Quantum effects on machine learning

Yong Shi, Dan Chia-Tien Lo, Hongmei Chi +3 more

Quantum-based Machine Learning (QML) combines quantum computing (QC) with machine learning (ML), which can be applied in various sectors, and there is a high demand for QML professionals. However, QML is not yet in many schools' curricula. We design labware for the basic concepts of QC, ML, and QML and their applications in science and engineering fields in Google Colab, applying a three-stage learning strategy for efficient and effective student learning.

A. S. Wilson

Esma Aı̈meur, Gilles Brassard A, Sébastien Gambs

Zhenhua Jiang, Linh Nguyen

This paper presents a novel Quantum Probabilistic Learning Machine (QuProbLeM) paradigm that is fully based on Bayesian inference and well suited to directly learning the patterns or correlations inherent in dynamic processes from operation data by using a probabilistic neural network. In the QuProbLeM, the respective probability distributions of the weights capturing the input-to-output relationships are learnable parameters that can be learned from data samples. This revolutionary machine learning (ML) paradigm is inspired by the philosophy of Parts and the Total (e.g., each part, or data sample, in a closed system affects the total in such a way that the whole system will thereafter influence every single part). This first-of-the-type probabilistic learning machine can be effectively implemented on a quantum computing system that may also share the same philosophy naturally. The proposed QuProbLeM solver can take as its input a set of quantum-encoded data samples comprising pairs of cause-effect variables, and its output may include the updated probability distributions of the model's parameters (i.e., weights) and the predicted effect (output) variables and their distributions when given new cause (input) variables or their distributions. Alternatively, classical data can be first fed into a front-end Bayesian learning network to initially learn the probability distributions of the cause-effect variable pairs and then encoded into coherent quantum states as the input. Uniquely, the QuProbLeM solver directly computes probability distributions of the learning network's weights in an inference-based, entangled manner (but not iteratively as in traditional ML), by synthesizing the quantum training datasets, inferring about the model fidelity and updating the weight's probabilities based on inverse quantum operations.

Saroj Kumar Nanda, S. Arockia Panimalar, Sreekanth Rallapalli +3 more

In terms of service quality for transportation, security, robotics, healthcare, power, finance, etc., machine learning (ML) is effectively disrupting and modernizing cities. Even with their overwhelming success, machine learning (ML) algorithms still require significant computational work with fast computer hardware in order to handle model complexity and guarantees that result in effective, dependable, and robust solutions. Regarding cybersecurity and digital protection, quantum computing (QC) is a formidable contender to assist machine learning (ML) systems achieve optimal performance. The goal of this research is to provide the quantum Adaptive Neuro-fuzzy Inference System (QANFIS) model for smart control application distributed denial of service attack detection. The usefulness of our proposed model is demonstrated by evaluating it against an actual dataset of denial of service attack incidents. Lastly, various unresolved problems and difficulties with fitting ML with QC are discussed along with the findings.

Pranab Rai, Tegil J John, Josaiah Murfreal Dkhar