Quantum Machine Learning (QML) models promise to have some computational (or quantum) advantage for classifying supervised datasets (e.g., satellite images) over some conventional Deep Learning (DL) techniques due to their expressive power via their local effective dimension. There are, however, two main challenges regardless of the promised quantum advantage: 1) Currently available quantum bits (qubits) are very small in number, while real-world datasets are characterized by hundreds of high-dimensional elements (i.e.features). Additionally, there is not a single unified approach for embedding real-world high-dimensional datasets in a limited number of qubits. 2) Some real-world datasets are too small for training intricate QML networks. Hence, to tackle these two challenges for benchmarking and validating QML networks on real-world, small, and high-dimensional datasets in one-go, we employ quantum transfer learning comprising a classical VGG16 layer and a multi-qubit QML layer. We use real-amplitude and strongly-entangling N-layer QML networks with and without data re-uploading layers as a multi-qubit QML layer, and evaluate their expressive power quantified by using their local effective dimension; the lower the local effective dimension of a QML network, the better its performance on unseen data. As datasets, we utilize Eurosat and synthetic datasets (i.e.easy-to-classify datasets), and an UC Merced Land Use dataset (i.e.a hard-to-classify dataset). Our numerical results show that the strongly-entangling N-layer QML network has a lower local effective dimension than the real-amplitude QML network and outperforms it on the hard-to-classify datasets. In addition, quantum transfer learning helps tackle the two challenges mentioned above for benchmarking and validating QML networks on real-world, small, and high-dimensional datasets.
Quantum Machine Learning (QML) models promise to have some computational (or quantum) advantage for classifying supervised datasets (e.g., satellite images) over some conventional Deep Learning (DL) techniques due to their expressive power via their local effective dimension. There are, however, two main challenges regardless of the promised quantum advantage: 1) Currently available quantum bits (qubits) are very small in number, while real-world datasets are characterized by hundreds of high-dimensional elements (i.e.features). Additionally, there is not a single unified approach for embedding real-world high-dimensional datasets in a limited number of qubits. 2) Some real-world datasets are too small for training intricate QML networks. Hence, to tackle these two challenges for benchmarking and validating QML networks on real-world, small, and high-dimensional datasets in one-go, we employ quantum transfer learning comprising a classical VGG16 layer and a multi-qubit QML layer. We use real-amplitude and strongly-entangling N-layer QML networks with and without data re-uploading layers as a multi-qubit QML layer, and evaluate their expressive power quantified by using their local effective dimension; the lower the local effective dimension of a QML network, the better its performance on unseen data. As datasets, we utilize Eurosat and synthetic datasets (i.e.easy-to-classify datasets), and an UC Merced Land Use dataset (i.e.a hard-to-classify dataset). Our numerical results show that the strongly-entangling N-layer QML network has a lower local effective dimension than the real-amplitude QML network and outperforms it on the hard-to-classify datasets. In addition, quantum transfer learning helps tackle the two challenges mentioned above for benchmarking and validating QML networks on real-world, small, and high-dimensional datasets.
Abstract This paper presents, via an explicit example with a real-world dataset, a hands-on introduction to the field of quantum machine learning (QML). We focus on the case of learning with a single qubit, using data re-uploading techniques. After a discussion of the relevant background in quantum computing and machine learning we provide a thorough explanation of the data re-uploading models that we consider, and implement the different proposed formulations in toy and real-world datasets using the qiskit quantum computing SDK. We find that, as in the case of classical neural networks, the number of layers is a determining factor in the final accuracy of the models. Moreover, and interestingly, the results show that single-qubit classifiers can achieve a performance that is on-par with classical counterparts under the same set of training conditions. While this cannot be understood as a proof of the advantage of quantum machine learning, it points to a promising research direction, and raises a series of questions that we outline.
Quantum machine learning, an important element of quantum computing, recently has gained research attention around the world. In this paper, we have proposed a quantum machine learning model to classify images using a quantum classifier. We exhibit the results of a comprehensive quantum classifier with transfer learning applied to image datasets in particular. The work uses hybrid transfer learning technique along with the classical pre-trained network and variational quantum circuits as their final layers on a small scale of dataset. The implementation is carried out in a quantum processor of a chosen set of highly informative functions using PennyLane a cross-platform software package for using quantum computers to evaluate the high-resolution image classifier. The performance of the model proved to be more accurate than its counterpart and outperforms all other existing classical models in terms of time and competence.
