing that ancillas can improve performance even under high decoherence.

Contribution

It demonstrates that increasing the virtual bond dimension with ancillas can compensate for decoherence effects in tensor network QML models, a novel insight for near-term quantum hardware.

Findings

Adding at least two ancillas improves model performance under decoherence.

Decohered unitary TTNs with ancillas perform as well as non-decohered models.

Ancillas help maintain classification accuracy despite qubit decoherence.

Abstract

Tensor network quantum machine learning (QML) models are promising applications on near-term quantum hardware. While decoherence of qubits is expected to decrease the performance of QML models, it is unclear to what extent the diminished performance can be compensated for by adding ancillas to the models and accordingly increasing the virtual bond dimension of the models. We investigate here the competition between decoherence and adding ancillas on the classification performance of two models, with an analysis of the decoherence effect from the perspective of regression. We present numerical evidence that the fully-decohered unitary tree tensor network (TTN) with two ancillas performs at least as well as the non-decohered unitary TTN, suggesting that it is beneficial to add at least two ancillas to the unitary TTN regardless of the amount of decoherence may be consequently introduced.