Let R=𝔽_q[v]/⟨ v^3-v⟩ be a finite non-chain ring, where q is an odd prime power. In this paper, we construct a class of entanglement-assisted quantum error-correcting codes (EAQECCs), including some maximal entanglement entanglement-assisted quantum error-correcting codes (ME-EAQECCs) via the Gray images of constacyclic codes over R . Several such codes are presented to demonstrate the effectiveness of our construction. Moreover, the codes constructed in this work are new, and their parameters are superior to those of the known EAQECCs provided in [1].
This paper presents a Modified Quantum Neural Network (MQNN) framework designed as an enhanced version of the classical Quantum Neural Network (QNN) for modeling nonlinear dynamic systems. The objective of this work is not to compete with other neural network architectures, but rather to refine and improve the fundamental QNN structure. The MQNN introduces key modifications at both the input encoding and output processing stages. Unlike the conventional QNN, which applies a sigmoid transformation prior to phase modulation, the MQNN directly maps raw inputs into phase values. This direct phase encoding broadens the representational capacity of the hidden-layer quantum neurons and enables them to capture richer nonlinear phase interactions. Additionally, the traditional trigonometric interference-based output neuron is replaced with a trainable amplitude–bias output unit, where the output scaling coefficient C_o and bias term w_o are adaptively tuned during learning. This modification simplifies the output mapping and enhances the network’s ability to normalize and dynamically scale its output. All parameters are updated through gradient-based learning to ensure stable convergence. The performance of the MQNN is evaluated on three benchmark nonlinear dynamic systems and compared solely with the classical QNN under identical conditions. Simulation results demonstrate that the MQNN achieves faster convergence, lower mean-square error, and improved generalization capability, confirming its effectiveness as a refined quantum-inspired modeling framework.
Eisert–Wilkens–Lewenstein (EWL) approach is a standard quantization scheme using which classical games can be analyzed. In this present work, we study the quantization of the classical Wessie–Franzen Volunteers’ Dilemma, where cost of volunteering is shared equally among the volunteers. When we use general EWL scheme for 2-player Volunteers’ Dilemma game, a local equivalence class of Controlled NOT (CNOT) is the only option that can achieve the Pareto Optimal Symmetric Nash Payoff. However, by modifying the EWL framework and changing the strategy profile, we construct a general expression that provides us with more flexible combinations which are a local equivalence class of special perfect entanglers (SPEs). Our findings reveal the importance of SPEs in quantum games like the Quantum Volunteers’ Dilemma, thereby underscoring a strategic advantage of decision-making in quantum settings.
We describe a self-shielding mechanism in open quantum systems by coupling Lindblad dynamics with an interval Type-2 fuzzy observer model. To avoid conceptual ambiguities associated with physical measurement apparatuses, we model the environment as an information-theoretic agent characterized by bounded cognitive resolution. We identify a stability regime characterized by a stable steady state. By formulating the mathematical expansion of the footprint of uncertainty (FOU), our comparative dynamics reveal that as the system’s back-action strength (k) increases, the observer’s reduced resolution significantly suppresses both the effective decoherence rate and the growth of Von Neumann entropy. This protective phase is evaluated through extensive stochastic simulations ( N=1000 ) and continuous crossover mapping, offering a theoretical framework for understanding the quantum-to-classical transition in complex, high-noise environments.
