Trainable Discrete Feature Embeddings for Variational Quantum Classifier

06/17/2021
by   Napat Thumwanit, et al.
0

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.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

12/15/2020

VSQL: Variational Shadow Quantum Learning for Classification

Classification of quantum data is essential for quantum machine learning...
09/01/2020

Universal Approximation Property of Quantum Feature Map

Encoding classical inputs into quantum states is considered as a quantum...
03/21/2021

Quantum Machine Learning with HQC Architectures using non-Classically Simulable Feature Maps

Hybrid Quantum-Classical (HQC) Architectures are used in near-term NISQ ...
03/09/2021

Variational quantum policies for reinforcement learning

Variational quantum circuits have recently gained popularity as quantum ...
01/04/2022

Efficient Quantum Feature Extraction for CNN-based Learning

Recent work has begun to explore the potential of parametrized quantum c...
03/03/2020

Robust data encodings for quantum classifiers

Data representation is crucial for the success of machine learning model...
10/24/2021

Boson sampling discrete solitons by quantum machine learning

We use a neural network variational ansatz to compute Gaussian quantum d...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.