Quantum Machine Learning: A Review and Case Studies

Abstract

Despite its undeniable success, classical machine learning remains a resource-intensive process. Practical computational efforts for training state-of-the-art models can now only be handled by high speed computer hardware. As this trend is expected to continue, it should come as no surprise that an increasing number of machine learning researchers are investigating the possible advantages of quantum computing. The scientific literature on Quantum Machine Learning is now enormous, and a review of its current state that can be comprehended without a physics background is necessary. The objective of this study is to present a review of Quantum Machine Learning from the perspective of conventional techniques. Departing from giving a research path from fundamental quantum theory through Quantum Machine Learning algorithms from a computer scientist’s perspective, we discuss a set of basic algorithms for Quantum Machine Learning, which are the fundamental components for Quantum Machine Learning algorithms. We implement the Quanvolutional Neural Networks (QNNs) on a quantum computer to recognize handwritten digits, and compare its performance to that of its classical counterpart, the Convolutional Neural Networks (CNNs). Additionally, we implement the QSVM on the breast cancer dataset and compare it to the classical SVM. Finally, we implement the Variational Quantum Classifier (VQC) and many classical classifiers on the Iris dataset to compare their accuracies.

Variational quantum circuits for convolution and window-based image processing applications

“…However, considering a 16 × 16 image with 8-bit resolution, even for optimized circuits, the circuit depth is still too high for quantum computers in the NISQ era. There are angle-based encoding methods, alternatively [15]. In angle-based methods, the inputs are normalized between the range [0-1] and then they are encoded [16].…”

Oncological Applications of Quantum Machine Learning

“…17 For instance, Grover & Shor's algorithms have demonstrated quadratic and exponential speed-up in exploring unstructured databases and solving integer factorization problems. 16,18 To enhance the speed and efficiency of ML relative to its classical computer counterparts, QML examines the designing and implementation of quantum software. 19 QML uses quantum algorithms as part of its implementation, potentially outperforming classical ML algorithms for specific problems.…”

“…These fundamental quantum phenomena pave the way for a novel information processing approach, potentially outperforming classical DL models. 16,20 AI has advanced significantly to mitigate the increasing prevalence of cancer and facilitate its prevention and early detection. Combining quantum computing with AI can herald a new era in data processing and digitization.…”

Quantum Machine Learning in Materials Prediction: A Case Study on ABO3 Perovskite Structures

“…The first step is to encode our classical data into a quantum state to create the input to be processed by a quantum computer. Several data encoding methods are used in QML, including the following. , Amplitude encoding: In amplitude encoding, classical data are encoded into the amplitudes of a quantum state. Quantum circuit encoding: Quantum circuit encoding involves constructing a quantum circuit that encodes classical data. Quantum embedding: Quantum embedding techniques aim to embed classical data into a quantum system by mapping the data points onto a quantum state. Quantum kernel methods: Quantum kernel methods employ quantum feature maps to implicitly encode classical data into quantum states. Quantum support vector machine (QSVM) encoding: QSVM encoding is specifically used for classification tasks. …”

“…computer. Several data encoding methods are used in QML, including the following 31,32. (i) Amplitude encoding:Inamplitude encoding, classical data are encoded into the amplitudes of a quantum state.…”