Quantum pattern recognition with multi-neuron interactions

Procedia Engineering, 2014

The advances that have been achieved in quantum computer science to date, slowly but steadily find their way into the field of artificial intelligence. Specifically the computational capacity given by quantum parallelism, resulting from the quantum linear superposition of quantum physical systems, as well as the entanglement of quantum bits seem to be promising for the implementation of quantum artificial neural networks. Within this elaboration, the required information processing from bit-level up to the computational neuroscience-level is explained in detail, based on the combined research in the fields of quantum physics and artificial neural systems.

Recently with the rapid development of technology, there are a lot of applications require to achieve learning with low-cost in order to accomplish inexpensive computation. However the known computational power of classical artificial neural networks (CANN), they are not capable to provide low-cost learning due to many reasons such as linearity, complexity of architecture, etc. In contrast, quantum neural networks (QNN), or neural networks inspired quantum computing, may be representing a good computational alternate to CANN, based on the computational power of quantum bit (qubit) over the classical bit. In this paper, a new algorithm of perceptron neural network inspired quantum computing based only on one neuron is introduced to overcome some limitations of the classical perceptron neural networks. The proposed algorithm is capable to construct its own set of activation operators that enough to accomplish the learning process after only one iteration autonomously and, consequently, reduces the cost of computation. For evaluation purpose, we utilize the proposed algorithm to solve five different problems using real and artificial data. It is shown throughout the paper that promising results are provided and compared favorably with other reported algorithms. keyword: Artificial neural networks and Quantum computing and Quantum neural networks

“Quing: International Journal of Innovative Research in Science and Engineering, 2023

In recent years, quantum computing has emerged as a potentially gamechanging technology, with applications across various disciplines, including AI and machine learning. In recent years, the combination of quantum computing and neural networks has led to the development of quantum neural networks (QNNs). This paper explores the potential of QNNs and their applications in solving complex problems that are challenging for classical neural networks. This paper explores the fundamental principles of quantum computing, the architecture of QNNs, and their advantages over classical neural networks. Furthermore, this will highlight key research areas and challenges in the development and utilization of QNNs. Through an in-depth analysis, it demonstrates the QNNs hold significant promise for addressing complex computational problems and advancing the field of artificial intelligence.

Engineering Applications of Artificial Intelligence, 2007

We study a quantum neural network with superposed qubits replacing classical neurons with deterministic states, and also with quantum gate operators in place of the classical action potentials observed in biological contexts. With our choice of logic gates interconnecting the neural lattice, we find that the state of the system behaves in ways reflecting both the strength of coupling between neurons as well as the initial conditions, and depending on whether there is a threshold for emission from excited to ground state, the system shows either chaotic oscillations or coherent ones with periodicity that depends on the strength of coupling in a unique way. The spatial pattern of the initial input affects the subsequent dynamic behavior of the system in an interesting unambiguous way, which indicates that it can serve as a dynamic memory system analogous to biological ones, but with an unlimited lifetime.

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.

1998

Quantum computation uses microscopic quantum level effects to perform computational tasks and has produced results that in some cases are exponentially faster than their classical counterparts by taking advantage of quantum parallelism. The unique characteristics of quantum theory may also be used to create a quantum associative memory with a capacity exponential in the number of neurons. This paper covers necessary high-level quantum mechanical ideas and introduces a simple quantum associative memory. Further, it provides discussion, empirical results and directions for future work.

Physical Review Letters, 2006

Qubit networks with long-range interactions inspired by the Hebb rule can be used as quantum associative memories. Starting from a uniform superposition, the unitary evolution generated by these interactions drives the network through a quantum phase transition at a critical computation time, after which ferromagnetic order guarantees that a measurement retrieves the stored pattern. The maximum memory capacity of these qubit networks is reached at a memory density alpha=p/n=1.

2016

Recently, with the rapid development of technology, there are a lot of applications require to achieve low-cost learning. However the computational power of classical artificial neural networks, they are not capable to provide low-cost learning. In contrast, quantum neural networks may be representing a good computational alternate to classical neural network approaches, based on the computational power of quantum bit (qubit) over the classical bit. In this paper we present a new computational approach to the quantum perceptron neural network can achieve learning in low-cost computation. The proposed approach has only one neuron can construct self-adaptive activation operators capable to accomplish the learning process in a limited number of iterations and, thereby, reduce the overall computational cost. The proposed approach is capable to construct its own set of activation operators to be applied widely in both quantum and classical applications to overcome the linearity limitation of classical perceptron. The computational power of the proposed approach is illustrated via solving variety of problems where promising and comparable results are given.

1997

Quantum computation uses microscopic quantum level effects to perform computational tasks and has produced results that in some cases are exponentially faster than their classical counterparts. Choosing the best weights for a neural network is a time consuming problem that makes the harnessing of this "quantum parallelism" appealing. This paper briefly covers necessary high-level quantum theory and introduces a model for a quantum neuron.

