Robust data encodings for quantum classifiers

by   Ryan LaRose, et al.

Data representation is crucial for the success of machine learning models. In the context of quantum machine learning with near-term quantum computers, equally important considerations of how to efficiently input (encode) data and effectively deal with noise arise. In this work, we study data encodings for binary quantum classification and investigate their properties both with and without noise. For the common classifier we consider, we show that encodings determine the classes of learnable decision boundaries as well as the set of points which retain the same classification in the presence of noise. After defining the notion of a robust data encoding, we prove several results on robustness for different channels, discuss the existence of robust encodings, and prove an upper bound on the number of robust points in terms of fidelities between noisy and noiseless states. Numerical results for several example implementations are provided to reinforce our findings.


Robustness Verification of Quantum Machine Learning

Several important models of machine learning algorithms have been succes...

Fock State-enhanced Expressivity of Quantum Machine Learning Models

The data-embedding process is one of the bottlenecks of quantum machine ...

Optimal Provable Robustness of Quantum Classification via Quantum Hypothesis Testing

Quantum machine learning models have the potential to offer speedups and...

Quantum machine learning models are kernel methods

With near-term quantum devices available and the race for fault-tolerant...

Trainable Discrete Feature Embeddings for Variational Quantum Classifier

Quantum classifiers provide sophisticated embeddings of input data in Hi...

On compression rate of quantum autoencoders: Control design, numerical and experimental realization

Quantum autoencoders which aim at compressing quantum information in a l...

Toward Robust Autotuning of Noisy Quantum Dot Devices

The current autotuning approaches for quantum dot (QD) devices, while sh...

Please sign up or login with your details

Forgot password? Click here to reset