Test-Time Training with Quantum Auto-Encoder: From Distribution Shift to Noisy Quantum Circuits

TL;DR

This paper introduces a test-time training method using quantum auto-encoders to adapt to data distribution shifts and mitigate quantum circuit noise, enhancing robustness and performance in quantum machine learning.

Contribution

The paper presents a novel test-time training framework with quantum auto-encoders that addresses distribution shifts and noise in quantum circuits, supported by theoretical guarantees.

Findings

QTTT improves robustness against data shifts.

QTTT effectively reduces quantum circuit noise.

Theoretical performance guarantees are established.

Abstract

In this paper, we propose test-time training with the quantum auto-encoder (QTTT). QTTT adapts to (1) data distribution shifts between training and testing data and (2) quantum circuit error by minimizing the self-supervised loss of the quantum auto-encoder. Empirically, we show that QTTT is robust against data distribution shifts and effective in mitigating random unitary noise in the quantum circuits during the inference. Additionally, we establish the theoretical performance guarantee of the QTTT architecture. Our novel framework presents a significant advancement in developing quantum neural networks for future real-world applications and functions as a plug-and-play extension for quantum machine learning models.