Review of some existing QML frameworks and novel hybrid classical–quantum neural networks realising binary classification for the noisy datasets

Abstract

One of the most promising areas of research to obtain practical advantage is Quantum Machine Learning which was born as a result of cross-fertilisation of ideas between Quantum Computing and Classical Machine Learning. In this paper, we apply Quantum Machine Learning (QML) frameworks to improve binary classification models for noisy datasets which are prevalent in financial datasets. The metric we use for assessing the performance of our quantum classifiers is the area under the receiver operating characteristic curve AUC–ROC. By combining such approaches as hybrid-neural networks, parametric circuits, and data re-uploading we create QML inspired architectures and utilise them for the classification of non-convex 2 and 3-dimensional figures. An extensive benchmarking of our new FULL HYBRID classifiers against existing quantum and classical classifier models, reveals that our novel models exhibit better learning characteristics to asymmetrical Gaussian noise in the dataset compared to known quantum classifiers and performs equally well for existing classical classifiers, with a slight improvement over classical results in the region of the high noise.

Physics‐Aware Analytic‐Gradient Training of Photonic Neural Networks

“…Additionally, our method utilizes differentiable DNN to represent non‐differentiable PNN, streamlining the training process and reducing costs in hybrid photonic‐digital networks. These results demonstrate a promising path toward the development of highly efficient and accurate PNNs, with potential applications in optimization, [ 35 ] cryptography, [ 36 ] quantum machine learning, [ 37,38 ] quantum finance, [ 39–41 ] cascaded photonic neural network, [ 42 ] etc.…”

Quantum Graph Neural Network Models for Materials Search

“…An interesting variant of QNNs is the hybrid classical–quantum neural network [ 54 , 70 , 71 , 72 , 73 , 74 ]. Since one can calculate gradients for the QNN layers [ 75 , 76 , 77 ], it is possible to create a neural network with classical and quantum layers.…”

An exponentially-growing family of universal quantum circuits

“…It was argued [3] that such quantum neural networks (QNN) could have higher trainability, capacity, and generalization bound than their classical counterparts. Practically, hybrid quantum neural networks (HQNN) have shown promise in small-scale benchmarking tasks [4][5][6][7] and larger-scale industrial tasks [8][9][10]. Nevertheless, the utility, practicality, and scalability of pure QNNs are still unclear.…”

“…This work primarily focuses on angle embedding. Nevertheless, it is worth noting that in the circuit model, all circuit parameters enter as angles at some level of the description 6. Extra gates to convert between different generators can be absorbed into the variational gates-see[15] 7.…”