Various models of quantum neural networks exist imitating the powerful class of machine learning algorithms, widely applied and used in many of intelligent systems and applications. While comparative models of quantum neural networks exist, their computational complexity might require specific unitary transforms for simulating the activation function of the cell, simulation of continuous processes for learning or adding a large amount of ancilla qubits. In order to solve some of these problems, we present a quantum neural network model called CNOT Measured Network (CMN). The CMN uses only CNOT quantum gates and the measurement operator and as such is very simple to implement in any quantum computer technology. The CMN can by using only these two simple operators, result in a Turing universal operators AND and OR while keeping the learning speed optimized to the complex nature of the quantum network and a constant number of ancila qubits.
Various models of quantum neural networks exist imitating the powerful class of machine learning algorithms, widely applied and used in many of intelligent systems and applications. While comparative models of quantum neural networks exist, their computational complexity might require specific unitary transforms for simulating the activation function of the cell, simulation of continuous processes for learning or adding a large amount of ancilla qubits. In order to solve some of these problems, we present a quantum neural network model called CNOT Measured Network (CMN). The CMN uses only CNOT quantum gates and the measurement operator and as such is very simple to implement in any quantum computer technology. The CMN can by using only these two simple operators, result in a Turing universal operators AND and OR while keeping the learning speed optimized to the complex nature of the quantum network and a constant number of ancila qubits.
Despite rapid theoretical and practical progress, machine learning algorithms require large computational resources. However, new opportunities are opening up due to the advent of quantum computing devices that directly use the laws of quantum mechanics to bypass the technological and thermodynamic limitations of classical computing. Taking into account that artificial neural networks are actively used in solving a number of practical problems, with the advent of quantum computing technologies, the development of quantum neural networks for the implementation of quantum machine learning is very relevant. The article considers a quantum analogue of classical neurons that form quantum neural networks capable of performing universal quantum computations.
One of the most promising new directions in quantum computing and artificial intelligence convergence is the Quantum Neural Networks (QNNs) that can potentially revolutionize the manner in which intelligent systems are designed, trained, and implemented. Although traditional deep learning models have proven to be incredibly successful in fields like computer vision, natural language processing and autonomous systems, they are becoming limited by computational bottlenecks, energy use, and scalability constraints when operating on large high-dimensional data. The distinctive principles of quantum mechanics (superposition, entanglement, and quantum parallelism) may be applied to the QNNs to provide a distinct computational paradigm with the ability to execute exponentially complicated computations more effectively than his classical counterparts. This article gives an in-depth analysis of QNNs as the new frontier of artificial intelligence. We start with the description of the theoretical foundations of quantum computation and their realization in the form of neural networks. We then examine state-of-the-art QNN architectures, such as hybrid quantum-classical models that help overcome present quantum hardware limitations in relation to the capabilities of classical machine learning. Some of the upcoming applications are showcased including molecular simulation and drug discovery and financial modeling, optimization, cryptography and intelligent automation. Such applications reveal the revolutionary power of QNNs to tackle the issues that cannot be tackled by traditional systems. Nonetheless, there are also major obstacles on the way to a practical implementation of QNN. Problems that still remain a barrier to widespread adoption include quantum decoherence, noise in near-term quantum devices, and the inability to scale qubit architectures or to train large-scale quantum models. In order to overcome these obstacles, we speak about the current research work on the topic of error correction, variational algorithms, and hybrid system designs. Also we examine the ethical and societal ramifications of quantum-accelerated AI, such as security, data sovereignty, and technological equity globally.
Abstract The field of artificial neural networks is expected to strongly benefit from recent developments of quantum computers. In particular, quantum machine learning, a class of quantum algorithms which exploit qubits for creating trainable neural networks, will provide more power to solve problems such as pattern recognition, clustering and machine learning in general. The building block of feed-forward neural networks consists of one layer of neurons connected to an output neuron that is activated according to an arbitrary activation function. The corresponding learning algorithm goes under the name of Rosenblatt perceptron. Quantum perceptrons with specific activation functions are known, but a general method to realize arbitrary activation functions on a quantum computer is still lacking. Here, we fill this gap with a quantum algorithm which is capable to approximate any analytic activation functions to any given order of its power series. Unlike previous proposals providing irreversible measurement–based and simplified activation functions, here we show how to approximate any analytic function to any required accuracy without the need to measure the states encoding the information. Thanks to the generality of this construction, any feed-forward neural network may acquire the universal approximation properties according to Hornik’s theorem. Our results recast the science of artificial neural networks in the architecture of gate-model quantum computers.
Quantum neural networks (QNNs) represent a cutting-edge approach to machine learning and artificial intelligence, harnessing the principles of quantum mechanics to process and analyze data in fundamentally new ways. Unlike classical neural networks, which operate on classical bits and are limited by the laws of classical physics, QNNs use quantum bits (qubits) and exploit quantum phenomena such as superposition and entanglement to achieve unprecedented computational power and efficiency. This chapter explores the principles and applications of QNNs, including their potential for solving complex optimisation problems, pattern recognition tasks, and quantum simulation tasks. We discuss various QNN architectures, training algorithms, and applications, highlighting their advantages and challenges compared to classical neural networks. Additionally, we explore the potential impact of QNNs on fields such as quantum computing, quantum communication, and quantum sensing, as well as their implications for society and the economy. Overall, this chapter provides insights into the emerging field of quantum neural networks and sets the stage for further research and innovation in this exciting area.
Most proposals for quantum neural networks have skipped over the implementation of the Qubit, superposition, entanglement and measurement in order to be used in MATLAB environment. Quantum computing uses unitary operators acting on discrete state vectors. Matlab is a well-known (classical) matrix computing environment, which makes it well suited for simulating quantum algorithms. The Quantum Computing Function (QCF) library extends Matlab by adding functions to represent and visualize common quantum operations. On the other hand a new mathematical model of computation called Quantum Neural Networks (QNNs) is defined, building on Deutsch's model of quantum computational network. The Quantum Neural Network (QNN) model began in order to combine quantum computing with the striking properties of neural computing. In this paper the use and importance of those functions is illustrated with the help of few examples. This paper presents a brief overview of QCF that how it can be useful in Quantum Neural Network simulation.
Universality of neural networks describes the ability to approximate arbitrary function, and is a key ingredient to keep the method effective. The established models for universal quantum neural networks(QNN), however, require the preparation of multiple copies of the same quantum state to generate the nonlinearity, with the copy number increasing significantly for highly oscillating functions, resulting in a huge demand for a large-scale quantum processor. To address this problem, we propose a new QNN model that harbors universality without the need of multiple state-duplications, and is more likely to get implemented on near-term devices. To demonstrate the effectiveness, we compare our proposal with two popular QNN models in solving typical supervised learning problems. We find that our model requires significantly fewer qubits and it outperforms the other two in terms of accuracy and relative error.