Operations on Quantum Physical Artificial Neural Structures

On this paper, we briefly analyze and compare some models of quantum artificial neural networks. Quantum operators must be linear ones; we verify that no unitary operators are used in two models of quantum perceptron. We also analyze a model of quantum weightless neural network and a quantum complex neural network. These models have quantum architecture and learning, but we show that they use nonlinear operators in the learning process. This study, toward a comparative method, tries to clarify important aspects in models of quantum neural networks as well as understand more its operation.

Information Sciences, 2000

We explore by simulation ways in which an array of quantum dot molecules could serve as a quantum neural computer. First, we show that a single quantum dot molecule evolving in real time can act as a recurrent temporal quantum neural network. Inputs are prepared by ®xing the initial states of a quantum dot molecule, and outputs determined by reading its value at a given time T later. The nodes of the network are the instantaneous states of the molecule at successive time slices. The nodes interact indirectly through their mutual interaction with local and phononic modes of the substrate. These modes can be preferentially excited optically, and, therefore, controlled externally. The number of excitations can thus be used as trainable``weight'' parameters for a neural network. This network is shown to perform classical logic gates. By preparing the input state as a superposition state, multiple inputs can be encoded as a single initial state. Second, we simulate the possibility of a spatial, rather than temporal, design, as a Hop®eld net. The network consists of a regular array of quantum dot molecules on a suitable substrate. The molecules interact indirectly as before, and, now, with each other directly through Coulombic interactions. Both of the quantum networks have none of the``wiring problems'' of traditional neural nets: the necessary connections are supplied by the physical system itself. Computation is performed by the intrinsic physics of the physical system. The long range character of the phononic interactions takes the net beyond traditional local connectionist structures. The hypothesized increase in

arXiv (Cornell University), 2002

An outstanding problem in quantum computing is the calculation of entanglement, for which no closed-form algorithm exists. Here we solve that problem, and demonstrate the utility of a quantum neural computer, by showing, in simulation, that such a device can be trained to calculate the entanglement of an input state, something neither an algorithmic quantum computer nor a classical neural net can do. Quantum neural network Two maximally entangled states are the Bell state, |00>+|11>, and the EPR state, |10>+|01>. Neither can be represented as a product state (a|1>+b|0>) A (c|1>+d|0>) B. They can be represented by specifying 4 c-numbers, but if we write the state as a product state not all those numbers are independent, since the product is equal to ac|11>+ad|10>+bc|01>+bd|00>. So, for example, with the Bell state, one cannot specify complex numbers a,b,c,d such that ac and bd are not zero but ad and bc are. This is also equivalent to saying [5] that the density matrix for

2021

Quantum machine learning emerges from the symbiosis of quantum mechanics and machine learning. In particular, the latter gets displayed in quantum sciences as: (i) the use of classical machine learning as a tool applied to quantum physics problems, (ii) or the use of quantum resources such as superposition, entanglement, or quantum optimization protocols to enhance the performance of classification and regression tasks compare to their classical counterparts. This paper reviews examples in these two scenarios. On the one hand, the application of classical neural network to design a new quantum sensing protocol. On the other hand, the design of a quantum neural network based on the dynamics of a quantum perceptron optimized with the aid of shortcuts to adiabaticity gives rise to a short operation time and robust performance. These examples demonstrate the mutual reinforcement of both neural networks and quantum physics.

Archives of Computational Methods in Engineering, 2018

Quantum neural network is a useful tool which has seen more development over the years mainly after twentieth century. Like artificial neural network (ANN), a novel, useful and applicable concept has been proposed recently which is known as quantum neural network (QNN). QNN has been developed combining the basics of ANN with quantum computation paradigm which is superior than the traditional ANN. QNN is being used in computer games, function approximation, handling big data etc. Algorithms of QNN are also used in modelling social networks, associative memory devices, and automated control systems etc. Different models of QNN has been proposed by different researchers throughout the world but systematic study of these models have not been done till date. Moreover, application of QNN may also be seen in some of the related research papers. As such, this paper includes different models which have been developed and further the implement of the same in various applications. In order to understand the powerfulness of QNN, few results and reasons are incorporated to show that these new models are more useful and efficient than traditional ANN.

ACM Computing Surveys

In recent years, Quantum Computing witnessed massive improvements in terms of available resources and algorithms development. The ability to harness quantum phenomena to solve computational problems is a long-standing dream that has drawn the scientific community’s interest since the late 80s. In such a context, we propose our contribution. First, we introduce basic concepts related to quantum computations, and then we explain the core functionalities of technologies that implement the Gate Model and Adiabatic Quantum Computing paradigms. Finally, we gather, compare and analyze the current state-of-the-art concerning Quantum Perceptrons and Quantum Neural Networks implementations.

2021

In recent years, Quantum Computing witnessed massive improvements both in terms of resources availability and algorithms development. The ability to harness quantum phenomena to solve computational problems is a long-standing dream that has drawn the scientific community’s interest since the late ’80s. In such a context, we pose our contribution. First, we introduce basic concepts related to quantum computations, and then we explain the core functionalities of technologies that implement the Gate Model and Adiabatic Quantum Computing paradigms. Finally, we gather, compare and analyze the current state-of-the-art concerning Quantum Perceptrons and Quantum Neural Networks implementations.

Data, 2022

The extraordinary advance in quantum computation leads us to believe that, in the not-too-distant future, quantum systems will surpass classical systems. Moreover, the field’s rapid growth has resulted in the development of many critical tools, including programmable machines (quantum computers) that execute quantum algorithms and the burgeoning field of quantum machine learning, which investigates the possibility of faster computation than traditional machine learning. In this paper, we provide a thorough examination of quantum computing from the perspective of a physicist. The purpose is to give laypeople and scientists a broad but in-depth understanding of the area. We also recommend charts that summarize the field’s diversions to put the whole field into context.

Research Square (Research Square), 2024

The capacity of quantum computing to tackle complex problems faster than general computers might lead to industry revolutions. However, actual implementation is problematic due to limited qubit coherence and inherent noise. Using Quantum Neural Networks (QNNs), hybrid quantum-classical algorithms successfully address optimization problems by combining the bene ts of both quantum and traditional computer paradigms. The interface layer, the classical layer, and the quantum layer make up the three fundamental parts of the suggested design. The architecture's performance is contrasted with existing methods to demonstrate its advantages in terms of speed, accuracy, and scalability. With the help of this innovative design, di cult issues that are outside the capabilities of conventional computers can now be tackled, offering a workable solution for issues with nance, logistics, and medication development.