1,720,977 research outputs found
Quantum Machine Learning: Perspectives in Cybersecurity
Full text linkIn this work, we give an overview on some recent results related to quantum machine learning (QML) regarding the training of quantum generative adversarial neural networks by means of classical shadows, and a parametric model implemented on a quantum annealer. Then, we argue that QML models can be robust against targeted data corruption and gradient-based attacks, motivating the exploration of the relationship between QML and cybersecurity
Entanglement, CP-Maps and Quantum Communications
No full textIn this chapter we review the employment of quantum entanglement as a resource for information processing and transmission. In particular we introduce and discuss the notion of completely positive maps operating on observable algebras of physical systems in order to have a model to construct communication channels based on quantum processes. Then we discuss advantages and limitations of entanglement-assisted quantum communication schemes like quantum teleportation and dense coding
An efficient geometric approach to quantum-inspired classifications
Full text linkOptimal measurements for the discrimination of quantum states are useful tools for classification problems. In order to exploit the potential of quantum computers, feature vectors have to be encoded into quantum states represented by density operators. However, quantum-inspired classifiers based on nearest mean and on Helstrom state discrimination are implemented on classical computers. We show a geometric approach that improves the efficiency of quantum-inspired classification in terms of space and time acting on quantum encoding and allows one to compare classifiers correctly in the presence of multiple preparations of the same quantum state as input. We also introduce the nearest mean classification based on Bures distance, Hellinger distance and Jensen–Shannon distance comparing the performance with respect to well-known classifiers applied to benchmark datasets
Scalable quantum neural networks by few quantum resources
Full text linkThis paper focuses on the construction of a general parametric model that can
be implemented executing multiple swap tests over few qubits and applying a
suitable measurement protocol. The model turns out to be equivalent to a
two-layer feedforward neural network which can be realized combining small
quantum modules. The advantages and the perspectives of the proposed quantum
method are discussed.Comment: 14 page
Quantum Concentration Inequalities and Equivalence of the Thermodynamical Ensembles: An Optimal Mass Transport Approach
No full textWe prove new concentration inequalities for quantum spin systems which apply to any local observable measured on any product state or on any state with exponentially decaying correlations. Our results do not require the spins to be arranged in a regular lattice, and cover the case of observables that contain terms acting on spins at arbitrary distance. Moreover, we introduce a local W1 distance, which quantifies the distinguishability of two states with respect to local observables. We prove a transportation-cost inequality stating that the local W1 distance between a generic state and a state with exponentially decaying correlations is upper bounded by a function of their relative entropy. Finally, we apply such inequality to prove the equivalence between the canonical and microcanonical ensembles of quantum statistical mechanics and the weak eigenstate thermalization hypothesis for the Hamiltonians whose Gibbs states have exponentially decaying correlations
A general learning scheme for classical and quantum Ising machines
Full text linkAn Ising machine is any hardware specifically designed for finding the ground
state of the Ising model. Relevant examples are coherent Ising machines and
quantum annealers. In this paper, we propose a new machine learning model that
is based on the Ising structure and can be efficiently trained using gradient
descent. We provide a mathematical characterization of the training process,
which is based upon optimizing a loss function whose partial derivatives are
not explicitly calculated but estimated by the Ising machine itself. Moreover,
we present some experimental results on the training and execution of the
proposed learning model. These results point out new possibilities offered by
Ising machines for different learning tasks. In particular, in the quantum
realm, the quantum resources are used for both the execution and the training
of the model, providing a promising perspective in quantum machine learning.Comment: 25 pages, 9 figures, updated to the version published on SciPos
A quantum k-nearest neighbors algorithm based on the Euclidean distance estimation
Full text linkThe k-nearest neighbors (k-NN) is a basic machine learning (ML) algorithm, and several quantum versions of it, employing different distance metrics, have been presented in the last few years. Although the Euclidean distance is one of the most widely used distance metrics in ML, it has not received much consideration in the development of these quantum variants. In this article, a novel quantum k-NN algorithm based on the Euclidean distance is introduced. Specifically, the algorithm is characterized by a quantum encoding requiring a low number of qubits and a simple quantum circuit not involving oracles, aspects that favor its realization. In addition to the mathematical formulation and some complexity observations, a detailed empirical evaluation with simulations is presented. In particular, the results have shown the correctness of the formulation, a drop in the performance of the algorithm when the number of measurements is limited, the competitiveness with respect to some classical baseline methods in the ideal case, and the possibility of improving the performance by increasing the number of measurements
Classical shadows meet quantum optimal mass transport
Full text linkClassical shadows constitute a protocol to estimate the expectation values of a collection of M observables acting on O(1) qubits of an unknown n-qubit state with a number of measurements that is independent of n and that grows only logarithmically with M. We propose a local variant of the quantum Wasserstein distance of order 1 of [De Palma et al., IEEE Trans. Inf. Theory 67, 6627 (2021)] and prove that the classical shadow obtained measuring O(log n) copies of the state to be learned constitutes an accurate estimate with respect to the proposed distance. We apply the results to quantum generative adversarial networks, showing that quantum access to the state to be learned can be useful only when some prior information on such state is available.29 page
Implementation and Empirical Evaluation of a Quantum Machine Learning Pipeline for Local Classification
Full text linkIn the current era, quantum resources are extremely limited, and this makes
difficult the usage of quantum machine learning (QML) models. Concerning the
supervised tasks, a viable approach is the introduction of a quantum locality
technique, which allows the models to focus only on the neighborhood of the
considered element. A well-known locality technique is the k-nearest neighbors
(k-NN) algorithm, of which several quantum variants have been proposed.
Nevertheless, they have not been employed yet as a preliminary step of other
QML models, whereas the classical counterpart has already proven successful. In
this paper, we present (i) an implementation in Python of a QML pipeline for
local classification, and (ii) its extensive empirical evaluation.
Specifically, the quantum pipeline, developed using Qiskit, consists of a
quantum k-NN and a quantum binary classifier. The results have shown the
quantum pipeline's equivalence (in terms of accuracy) to its classical
counterpart in the ideal case, the validity of locality's application to the
QML realm, but also the strong sensitivity of the chosen quantum k-NN to
probability fluctuations and the better performance of classical baseline
methods like the random forest.Comment: 33 pages, 8 figures, 9 table
- …
