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Quantum state engineering plays a vital role in various applications in the field of quantum information. Different strategies, including drive-and-dissipation, adiabatic cooling, and measurement-based steering, have been proposed in the past for state generation and manipulation, each with its upsides and downsides. Here, we address a class of measurement-based state engineering protocols where a sequence of generalized measurements is employed to steer a quantum system toward a desired target state. Previously studied measurement-based protocols relied on idealized procedures and avoided exploration of the effects of various errors stemming from imperfections of experimental realizations and external noise. We employ the quantum trajectory formalism to provide a detailed analysis of the robustness of these steering protocols against various errors. We study a set of errors that can be classified as dynamic or static, depending on whether they remain unchanged while running the protocol. More specifically, we investigate the impact of erroneous choice of system-detector coupling, re-initialization of the detector state following a measurement step, fluctuating steering directions, and environmentally induced errors in the system-detector interaction. We show that the protocol remains fully robust against the erroneous choice of system-detector coupling parameters and presents reasonable robustness against other errors. We employ various quantifiers such as fidelity, trace distance, and linear entropy to characterize the protocol's robustness and provide analytical results. Subsequently, we demonstrate the commutation between the classical expectation value and the time-ordering operator of the exponential of a Hamiltonian with multiplicative white noise, as well as the commutation of the expectation value and the partial trace with respect to detector outcomes., Comment: 31 pages of main text + 17 pages of appendices, 13 figures

Data science is an interdisciplinary research area where scientists are typically working with data coming from different fields. When using and analyzing data, the scientists implicitly agree to follow standards, procedures, and rules set in these fields. However, guidance on the responsibilities of the data scientists and the other involved actors in a data science project is typically missing. While literature shows that novel frameworks and tools are being proposed in support of open-science, data reuse, and research data management, there are currently no frameworks that can fully express responsibilities of a data science project. In this paper, we describe the Transparency, Accountability, Privacy, and Societal Responsibility Matrix (TAPS-RM) as framework to explore social, legal, and ethical aspects of data science projects. TAPS-RM acts as a tool to provide users with a holistic view of their project beyond key outcomes and clarifies the responsibilities of actors. We map the developed model of TAPS-RM with well-known initiatives for open data (such as FACT, FAIR and Datasheets for datasets). We conclude that TAPS-RM is a tool to reflect on responsibilities at a data science project level and can be used to advance responsible data science by design.

Density functional theory based First Principles calculations were used to study the effect of Silicon (Si) doping on the structural, electronic and optical properties of CsCaI3. From our calculations, we predict that CsCaI3 can form stable perovskite structure. It is also observed that after substitutional doping of Si in CsCaI3, the material still can stay in perovskite form. Band structure studies showed that with Si doping, the band gap can be varied from 4.76eV for un-doped CsCaI3 to 0.639eV for 75 percent Si doped CsCaI3. The optical absorption spectra of the materials showed that Si doping can induce light absorption in the visible region in CsCaI3. Electron Localization Function (ELF) and effective mass calculations show that Si doping can improve the electronic conduction in this material to meet the requirements of photo Voltaic applications.