Trainable Discrete Feature Embeddings for Variational Quantum Classifier
- URL: http://arxiv.org/abs/2106.09415v1
- Date: Thu, 17 Jun 2021 12:02:01 GMT
- Title: Trainable Discrete Feature Embeddings for Variational Quantum Classifier
- Authors: Napat Thumwanit, Chayaphol Lortararprasert, Hiroshi Yano, Rudy Raymond
- Abstract summary: We show how to map discrete features with fewer quantum bits using Quantum Random Access Coding (QRAC)
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.
- Score: 4.40450723619303
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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