Quantum computing is a field with increasing relevance as quantum hardware improves and more applications of quantum computing are discovered. In this paper, we demonstrate the feasibility of modeling Ising Model Hamiltonians on the IBM quantum computer. We developed quantum circuits to simulate these systems more efficiently for both closed and open boundary Ising models, with and without perturbations. We tested these various geometries of systems in both 1-D and 2-D space to mimic two real systems: magnetic materials and biological neural networks (BNNs). Our quantum model is more efficient than classical computers, which can struggle to simulate large, complex systems of particles.
Quantum computing is a field with increasing relevance as quantum hardware improves and more applications of quantum computing are discovered. In this paper, we demonstrate the feasibility of modeling Ising Model Hamiltonians on the IBM quantum computer. We developed quantum circuits to simulate these systems more efficiently for both closed and open boundary Ising models, with and without perturbations. We tested these various geometries of systems in both 1-D and 2-D space to mimic two real systems: magnetic materials and biological neural networks (BNNs). Our quantum model is more efficient than classical computers, which can struggle to simulate large, complex systems of particles.
Quantum computing is a field with increasing relevance as quantum hardware improves and more applications of quantum computing are discovered. In this paper, we demonstrate the feasibility of modeling Ising Model Hamiltonians on the IBM quantum computer. We developed quantum circuits to simulate these systems more efficiently for both closed and open boundary Ising models, with and without perturbations. We tested these various geometries of systems in both 1-D and 2-D space to mimic two real systems: magnetic materials and biological neural networks (BNNs). Our quantum model is more efficient than classical computers, which can struggle to simulate large, complex systems of particles.
This paper proposes a novel quantum computing architecture inspired by the structure and function of biological neural networks. The core claim is to enhance quantum computing efficiency and fault tolerance by leveraging the inherent properties of biological systems. The mechanism involves modeling quantum bits (qubits) using biologically inspired neuron and synapse models, mimicking their interactions, and utilizing the self-organizing capabilities of neural networks for quantum computation. Unlike traditional quantum computing architectures rooted in classical physics, this approach aims to capitalize on the flexibility and adaptability found within biological systems. The architecture explores concepts such as quantum entanglement and superposition through simulated neural network dynamics, offering a potentially more robust and scalable solution for quantum processing. This work lays the foundation for a biologically-inspired quantum computing paradigm.
This paper introduces the Non-linear Self-Organizing Neural Network Model (NSNQN) – a novel framework for quantum computing that leverages the principles of self-organization and quantum entanglement to simulate complex biological neural systems. Traditional neural networks often struggle to capture the intricate self-organization observed in biological neurons, limiting their ability to model intricate biological processes. The NSNQN utilizes quantum entanglement to enable self-learning and adaptive network behavior, offering a pathway toward advanced quantum computing applications. We detail the model's architecture, the integration of quantum entanglement, and the potential for achieving self-aware and responsive neural networks. The paper explores the theoretical foundations and preliminary results demonstrating the NSNQN's capability to emulate biological neural network dynamics. We conclude by highlighting the significant potential of this approach for advancing quantum computation and neuroscience research.
Quantum information has emerged as a transformative technology with the potential to revolutionize various fields, and biological neural networks, with their intricate complexity and dynamic nature, represent a particularly promising area for exploration. This paper investigates the potential of quantum information to enhance learning mechanisms within biological neural networks, leveraging quantum entanglement and quantum computation to improve efficiency and model expressiveness. We propose a novel quantum neural network architecture that employs quantum entanglement to stabilize learning and optimize network parameters, addressing the limitations of conventional neural network learning. This research explores the fundamental principles behind such a system and highlights its potential for advancing our understanding of biological brain function. The core claim is that quantum information can significantly improve learning efficiency and model representation capabilities in biological neural networks.
This paper introduces Quantum Bio-Inspired Computing (QBIC), a novel computational paradigm that leverages the inherent parallelism of biological neural networks to construct efficient quantum computing architectures. QBIC's core claim is that by simulating the complex, parallel processing capabilities of biological neural networks, we can build quantum computing systems capable of exceeding the limitations of traditional quantum algorithms. The central mechanism involves designing quantum neuron models based on quantum decoherence and entanglement, utilizing quantum circuits to emulate synaptic connections and inter-neuronal communication within biological neural networks, thereby enabling large-scale parallel computations. Unlike traditional approaches that directly implement quantum algorithms, QBIC represents a fundamentally new approach, drawing inspiration from biological computation and integrating it with quantum technologies. This offers the potential to tackle unstructured and complex datasets with unprecedented efficiency. The framework presented here provides a foundational approach for exploring the intersection of biology and quantum computing, potentially unlocking new computational frontiers. ---
Quantum computing and neural networks have revolutionized computational intelligence and have the potential to bring revolution in a wide range of scientific, industrial, and technological fields. In some computational activities, quantum computing utilizes the concepts of superposition, entanglement, and quantum parallelism and performs better than classical computers by processing complicated data with high speed and efficiency. Neural networks have transformed decision-making, natural language processing, and pattern recognition especially deep learning models. By incorporating quantum computing into neural network architectures, Quantum Neural Networks (QNNs) open new paradigms in data encoding, feature representation, and learning dynamics by overcoming the computational constraints and scaling related problems of classical models. In this chapter, the theoretical basis, latest developments and possible applications of a quantum neural network have been discussed. This chapter covers the constraints of near-term quantum devices, quantum noise, data encoding techniques, and practical difficulties in hardware implementation. Real-world applications in quantum chemistry, financial modeling, cryptography, and biological imaging have been studied. To fully realize the promise of quantum neural processing, this chapter concludes by describing current research shortcomings and providing opportunities for multi-disciplinary collaboration among computer scientists, physicists, and AI researchers.