Trainable Discrete Feature Embeddings for Variational Quantum Classifier
This work addresses the challenge of efficiently encoding discrete data for quantum machine learning, offering a method that could enhance the utility of near-term quantum computers, though it appears incremental as it builds on existing QRAC and metric learning techniques.
The authors tackled the problem of embedding discrete features in variational quantum classifiers by proposing a trainable quantum circuit that combines Quantum Random Access Coding (QRAC) with quantum metric learning, resulting in improved classification performance on real-world datasets with fewer qubits.
Quantum classifiers provide sophisticated embeddings of input data in Hilbert space promising quantum advantage. The advantage stems from quantum feature maps encoding the inputs into quantum states with variational quantum circuits. A recent work shows how to map discrete features with fewer quantum bits using Quantum Random Access Coding (QRAC), an important primitive to encode binary strings into quantum states. We propose a new method to embed discrete features with trainable quantum circuits by combining QRAC and a recently proposed strategy for training quantum feature map called quantum metric learning. We show that the proposed trainable embedding requires not only as few qubits as QRAC but also overcomes the limitations of QRAC to classify inputs whose classes are based on hard Boolean functions. We numerically demonstrate its use in variational quantum classifiers to achieve better performances in classifying real-world datasets, and thus its possibility to leverage near-term quantum computers for quantum machine learning.