Computers has been endowed with a part of human-like intelligence owing to the rapid development of the artificial intelligence technology represented by the neural networks. Facing the challenge to make machines more imaginative, we consider a quantum stochastic neural network (QSNN), and propose a learning algorithm to update the parameters governing the network evolution. The QSNN can be applied to a class of classification problems, we investigate its performance in sentence classification and find that the coherent part of the quantum evolution can accelerate training, and improve the accuracy of verses recognition which can be deemed as a quantum enhanced associative memory. In addition, the coherent QSNN is found more robust against both label noise and device noise so that it is a more adaptive option for practical implementation.
Computers has been endowed with a part of human-like intelligence owing to the rapid development of the artificial intelligence technology represented by the neural networks. Facing the challenge to make machines more imaginative, we consider a quantum stochastic neural network (QSNN), and propose a learning algorithm to update the parameters governing the network evolution. The QSNN can be applied to a class of classification problems, we investigate its performance in sentence classification and find that the coherent part of the quantum evolution can accelerate training, and improve the accuracy of verses recognition which can be deemed as a quantum enhanced associative memory. In addition, the coherent QSNN is found more robust against both label noise and device noise so that it is a more adaptive option for practical implementation.
We study a quantum stochastic neural network (QSNN) based on quantum stochastic walks on a graph, and use gradient descent to update the network parameters. We apply a toy model of QSNN with a few neurons to the problems of function approximation, two-dimensional data classification, and sequence classification. A simple QSNN with five neurons is trained to determine whether a sequence of words is a sentence or not, and we find that a QSNN can reduce the number of training steps. A QSNN with 11 neurons shows a quantum advantage in improving the accuracy of recognizing new types of inputs like verses. Moreover, with our toy model, we find the coherent QSNN is more robust against both label noise and device noise, compared with the decoherent QSNN. These results show that quantum stochastic walks may be a useful resource to implement a quantum neural network.
Quantum Adaptive Neural Network Model represents a novel approach to neural network architecture leveraging quantum phenomena to enhance expressiveness and computational efficiency. Traditional neural networks often struggle with limitations in handling complex data and achieving optimal performance. This research proposes a quantum-enhanced neural network model, employing quantum machine learning techniques to overcome these limitations. The core mechanism centers around mimicking neural network behavior using quantum simulations, with subsequent optimization driven by quantum algorithms. This work aims to demonstrate a significant improvement in model accuracy and potentially accelerate training times, offering a compelling advancement in the field of artificial intelligence. We explore the potential of quantum-enhanced neural networks to address challenges inherent in large-scale data processing and complex pattern recognition.
In quantum information technology, crucial information is regularly encoded in different quantum states. To extract information, the identification of one state from the others is inevitable. However, if the states are non-orthogonal and unknown, this task will become awesomely tricky, especially when our resources are also limited. Here, we introduce the quantum stochastic neural network (QSNN), and show its capability to accomplish the binary discrimination of quantum states. After a handful of optimizing iterations, the QSNN achieves a success probability close to the theoretical optimum, no matter whether the states are pure or mixed. Other than binary discrimination, the QSNN is also applied to classify an unknown set of states into two types: entangled ones and separable ones. After training with four samples, it can classify a number of states with acceptable accuracy. Our results suggest that the QSNN has the great potential to process unknown quantum states in quantum information.
A promising combination of artificial intelligence and quantum computing, quantum neural networks (QNNs) have the potential to completely transform the way neural networks interpret and learn from data. QNNs can outperform classical neural networks in terms of speed, efficiency, and scalability by utilizing the special qualities of quantum physics, such as superposition, entanglement, and quantum parallelism. The basic ideas of QNNs, their structures, and the ways in which quantum algorithms can be incorporated into machine learning processes to improve problem-solving skills are all examined in this study. current developments in QNNs, emphasizing how they can be used for challenging applications including pattern recognition, optimization, and high-dimensional data space decision-making. We also go over the difficulties in creating scalable QNN models and the possible future applications of QNNs in artificial intelligence. By utilizing the potential of quantum computing, QNNs have the potential to advance artificial intelligence by providing answers to issues that are now intractable through traditional methods.
In this paper, machine learning and artificial neural network models are proposed for quantum noise classification in stochastic quantum dynamics. For this purpose, we train and then validate support vector machine, multi-layer perceptron and recurrent neural network, models with different complexity and accuracy, to solve supervised binary classification problems. By exploiting the quantum random walk formalism, we demonstrate the high efficacy of such tools in classifying noisy quantum dynamics using data sets collected in a single realisation of the quantum system evolution. In addition, we also show that for a successful classification one just needs to measure, in a sequence of discrete time instants, the probabilities that the analysed quantum system is in one of the allowed positions or energy configurations, without any external driving. Thus, neither measurements of quantum coherences nor sequences of control pulses are required. Since in principle the training of the machine learning models can be performed a-priori on synthetic data, our approach is expected to find direct application in a vast number of experimental schemes and also for the noise benchmarking of the already available noisy intermediate-scale quantum devices.
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.