Flavor violating Higgs signals, such as the top FCNC decay t→ ch 0 and the LFV Higgs decay h 0 → τµ, have been studied at the LHC. These signals can arise within the general Two-Higgs Doublet Model (THDM), where each Higgs doublet couples to all fermion types through Yukawa matrices Y f 1 and Y f 2. The Yukawa matrices can be assumed to have the same form or they could have different structures. In this paper we study the case when both Y f 1 and Y f 2 have completely different forms, but in such a way that they complement to produce a specific hermitian mass matrix. We find that for specific four-zero textures, the flavor violating Higgs couplings depend only on the free parameters tan β, γ f and the fermion masses. We use the current bounds on the low energy processes to derive constraints on the heavy Higgs boson mass, tan β and γ f. Then, we use these constraints to evaluate the LFV Higgs decays, which reach branching ratios that could be tested at the LHC.

The extraordinary advance in quantum computation leads us to believe that, in the not-too-distant future, quantum systems will surpass classical systems. Moreover, the field’s rapid growth has resulted in the development of many critical tools, including programmable machines (quantum computers) that execute quantum algorithms and the burgeoning field of quantum machine learning, which investigates the possibility of faster computation than traditional machine learning. In this paper, we provide a thorough examination of quantum computing from the perspective of a physicist. The purpose is to give laypeople and scientists a broad but in-depth understanding of the area. We also recommend charts that summarize the field’s diversions to put the whole field into context.

Motivated by the growing threat of distributed denial-of-service (DDoS) attacks and the emergence of quantum computing, this study introduces a novel “quanvolutional autoencoder” architecture for learning representations. The architecture leverages the computational advantages of quantum mechanics to improve upon traditional machine learning techniques. Specifically, the quanvolutional autoencoder employs randomized quantum circuits to analyze time-series data from DDoS attacks, offering a robust alternative to classical convolutional neural networks. Experimental results suggest that the quanvolutional autoencoder performs similarly to classical models in visualizing and learning from DDoS hive plots and leads to faster convergence and learning stability. These findings suggest that quantum machine learning holds significant promise for advancing data analysis and visualization in cybersecurity. The study highlights the need for further research in this fast-growing field, particularly for unsupervised anomaly detection.

The general two-Higgs doublet model (2HDM) contains a rich spectrum of neutral and charged Higgs bosons, whose detection would be a clear signal of new physics. When the Higgs potential is $CP$ conserving, the spectrum includes a pseudoscalar mass eigenstate ${A}^{0}$, which does not couple to vector bosons at tree level. However, fermionic loops (top and bottom mainly) induce the coupling $AV{V}^{$'${}}$ (with $V,{V}^{$'${}}=$\gamma${},Z$) at higher orders. We evaluate the amplitude for the decays ${A}^{0}$\rightarrow${}ZZ,Z$\gamma${},$\gamma${}$\gamma${}$, including a generic fermionic loop contribution, and present results on the branching ratios for 2HDM-I,II and III. Current LHC searches on heavy Higgs bosons are used as an estimate to constrain the allowed mass range for ${A}^{0}$.