Hari Mohan Gaur | Searcharxiv

arXiv2026

Quantum Blackhole Learning-Optimized Hadamard Neural Network Model for Dynamic Resource Reservation in Industry Clouds

Accurate workload prediction and proactive resource reservation are crucial for industry clouds. However, the conventional machine learning (CML) models with limited learning capabilities often fail to predict diverse, high-dimensional workloads with sudden changes in resource demand, leading to excessive power consumption and resource management issues. In this context, this article proposes a novel Hadamard neural network with quantum blackhole (QB-HNN) optimization. This model combines the computational efficiency of quantum mechanics with the persuasive learning capability of neural networks (NNs). The workload information is transformed into qubits and propagated via a deep network of qubit neurons comprising a Hadamard-gated activation function to fetch superposition within the QB-HNN model for intuitive pattern learning. Furthermore, a novel quantum blackhole biphase optimization (QB-BiO) algorithm is introduced to train and optimize qubit neural weights. The performance of the proposed model is comprehensively evaluated and compared with five state-of-the-art approaches using six benchmark datasets of three heterogeneous varieties of cloud workloads. The prediction accuracy achieved for an extensive range of workloads confirms its influential performance by minimizing the prediction error up to 36.36% and 22.83% over existing LSTM- and EQNN-based prediction approaches, respectively.

cs.DC↗

arXiv2021

Testable Designs of Toffoli Fredkin Reversible Circuits

Loss of every bit in traditional logic circuits involves dissipation of power in the form of heat that evolve to the environment. Reversible logic is one of the alternatives that have capabilities to mitigate this dissipation by preventing the loss of bits. It also have the potential to broaden the horizon of futuristic reckon with its applications to quantum computation. Application of testing strategies to the logic circuits is a necessity that guarantees their true functioning where the researchers are at par with solutions for the upcoming challenges and agreements for reversible logic circuits. Novel methods of designing Toffoli, Fredkin and mixed Toffoli-Fredkin gates based reversible circuits for testability are put fourth in this article. The proposed designs are independent of the implementation techniques and can be brought into real hardware devices after obtaining a stable fabrication environment. The experimentation for the proposed models are performed on RCViewer and RevKit tools to verify the functionality and computation of cost metrics. Fault simulations are carried out using C++ and Java to calculate fault coverage in respective methodologies. The results confirmed that all the presented work outperforms existing state-of-art approaches.

cs.AR↗