Top Research Papers on Quantum Machine Learning

Since the processing power of quantum computers is significantly higher than classical computers, it is expected to predict earthquakes accurately and give an early warning to alert the locals residing in that area.

A new method for Detecting fraud, combining quantum-inspired feature engineering and neural network modeling, which achieves a classification accuracy of 92%, with a precision and recall of 0.65 and recall of 0.68, demonstrating the promising use of quantum-enhanced machine learning for fraud detection.

The findings reveal that QML-IDS outperforms classical Machine Learning methods, demonstrating the promise of quantum-enhanced cybersecurity solutions for the age of quantum utility.

It is shown that the proposed QML-based demapping technique requires fewer qubits compared to existing techniques and achieves a lower estimated processing time than the classical demapping for higher constellation number.

This work describes the selection approach and analysis of existing AutoML frameworks regarding their capability of a) incorporating Quantum Machine Learning (QML) algorithms into this automated solving approach of the AutoML framing and b) solving a set of industrial use-cases with different ML problem types by benchmarking their most important characteristics. For that, available open-source tools are condensed into a market overview and suitable frameworks are systematically selected on a multi-phase, multi-criteria approach. This is done by considering software selection approaches, as wel...

This paper goes over the detailed design and framework of QMLS, including MLMG, MLMV, and QS, which aims to shorten the whole R&D phase to three to six months and decrease the cost to merely fifty to eighty thousand USD.

This work presents an operational semantics of QML programs using quantum circuits, and a denotational semantics using superoperators, and introduces the language QML, a functional language for quantum computations on finite types.

The findings suggest that the QSVM algorithm outperforms classical SVM on complex datasets, and the performance gap between quantum and classical models increases with dataset complexity, as simple models tend to overfit with complex datasets.

This work gives an example based on a quantum optical perceptron where the energy is dissipated as spontaneous emission at optical frequencies so this perceptron is as efficient as it is possible to get.

The distributed federated learning scheme demonstrated almost the same level of trained model accuracies and yet significantly faster distributed training, and demonstrates a promising future research direction for scaling and privacy aspects.

This strategy modifying the semantics on projections of products, by adding some rules to allow measurements, changes the semantics on projections of products.

An extension of the denotational semantic model of the quantum programming language QML, to which computational reversibility is incorporated, and a history track is incorporated which allows reversibility in QML.

It is argued that these challenges call for a critical debate on whether quantum advantage and the narrative of 'beating' classical machine learning should continue to dominate the literature the way it does, and examples for how other perspectives in existing research provide an important alternative to the focus on advantage.

This paper introduces a novel dataset based on concepts from quantum mechanics using the exponential map of a Lie algebra that will be made publicly available and contributes a novel contribution to the empirical evaluation of quantum supremacy.

These brief lecture notes cover the basics of neural networks and deep learning as well as their applications in the quantum domain, for physicists without prior knowledge.

It is demonstrated that a convolutional neural network-based algorithm successfully learns the Krylov spread complexity across all timescales, including the late-time plateaus where states appear nearly featureless and random.

The state of the art of quantum algorithms for financial applications, with particular focus to those use cases that can be solved via Machine Learning are presented.

An efficient way of deploying neuromorphic quantum computing for quantum machine learning may be enabled.

An automated search algorithm (QES), which derives optimal design of entangling layout for supervised quantum machine learning by establishing the connection between the structures of entanglement using CNOT gates and the representations of directed multi-graphs, enabling a well-defined search space.

Two quantum machine learning models that leverage the principles of quantum mechanics for effective computations are introduced, enabling the execution of computations even in the noisy intermediate-scale quantum era, where circuits with a large number of qubits are currently infeasible.

This work presents a simple, yet powerful, framework where the underlying invariances in the data are used to build QML models that, by construction, respect those symmetries that remain invariant under the action of any element of the symmetry group G associated to the dataset.

A comparative analysis of differences and similarities between classical and quantum fair machine learning algorithms is undertaken, specifying how the unique features of quantum computation alter measures, metrics and remediation strategies when quantum algorithms are subject to fairness constraints.

This doctoral thesis introduces a quantum generative adversarial network and a quantum Boltzmann machine implementation, both of which can be realized with parameterized quantum circuits and are compatible with first-generation quantum hardware.

This research clarifies the scope, intricacy, and scalability issues surrounding QNNs and offers insights into the possible advantages and difficulties of quantum-enhanced deep learning.

A quantum k-means algorithm with a low time complexity of $\mathcal{O}(NK log(D)I/C)$ to apply it to the fundamental problem of discriminating quantum states at readout and shows cross-talk in the (1, 2) and (2, 3) neighboring qubit couples for the analyzed device.

This tutorial intends to introduce readers with a background in AI to quantum machine learning (QML) -- a rapidly evolving field that seeks to leverage the power of quantum computers to reshape the landscape of machine learning. For self-consistency, this tutorial covers foundational principles, representative QML algorithms, their potential applications, and critical aspects such as trainability, generalization, and computational complexity. In addition, practical code demonstrations are provided in https://qml-tutorial.github.io/ to illustrate real-world implementations and facilitate hands-...

