Quantum Neural Computing Research Articles

Quantum Neural Network for Quantum Neural Computing.

Neural networks have achieved impressive breakthroughs in both industry and academia. How to effectively develop neural networks on quantum computing devices is a challenging open problem. Here, we propose a new quantum neural network model for quantum neural computing using (classically controlled) single-qubit operations and measurements on real-world quantum systems with naturally occurring environment-induced decoherence, which greatly reduces the difficulties of physical implementations. Our model circumvents the problem that the state-space size grows exponentially with the number of neurons, thereby greatly reducing memory requirements and allowing for fast optimization with traditional optimization algorithms. We benchmark our model for handwritten digit recognition and other nonlinear classification tasks. The results show that our model has an amazing nonlinear classification ability and robustness to noise. Furthermore, our model allows quantum computing to be applied in a wider context and inspires the earlier development of a quantum neural computer than standard quantum computers.

A Novel Neural Network Based on Quantum Computing

Since the first quantum neural network based on quantum computing was proposed by famous scholar Kak, much attention has been taken focus on designing new quantum neural network models. In this paper, a novel efficient quantum feed-forward neural network based on quantum computing is established, which adopts genetic algorithm to improve the traditional back propagation algorithm in parameters learning process. We clearly show the mathematical process of the new proposed quantum network model and improved algorithm. The experimental results of MATLAB simulations show that the new network model which makes the best use of fast quantum neural computation does a better job in function approximation and prediction of educational short video’s spreading capacity than traditional back propagation neural network, and the improved algorithm is more efficient than common back propagation algorithm in the proposed quantum network model. Our model can be widely used in weather prediction, handwriting recognition, speech recognition, and other aspects.

Quantum Robotics, Neural Networks and the Quantum Force Interpretation

A future quantum technological infrastructure demands the development of quantum cyber-physical-cognitive systems, merging quantum artificial intelligence, quantum robotics and quantum information and communication technologies. To support such a development, the current work introduces a new interpretation of quantum mechanics, grounded on a link between quantum computer science, systems science and field-based computation. This new interpretation is applied to quantum artificial neural networks, with examples implemented experimentally on IBM’s five qubit transmon bowtie chip, accessed via cloud using IBM Q Experience, illustrating how quantum neural computing can be implemented on actual quantum computers. A new form of quantum neural machine learning, based on a quantum optimization of a conditional utility function is also introduced and applied to quantum robotics, where a quantum robot, characterized by an interface and a multilayer quantum artificial neural network, interacts with a quantum target, changing the target’s dynamics adaptively, based upon the quantum optimization dynamics, computing the optima for a performance measure and changing the target’s dynamics accordingly.

Quantum Stochastic Neural Maps and Quantum Neural Networks

Quantum neural computation employs quantum computational circuits that consist of chains of conditional unitary operators that follow the network’s neural links leading to entanglement between the local neuron-level computation and the network. When such quantum computational circuits are applied iteratively, instead of an input and output density, we have a sequence of density operators, with the transition from one density to another resulting from a form of unitary quantum map. It has been shown that these unitary quantum neural maps lead to complex emergent dynamics at the level of relevant quantum averages. The present work expands the research on quantum neural maps combining it with quantum stochastic processes theory, introducing the concept of a quantum stochastic neural map, resulting from a coupling to an external noise source. The unitary and stochastic maps are implemented for a quantum recurrent neural network, showing evidence of complex emergent dynamics, including, in the case of the stochastic map, fractal attractors that leave a signature at the level of the energy versus mutual information plots, as well as local (neuron-level) entropy resilient dynamics, where each neuron, as an open computing unit, exhibits dissipation but does not reach full entanglement-related decoherence.

Neurocomputing approach to matrix product state using quantum dynamics

During the last three decades, quantum neural computation has received a relatively high amount of attention among researchers and academic communities since the model of quantum neural network has been proposed. Matrix product state is the well-designed class of tensor network states, which plays an important role in processing of quantum information. The area of dynamical systems help us to study the temporal behavior of systems in time. In our previous work, we have shown the relationship between quantum finite state machine and matrix product state. In this paper, we have used the proposed unitary criteria to investigate the dynamics of matrix product state with quantum weightless neural networks, where the output qubit is extracted and fed back (iterated) to input. Further, we have used Von Neumann entropy to measure possible entanglement of output quantum state. Finally, we have plotted the dynamics for each matrix product state against iterations and analyzed their results.

Quantum Robotics, Neural Networks and the Quantum Force Interpretation

A future quantum technological infrastructure demands the development of quantum cyber-physical-cognitive systems, merging quantum artificial intelligence, quantum robotics and quantum information and communication technologies. To support such a development, the current work introduces a new interpretation of quantum mechanics, grounded on a link between quantum computer science, systems science and field-based computation. This new interpretation is applied to quantum artificial neural networks, with examples implemented experimentally on IBM's five qubit transmon bowtie chip, accessed via cloud using IBM Q Experience, illustrating how quantum neural computing can be implemented on actual quantum computers. A new form of quantum neural machine learning, based on a quantum optimization of a conditional utility function is also introduced and applied to quantum robotics, where a quantum robot, characterized by an interface and a multilayer quantum artificial neural network, interacts with a quantum target, changing the target's dynamics adaptively, based upon the quantum optimization dynamics, computing the optima for a performance measure and changing the target's dynamics accordingly.