Remote sensing imagery classification remains a formidable challenge, primarily attributed to the high dimensionality and complexity inherent in the data. While deep learning has demonstrated efficacy in this domain, its computational demands and reliance on extensive training data pose significant hurdles. This research investigates how quantum computing could be able to overcome these constraints and improve deep learning’s capacity for classifying remote sensing data. The study provides an extensive review of recent developments in quantum deep learning techniques specifically tailored for remote sensing applications. Various quantum algorithms designed for image feature extraction and classification are discussed, shedding light on their potential to revolutionize the field. As a practical demonstration, the chapter presents a case study focused on leveraging quantum deep learning for land cover classification using Sentinel-2 imagery. This chapter’s main focus is on hybrid quantum convolutional neural networks (QCNNs) based on circuits as effective tools for remote sensing picture classification. These hybrid models integrate quantum layers within traditional neural networks, introducing a novel paradigm for image analysis. To evaluate the efficacy of this approach, the proposed QCNN architecture is applied to land-use and land-cover classification, employing the Euro SAT dataset as a benchmark.
Quantum machine learning (QML) promises computational advantages for complex learning tasks, but identifying which datasets stand to benefit remains an open question. The recently proposed bit-bit encoding scheme encodes both inputs and outputs as bitstrings, which leads to quantum models that are universal approximators. Under bit-bit encoding, the number of input and output pairs increases exponentially with the number of qubits, which allows the calculation of the number of qubits required to fully represent a dataset. Datasets that can be covered with fewer than 50 qubits are unlikely to benefit from quantum advantage, as they are classically simulable. We use bit-bit encoding to perform a resource estimation study on both synthetic and real-world classification datasets. On synthetic data, we analyze how qubit requirements scale with the number of features and samples. On real datasets, we compare qubit requirements across different classical dimensionality reduction schemes. We find that all tested datasets require 49 qubits or fewer for full coverage, regardless of dimensionality reduction method. This suggests that standard, medium-sized, single-label classification datasets are unlikely to see performance gains from QML. In future work, we will explore more complex data types, such as multi-label, sequential, and regression, that may require more than 50 qubits for coverage and could therefore be promising candidates for quantum advantage.
The classical-quantum transfer learning (CQTL) method is introduced to address the challenge of training large-scale, high-resolution image data on a limited number of qubits (ranging from tens to hundreds) in the current Noisy Intermediate-Scale quantum (NISQ) era. existing CQTL frameworks have been demonstrate quantum advantages with a small number of parameters (around 50), but the performance of quantum neural networks is sensitive to the number of parameters. Currently, there is a lack of exploration into larger-scale quantum circuits with more parameters. This paper proposes an amplitude-encoding-based classical-quantum transfer learning (AE-CQTL) framework, accompanied by an effective learning algorithm. The AE-CQTL framework multiplies the parameters of quantum circuits by using multi-layer ansatz. Based on the AE-CQTL framework, we designed and implemented two CQTL neural network models: Transfer learning Quantum Neural Network (TLQNN) and Transfer Learning Quantum Convolutional Neural Network (TLQCNN). Both models significantly expand the parameter capacity of quantum circuits, elevating the parameter scale from a few dozen to over one hundred parameters. In cross-experiments with three benchmark datasets (MNIST, Fashion-MNIST and CIFAR10) and three source models (ResNet18, ResNet50 and DenseNet121), TLQNN and TLQCNN have exceeded the benchmark classical classifier in multiple performance metrics, including accuracy, convergence, stability, and generalization capability. Our work contributes to advancing the application of classical-quantum transfer learning on larger-scale quantum devices in future.
Abstract Quantum machine learning (QML) is an emerging field that investigates the capabilities of quantum computers for learning tasks. While QML models can theoretically offer advantages such as exponential speed-ups, challenges in data loading and the ability to scale to relevant problem sizes have prevented demonstrations of such advantages on practical problems. In particular, the encoding of arbitrary classical data into quantum states usually comes at a high computational cost, either in terms of qubits or gate count. However, real-world data typically exhibits some inherent structure (such as image data) which can be leveraged to load them with a much smaller cost on a quantum computer. This work further develops an efficient algorithm for finding low-depth quantum circuits to load classical image data as quantum states. To evaluate its effectiveness, we conduct systematic studies on the MNIST, Fashion-MNIST, CIFAR-10, and Imagenette datasets. The corresponding circuits for loading the full large-scale datasets are available publicly as PennyLane datasets and can be used by the community for their own benchmarks. We further analyze the performance of various quantum classifiers, such as quantum kernel methods, parameterized quantum circuits, and tensor-network classifiers, and we compare them to convolutional neural networks. In particular, we focus on the performance of the quantum classifiers as we introduce nonlinear functions of the input state, e.g. by letting the circuit parameters depend on the input state.