A parallel quantum eigensolver for solving a set of machine learning problems, which is based on quantum multi-resonant transitions that simultaneously trigger multiple energy transitions in the systems on demand, and is hardware efficient, such that it is implementable in near-term quantum computers.

The current state of quantum machine learning algorithms, including variational quantum algorithms, quantum neural networks, and quantum generative models are examined, and the importance of interdisciplinary collaboration in the continued advancement of this field is emphasized.

Inspired by XAI, the question of explainability of quantum circuits is raised by quantifying the importance of (groups of) gates for specific goals by transferring and adapting the well-established concept of Shapley values to the quantum realm.

It is found that the FRQI format not only provides the best classification accuracy compared to NEQR and other popular encoding methods but also is consistent across different circuit depths of QNN.

The fundamental principles of quantum metrology, potential applications, and recent advancements in quantum metrology assisted by machine learning are reviewed.

This monograph provides a self-contained introduction to quantum machine learning for an audience of engineers with a background in probability and linear algebra and describes the necessary background, concepts, and tools necessary to describe quantum operations and measurements.

A brief overview of the well-known techniques is presented but also their learning strategies using statistical physical insight to empower and promote cross-pollination among future research in all areas of chemistry which can benefit from ML and in turn can potentially accelerate the growth of such algorithms.

It is argued that one should instead employ quantum datasets composed of quantum states, and a novel method for generating multipartite entangled states is introduced, providing a use-case of quantum neural networks for quantum entanglement theory.

This perspective takes a different approach, aiming to harness the potential of noise and dissipation instead of combating them, and shows that these seemingly detrimental factors can provide substantial advantages in the operation of QML algorithms under certain circumstances.

A review of Quantum Machine Learning from the perspective of conventional techniques, and a set of basic algorithms for Quantum Machine learning, which are the fundamental components for quantum Machine Learning algorithms are discussed.

This work addresses the problem of efficiently compressing and loading classical data for use on a quantum computer, and proposes a hardware-efficient quantum circuit approach, which can achieve competitive accuracy with current tensor learning methods using only 11 qubits.

A new approach for quantum linear algebra based on quantum subspace states is introduced and three new quantum machine learning algorithms are presented that reduce exponentially the depth of circuits used in quantum topological data analysis.

An operational model is developed that incorporates a history track for the quantum programming language QML, considering classical and quantum data, and omitting measurements, that can be explicitly and naturally applied from the proposed rules.

The fundamental ideas and methods of quantum computing—including quantum gates, quantum circuits, and quantum algorithms—as they relate to machine learning are examined in this abstract.

This work proposes an alternative paradigm for the symmetry-informed construction of variational quantum circuits, based on homogeneous spaces, relaxing the overly stringent requirement of equivariance, and introduces horizontal quantum gates, which only transform the state with respect to the directions orthogonal to those of the symmetry.

This work introduces several probabilistic quantum algorithms that overcome the normal unitary restrictions in quantum machine learning by leveraging the Linear Combination of Unitaries (LCU) method, and proposes a general framework for applying a linear combination of irreducible subspace projections on quantum encoded data.

A link between quantum machine learning classification and quantum hypothesis testing is established and it is shown that the accuracy and generalization capability of quantum classifiers depend on the (R\'enyi) mutual informations between the quantum state space $Q$ and the classical parameter space $X$ or class space $C$.

This work presents the Scaled QUantum IDentifier (SQUID), an open-source framework for exploring hybrid Quantum-Classical algorithms for classification problems and highlights the implications for scalability for gradient-based optimization of quantum models on the choice of output for variational quantum models.

The concepts of quantum computing which influences machine learning in a quan- tum world are addressed and the speedup observed in different machine learning algorithms when executed on quantum computers is stated.

Novel blind quantum machine learning protocols based on the quantum bipartite correlator algorithm are introduced, introducing robust algorithm-specific privacy-preserving mechanisms with low computational overhead that do not require complex cryptographic techniques.

Light is shed on one of the major architectures considered to be predestined for variational quantum ML, how layouts like matrix product state, projected entangled pair states, tree tensor networks and multi-scale entanglement renormalization ansatz can be mapped to a quantum computer, and which implementation techniques improve their performance.

This paper introduces quantum computing over classical computation, followed by the recent tools and techniques developed in the area, and looks at multiple QML models like quantum kernel, quantum support vector machine (QSVM), etc.

It is demonstrated that the $\ensuremath{\phi}}^{4}$ scalar field theory satisfies the Hammersley-Clifford theorem, therefore recasting it as a machine learning algorithm within the mathematically rigorous framework of Markov random fields.

This work measures quantum kernels using randomized measurements to gain a quadratic speedup in computation time and quickly process large datasets and encodes high-dimensional data into quantum computers with the number of features scaling linearly with the circuit depth.