Machine Learning in a Quantum World

Zainab Abohashima, Mohamed Elhoseny, Essam Halim Houssein +1 more

Due to the superiority and noteworthy progress of Quantum Computing (QC) in a lot of applications such as cryptography, chemistry, Big data, machine learning, optimization, Internet of Things (IoT), Blockchain, communication, and many more. Fully towards to combine classical machine learning (ML) with Quantum Information Processing (QIP) to build a new field in the quantum world is called Quantum Machine Learning (QML) to solve and improve problems that displayed in classical machine learning (e.g. time and energy consumption, kernel estimation). The aim of this paper presents and summarizes a comprehensive survey of the state-of-the-art advances in Quantum Machine Learning (QML). Especially, recent QML classification works. Also, we cover about 30 publications that are published lately in Quantum Machine Learning (QML). we propose a classification scheme in the quantum world and discuss encoding methods for mapping classical data to quantum data. Then, we provide quantum subroutines and some methods of Quantum Computing (QC) in improving performance and speed up of classical Machine Learning (ML). And also some of QML applications in various fields, challenges, and future vision will be presented.

Niels M. P. Neumann, Frank Phillipson, R. Versluis

Sally Shrapnel, Gerard J Milburn, Michael J. Kewming

The failure of machine learning models to accurately capture causal relationships is becoming increasingly well understood, leading to an explosion of casually motivated machine learning techniques. In this talk we consider the quantum case: can we learn causal structure in a quantum world? We introduce a quantum causal machine learning framework and also consider the possibility of a quantum enhanced agent that learns via explicitly quantum interventions. The latter model of a quantum agent uses optical probes to learn about the external world in a manner that improves upon the classical analogue.

Charles R. Giardina

Maria Schuld, Francesco Petruccione

Ema Puljak

Dr. Ema Puljak Bio: Ema Puljak is a Quantum Algorithms Expert at PlanQC, working on quantum and quantum-inspired machine learning for real-world applications. She completed a collaborative PhD between CERN, the Barcelona Supercomputing Center, and Universitat Autònoma de Barcelona, focusing on improving anomaly detection methods using quantum and tensor-network techniques for medical imaging and high-energy physics. Passionate about science communication, she regularly delivers accessible talks and tutorials on quantum computing, tensor networks, and quantum machine learning. TITLE: Friendly Introduction to Quantum and Quantum-Inspired Machine Learning Abstract: This talk introduces quantum and quantum-inspired machine learning from a practical perspective. We start with a brief overview of classical ML, then move to quantum-inspired approaches based on tensor networks, which already enable efficient representations and compression on classical hardware. Next, we cover quantum machine learning, including variational circuits, quantum kernels, and hybrid quantum–classical workflows, highlighting where quantum models may assist (rather than replace) classical ML. We close with real-world application outlooks for both paradigms and open questions around scalability, and future perspectives, including whether demonstrating quantum advantage should be the only goal of quantum machine learning.