Incremental Data-Uploading for Full-Quantum Classification

05/06/2022
by   Maniraman Periyasamy, et al.
10

The data representation in a machine-learning model strongly influences its performance. This becomes even more important for quantum machine learning models implemented on noisy intermediate scale quantum (NISQ) devices. Encoding high dimensional data into a quantum circuit for a NISQ device without any loss of information is not trivial and brings a lot of challenges. While simple encoding schemes (like single qubit rotational gates to encode high dimensional data) often lead to information loss within the circuit, complex encoding schemes with entanglement and data re-uploading lead to an increase in the encoding gate count. This is not well-suited for NISQ devices. This work proposes 'incremental data-uploading', a novel encoding pattern for high dimensional data that tackles these challenges. We spread the encoding gates for the feature vector of a given data point throughout the quantum circuit with parameterized gates in between them. This encoding pattern results in a better representation of data in the quantum circuit with a minimal pre-processing requirement. We show the efficiency of our encoding pattern on a classification task using the MNIST and Fashion-MNIST datasets, and compare different encoding methods via classification accuracy and the effective dimension of the model.

READ FULL TEXT

Authors

page 1

page 3

01/10/2022

EP-PQM: Efficient Parametric Probabilistic Quantum Memory with Fewer Qubits and Gates

Machine learning (ML) classification tasks can be carried out on a quant...
07/12/2021

Fock State-enhanced Expressivity of Quantum Machine Learning Models

The data-embedding process is one of the bottlenecks of quantum machine ...
08/02/2021

Large-scale quantum machine learning

Quantum computers promise to enhance machine learning for practical appl...
08/13/2020

Single-Photon Image Classification

Quantum computing-based machine learning mainly focuses on quantum compu...
08/19/2020

The effect of data encoding on the expressive power of variational quantum machine learning models

Quantum computers can be used for supervised learning by treating parame...
05/23/2022

Overfitting in quantum machine learning and entangling dropout

The ultimate goal in machine learning is to construct a model function t...
This week in AI

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