Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Reconfigurable Electric Circuits Within the Quantum Circuits Mathematical Framework

Alberto Delgado

Published Oct 14, 2024• 0 citations
Publisher

Abstract

Classical probabilistic reconfigurable systems can be described using the quantum circuits mathematical framework, the analogy reconfigurable - quantum is illustrated with an electric circuit and a neural network, the concept of classically entangled states appears when a DPDT switch is used in electric networks.
DOI
10.36227/techrxiv.172710248.83116727/v2

Related papers

Reconfigurable Electric Circuits Within the Quantum Circuits Mathematical Framework

Alberto Delgado

Classical probabilistic reconfigurable systems can be described using the quantum circuits mathematical framework, the analogy reconfigurable - quantum is illustrated with an electric circuit and a neural network, the concept of classically entangled states appears when a DPDT switch is used in electric networks.

•Sep 23, 2024•0 citations

Dynamically Reconfigurable Quantum Circuits for Machine Learning

Jincheng Zhang

This paper explores the development of dynamically reconfigurable quantum circuits tailored for machine learning applications. The core claim is the design of a hardware architecture capable of on-the-fly circuit reconfiguration, optimizing performance and resource utilization. This is achieved through the employment of programmable quantum gates and dynamic routing of quantum information dictated by the input data and algorithm requirements. The research addresses the limitations of static quantum circuit design by enabling adaptive and efficient quantum computation. Specifically, we investigate methods for mapping machine learning algorithms onto reconfigurable quantum circuits, considering factors such as circuit depth, qubit connectivity, and gate fidelity. We propose a framework for defining a quantum circuit's reconfiguration policy, incorporating real-time feedback from the execution of the algorithm. This framework allows the circuit to adapt its structure and operations to maximize computational efficiency. The theoretical analysis and design principles presented here contribute to the advancement of quantum machine learning by offering a pathway toward more flexible and powerful quantum computing architectures. The ultimate goal is to create quantum systems that can learn and adapt in a way analogous to biological neural networks, unlocking the full potential of quantum computation for complex machine learning problems.

•Aug 31, 2026•0 citations

Quantum-Inspired Probabilistic Circuit Synthesis

Jincheng Zhang

This paper explores a novel approach to neural circuit synthesis, drawing inspiration from the principles of quantum mechanics. Traditional neural network optimization often relies on deterministic algorithms, which can be susceptible to local optima and limited exploration of the solution space. This work proposes a methodology termed "Quantum-Inspired Probabilistic Circuit Synthesis" that leverages concepts like superposition and interference to introduce probabilistic pathways within a neural network architecture. The core idea is to generate a network where connections between nodes are not fixed but governed by probabilities, allowing for a more diverse and robust exploration of the circuit's behavior. We formulate a synthesis process based on probabilistic gate selection and connection weighting, mimicking quantum interference to constructively reinforce promising pathways and destructively cancel unfavorable ones. This approach aims to significantly improve the efficiency and robustness of neural circuit design, offering a potentially superior alternative to conventional deterministic methods. The theoretical framework is presented, along with a description of the core algorithmic components, and a discussion of the potential benefits and challenges associated with this new paradigm.

•Sep 3, 2026•0 citations

Probabilistic Simulation of Quantum Circuits with Neural Network Approximations

Jincheng Zhang

The simulation of quantum circuits presents a significant bottleneck in the development and understanding of quantum algorithms. Traditional methods relying on the full wave function description quickly become computationally intractable as the number of qubits and circuit depth increases. This work proposes a novel approach utilizing neural networks to approximate the probabilities of measurement outcomes within a quantum circuit. The core idea is to train a neural network to learn the complex dynamics of quantum evolution, thereby enabling efficient simulation with a significantly reduced parameter space. We introduce a framework that maps the quantum circuit to a neural network architecture, allowing for approximate but tractable simulation. The effectiveness of this approach is demonstrated through theoretical analysis and a conceptual implementation strategy, highlighting its potential to unlock the exploration of complex quantum algorithms and accelerate the development of quantum computing. The primary objective is to provide a computationally feasible alternative to traditional simulation techniques, bridging the gap between theoretical quantum algorithm design and practical implementation. The key innovation lies in leveraging the representational power of neural networks to capture the essential correlations inherent in quantum systems.

•Sep 2, 2026•0 citations

Fundamentals of Quantum-Inspired Neural Computing

Rebwar Khalid Muhammed, Aso Mohammed Aladdin, Rebwar Khalid Hamad +2 more

This chapter offers a clear and accessible introduction to the field of quantum-inspired neural computing. This area combines ideas from quantum mechanics with traditional neural network models to create new possibilities in machine learning and optimization metaheuristic algorithms. Unlike actual quantum computing, quantum-inspired approaches work on classical computers but borrow key concepts like superposition, entanglement, and probabilistic behaviour to improve how neural networks learn and process information. The study begins by explaining why researchers are interested in using quantum principles in neural networks. It then introduces the basic mathematical ideas behind these concepts, such as quantum states and transformations, and shows how they can be used to design more powerful and flexible learning systems. It focuses on discussing current models, outlining the distinctions between quantum-inspired and true quantum systems, and evaluating the potential benefits and challenges of this approach. In addition, Quantum-inspired neural computing introduces a novel framework that bridges classical artificial intelligence with principles derived from quantum mechanics. By simulating quantum behaviours on classical hardware, it opens new avenues for developing more adaptive and efficient learning models. This emerging approach offers promising advantages in areas such as optimization, parallel processing, and high-dimensional data representation. Finally, despite its potential, the field faces notable limitations and challenges. These include the lack of limited theoretical formalization, and difficulties in scaling models for complex real-world applications. Moreover, distinguishing genuine quantum-inspired advantages from conservative algorithmic improvements remains an current research apprehension.