Neural Networks, 2016

In this work, we propose a quantum neural network named quantum perceptron over a field (QPF). Quantum computers are not yet a reality and the models and algorithms proposed in this work cannot be simulated in actual (or classical) computers. QPF is a direct generalization of a classical perceptron and solves some drawbacks found in previous models of quantum perceptrons. We also present a learning algorithm named Superposition based Architecture Learning algorithm (SAL) that optimizes the neural network weights and architectures. SAL searches for the best architecture in a finite set of neural network architectures with linear time over the number of patterns in the training set. SAL is the first learning algorithm to determine neural network architectures in polynomial time. This speedup is obtained by the use of quantum parallelism and a non-linear quantum operator.

In this paper we consider a Quantum computational algorithm that can be used to determine (probabilistically) how close a given signal is one of a set of previously observed signals stored in the state of a quantum neurocomputional machine. The realization of a new quantum algorithm for factorization of integers by Shor and its implication to cryptography has created a rapidly growing field of investigation. Although no physical realization of quantum computers is available, a number of softwar systems simulating a quantum computation process exist. In light of the rapidly increasing power of desktop computers and their ability to carry out these simulations, it is worthwhile to investigate possible advantages as well as realizations of quantum algorithms in signal processing applications. The algorithm presented in this paper offers a glimpse of the potentials of this approach. Neural Networks (NN) provide a natural paradigm for parallel and distributed processing of a wide class of signals. Neural Networks within the context of classical computation have been used for approximation and classification tasks with some success. In this paper we propose a model for Quantum Neurocomputation (QN) and explore some of its properties and potential applications to signal processing in an information-theoretic context. A Quantum Computer can evolve a coherent superposition of many possible input states, to an output state through a series of unitary transformations that simultaneously affect each element of the superposition. This construction generates a massively parallel data processing system existing within a single piece of hardware. Our model of QN consists of a set of Quantum Neurons and Quantum interconnections. Quantum neurons represent a normalized element of the n-dimensional Hilbert space -a state of a finite dimensional quantum mechanical system. Quantum connections provide a realization of probability distribution over the set of state that combined with the Quantum Neurons provide a densit matrix representation of the system. A second layer with a similar architecture interrogates the system through a series of random state descriptions to obtain an average state description. We discuss the application of this paradigm to the quantum analog of independent states using the quantum version of the Kullback-Leibler distance.

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

Symmetry

The problem of pattern classification in quantum data has been of great importance over the past few years. This study investigates the effect of deploying Grover’s, the partial diffusion, and the fixed-phase algorithms separately to amplify the amplitudes of a desired pattern in an unstructured dataset. These quantum search operators were applied to symmetric and antisymmetric input superpositions on a three-qubit system for 20 iterations each. After each iteration, different probabilities of classification were calculated in order to determine the accuracy of classification for each of the three quantum search operators. The results indicated that, in the case of applying the three quantum search operators to incomplete superposition input states, the partial diffusion operator outperformed the other operators with a probability of correct classification that reached 100% in certain iterations. It also showed that the classification accuracy of the fixed-phase operator exceeded th...

ArXiv, 2015

Recently with the rapid development of technology, there are a lot of applications require to achieve low-cost learning in order to accomplish inexpensive computation. However the known computational power of classical artificial neural networks (CANN), they are not capable to provide low-cost learning due to many reasons such as linearity, complexity of architecture, etc. In contrast, quantum neural networks (QNN) may be representing a good computational alternate to CANN, based on the computational power of quantum bit (qubit) over the classical bit. In this paper, a new algorithm of quantum perceptron neural network based only on one neuron is introduced to overcome some limitations of the classical perceptron neural networks. The proposed algorithm is capable to construct its own set of activation operators that enough to accomplish the learning process in a limited number of iterations and, consequently, reduces the cost of computation. For evaluation purpose, we utilize the pr...

Information Sciences

It is shown by classical simulation and experimentation that quantum artificial neural networks (QUANNs) are more efficient and in some cases more powerful than classical artificial neural networks (CLANNs) for a variety of experimental tasks. This effect is particularly noticeable with larger and more complex domains. The gain in efficiency is achieved with no generalisation loss in most cases. QUANNs are also more powerful than CLANNs, again for some of the tasks examined, in terms of what the network can learn. What is more, it appears that not all components of a QUANN architecture need to to be quantum for these advantages to surface. It is demonstrated that a fully quantum neural network has no advantage over a partly quantum network and may in fact produce worse results. Overall, this work provides a first insight into the expected behaviour of individual components of QUANNs, if and when quantum hardware is ever built, and raises questions about the interface between quantum...