Re-directing Climate Change and Terrorism by Allying Classical with Quantum Neural Computing

Before the turn of the 21st century, terrorism and climate change may cause the breakdown of civilization. The cause of this advancing upheaval is the disparity between scientific ascent and social descent. This paper explores the social values hidden in sacred texts and artistic masterpieces through a debatable view of an impaired capacity for renewing familiar reality in Autism Spectrum Disorder (ASD), as well as an impaired ability to preserve shared beliefs in Schizophrenia Spectrum Disorders (SSD). The author posits that we will overcome the challenges unleashed by fundamentalism and global warming if we bolster the alliance of a) our quantum capacity for renovating shared knowledge with our classical ability to preserve it and of b) our appreciation of beauty with a humanistic use of science.

Analysis of quantum neural network learning ability

This paper represents the spatial-temporal integration of neural signals from the Quantum Hamiltonian of the system of analysis of quantum neuron. A theoretical quantum neural network model is proposed by quantum neural model of wave functions. Quantum Turing machine operator in a single step has been constructed and the study algorithm has been presented. Simulation results show that quantum neuron has a strong ability to learn. Finally some other related problems about quantum neural computation have been discussed.

A Heuristic View of the Neurobiological Correlates of Classical and Quantum Neural Computing from the Perspective of Autism

The odd performance of autistic individuals in neuropsychological tests and anomalies in their discourse suggest that when nonautistic individuals face a problem, their minds enter quantum coherence: The legitimacy of involved prototypical schemata—guarded by classical neural computing, sequence, or the first attention—is suspended by quantum neural computing, simultaneity, or the second attention; and the consequences of conflicting variants of the schemata on hold are simulated in cerebellar microcomplexes. Within quantum decoherence: The second attention discerns a promising variant (learning); and the first attention will validate the new schema and shield it (memory) against illegitimate change if unfit mistakes are eradicated or amusing mistakes are introduced. In this article, psychological aspects of the quantum jump from coherence to decoherence (which autistics cannot follow), damage observed in autopsied brains of autistic subjects, and tertiary research on the cerebellum lead to heuristics on the neurobiological correlates of classical and quantum neural computing. NeuroQuantology | June 2013 | Volume 11 | Issue 2 | Page 314-331

Information and Learning in Neural Systems

This paper presents a brief account of my research on applying information to neural systems. Although information in physical systems is measured in terms of entropy, it also has a subjective meaning that plays a role in cognition and creativity. To cover the broader meaning of information, I developed learning mechanisms that can recall given a fragment of the memory and a model for instantaneous learning in a neural network. Further, I introduced the notion of quantum neural computing and investigated new aspects of quantum entropy. I have also investigated questions of learning from the perspective of the three languages of the brain: associative, re-organizational, and quantum.

A multilayered feed‐forward network based on qubit neuron model

AbstractWith the development of our highly information‐oriented society, there is an increasing demand for large‐scale and high‐level information processing. Toward this goal, studies have sought to create a new computation principle having an information processing ability exceeding the existing Neumann‐type computer, such as the creation of a new computation theory or the integration of the frameworks of existing computation theories. As one such approach, quantum neural computing is considered to be interesting, which integrates neural computing and quantum computation. This paper constructs the feed‐forward neural network, which is widely used in practice, based on the qubit neuron model. The 4‐bit parity‐check problem and the general function identification problem are considered. The performance is compared to the feed‐forward network based on the conventional neuron model, and it is shown that the proposed model has a higher performance than the conventional model, using the learning diagram composed of convergence rate and the number of learning iterations. The reason for the better performance is also discussed. © 2004 Wiley Periodicals, Inc. Syst Comp Jpn, 35(13): 43–51, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/scj.10342

Simulations of quantum neural networks

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 fixing 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 Hopfield 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 complexity and power, in going to the quantum regime, is demonstrated. We train the quantum Hopfield net using simultaneous recurrent backpropagation.

A network model based on qubitlike neuron corresponding to quantum circuit

Investigations into quantum computations have begun from the pioneering theoretical studies of Feynman, Deutsch, and others, and detailed studies have been done since the discovery of a quantum algorithm which can solve the problem of factorizing a large integer in polynomial time by Shor in 1994. Recently, the existence of nonalgorithmic quantum computations in microtubules inside a neural circuit has been debated, resulting in the proposal of the concept of the quantum neural computation theory, although detailed studies have not as yet been made. In this paper, in order to construct a new framework for describing the cohesiveness of the distribution and synthesis inherent in a neural network, a neural state is described quantum dynamically and a qubitlike neural network corresponding to the quantum circuit of quantum computations is studied. Specifically, a qubitlike neural network is constructed for a 3-bit quantum circuit, which is the minimum quantum logical gate describing all basic logical operations, and in this model we investigate how to determine circuit parameters by learning. © 2000 Scripta Technica, Electron Comm Jpn Pt 3, 83(10): 67–73, 2000

On quantum neural computing

This paper examines the notion of quantum neural computing in the context of several new directions in neural network research. In particular, we consider new neuron and network models that lead to rapid training, chaotic dynamics in neuron assemblies, models of attention and awareness, cytoskeletal microtubule information processing, and quantum models. Recent discoveries in neuroscience that cannot be placed in the reductionist models of biological information processing are examined. We consider some characteristics of a quantum neural computer. We show that information is not a locally additive variable in a quantum computation; this property may be used to examine the nature of biological information structures.