CRC Press eBooks•May 20, 2026•0 citations

Quantum Entanglement-based Deep Convolutional Neural Networks

Desislav Andreev, Milena Kirilova Lazarova

The paper presents a quantum-based approach for modeling deep neural networks. Quantum entanglement-based neural network model is described, that utilizes several quantum phenomena applied to the connections of the network structures. The concept of tensor networks and their relation to the quantum neural network modeling is presented. A quantum entanglement-based deep convolutional neural network model is suggested based on the classical VGG16 model. The network architecture, the possibilities for its implementation using quantum logic circuits and through simulation on classical computer as well as the quantum neural network training are also discussed in the paper.

International Conference on Computer Systems and Technologies (CompSysTech)•Jun 19, 2020•0 citations
This paper explores the development of dynamically reconfigurable quantum circuits tailored for machine learning applications. The core claim is the design of a hardware architecture capable of on-the-fly circuit reconfiguration, optimizing performance and resource utilization. This is achieved through the employment of programmable quantum gates and dynamic routing of quantum information dictated by the input data and algorithm requirements. The research addresses the limitations of static quantum circuit design by enabling adaptive and efficient quantum computation. Specifically, we investigate methods for mapping machine learning algorithms onto reconfigurable quantum circuits, considering factors such as circuit depth, qubit connectivity, and gate fidelity. We propose a framework for defining a quantum circuit's reconfiguration policy, incorporating real-time feedback from the execution of the algorithm. This framework allows the circuit to adapt its structure and operations to maximize computational efficiency. The theoretical analysis and design principles presented here contribute to the advancement of quantum machine learning by offering a pathway toward more flexible and powerful quantum computing architectures. The ultimate goal is to create quantum systems that can learn and adapt in a way analogous to biological neural networks, unlocking the full potential of quantum computation for complex machine learning problems.
This paper explores a novel approach to neural circuit synthesis, drawing inspiration from the principles of quantum mechanics. Traditional neural network optimization often relies on deterministic algorithms, which can be susceptible to local optima and limited exploration of the solution space. This work proposes a methodology termed "Quantum-Inspired Probabilistic Circuit Synthesis" that leverages concepts like superposition and interference to introduce probabilistic pathways within a neural network architecture. The core idea is to generate a network where connections between nodes are not fixed but governed by probabilities, allowing for a more diverse and robust exploration of the circuit's behavior. We formulate a synthesis process based on probabilistic gate selection and connection weighting, mimicking quantum interference to constructively reinforce promising pathways and destructively cancel unfavorable ones. This approach aims to significantly improve the efficiency and robustness of neural circuit design, offering a potentially superior alternative to conventional deterministic methods. The theoretical framework is presented, along with a description of the core algorithmic components, and a discussion of the potential benefits and challenges associated with this new paradigm.
The simulation of quantum circuits presents a significant bottleneck in the development and understanding of quantum algorithms. Traditional methods relying on the full wave function description quickly become computationally intractable as the number of qubits and circuit depth increases. This work proposes a novel approach utilizing neural networks to approximate the probabilities of measurement outcomes within a quantum circuit. The core idea is to train a neural network to learn the complex dynamics of quantum evolution, thereby enabling efficient simulation with a significantly reduced parameter space. We introduce a framework that maps the quantum circuit to a neural network architecture, allowing for approximate but tractable simulation. The effectiveness of this approach is demonstrated through theoretical analysis and a conceptual implementation strategy, highlighting its potential to unlock the exploration of complex quantum algorithms and accelerate the development of quantum computing. The primary objective is to provide a computationally feasible alternative to traditional simulation techniques, bridging the gap between theoretical quantum algorithm design and practical implementation. The key innovation lies in leveraging the representational power of neural networks to capture the essential correlations inherent in quantum systems.
This chapter offers a clear and accessible introduction to the field of quantum-inspired neural computing. This area combines ideas from quantum mechanics with traditional neural network models to create new possibilities in machine learning and optimization metaheuristic algorithms. Unlike actual quantum computing, quantum-inspired approaches work on classical computers but borrow key concepts like superposition, entanglement, and probabilistic behaviour to improve how neural networks learn and process information. The study begins by explaining why researchers are interested in using quantum principles in neural networks. It then introduces the basic mathematical ideas behind these concepts, such as quantum states and transformations, and shows how they can be used to design more powerful and flexible learning systems. It focuses on discussing current models, outlining the distinctions between quantum-inspired and true quantum systems, and evaluating the potential benefits and challenges of this approach. In addition, Quantum-inspired neural computing introduces a novel framework that bridges classical artificial intelligence with principles derived from quantum mechanics. By simulating quantum behaviours on classical hardware, it opens new avenues for developing more adaptive and efficient learning models. This emerging approach offers promising advantages in areas such as optimization, parallel processing, and high-dimensional data representation. Finally, despite its potential, the field faces notable limitations and challenges. These include the lack of limited theoretical formalization, and difficulties in scaling models for complex real-world applications. Moreover, distinguishing genuine quantum-inspired advantages from conservative algorithmic improvements remains an current research apprehension.
The paper presents a quantum-based approach for modeling deep neural networks. Quantum entanglement-based neural network model is described, that utilizes several quantum phenomena applied to the connections of the network structures. The concept of tensor networks and their relation to the quantum neural network modeling is presented. A quantum entanglement-based deep convolutional neural network model is suggested based on the classical VGG16 model. The network architecture, the possibilities for its implementation using quantum logic circuits and through simulation on classical computer as well as the quantum neural network training are also discussed in the paper.