Non-separated states from squeezed dark-state polaritons in electromagnetically-induced-transparency media

Within the frame of quantized dark-state polaritons in electromagnetically-induced-transparency media, noise fluctuations in the quadrature components are studied. Squeezed state transfer, quantum correlation, and noise entanglement between probe field and atomic polarization are demonstrated in single- and double-$\Lambda$ configurations, respectively. Even though a larger degree of squeezing parameter in the continuous variable helps to establish stronger quantum correlations, inseparability criterion is satisfied only within a finite range of squeezing parameter. The results obtained in the present study may be useful for guiding experimental realization of quantum memory devices for possible applications in quantum information and computation.Comment: 12 pages, 7 figure

Higher-order solutions to non-Markovian quantum dynamics via hierarchical functional derivative

Solving realistic quantum systems coupled to an environment is a challenging task. Here we develop a hierarchical functional derivative (HFD) approach for efficiently solving the non-Markovian quantum trajectories of an open quantum system embedded in a bosonic bath. An explicit expression for arbitrary order HFD equation is derived systematically. Moreover, it is found that for an analytically solvable model, this hierarchical equation naturally terminates at a given order and thus becomes exactly solvable. This HFD approach provides a systematic method to study the non-Markovian quantum dynamics of an open system coupled to a bosonic environment.Comment: 5 pages, 2 figure

A Mixed-methods Study of Governance Mechanisms and Outsourcing Information System Services on Goal Performance

Background: Information systems outsourcing (ISO) is one of the critical businesses in information technology outsourcing (ITO). Due to the increasing complexity of ISO, the failure rate of such outsourcing increases. Outsourcing information system services (OISS) was thus proposed to deal with this. A conceptual framework based on the information processing view was developed to investigate how the client firms assess OISS goal performance. Governance mechanisms (governance structure, relational governance, and IT coordination) were treated as antecedents of transaction cost and outsourcing flexibility; these would further affect goal performance (goal achievement and goal exceedance) with task complexity as a moderator. Method: A mix-methods study was conducted; the qualitative approach was employed to validate the conceptual framework by interviewing three managers with experiences in OISS from the client firms, whereas the quantitative approach, with 206 responses from those with OISS experiences from the client firms, provides empirical evidence. Results: The results indicated that relational governance effectively reduced transaction cost and increased outsourcing flexibility; the governance structure was also vital for outsourcing flexibility. Transaction cost was found to negatively affect goal achievement, and outsourcing flexibility positively affected both goal achievement and goal exceedance. The moderating effects of task complexity were also confirmed. Conclusion: The results extended the information processing view to OISS and proved that transaction cost and outsourcing flexibility are necessary to link governance mechanisms and goal performance. Practically, the client firms are suggested to maintain a positive relationship with the OISS provider. The OISS provider should offer an exclusive channel during and after the execution of the OISS project to reduce the possible cost that occurs during the implementation and improve the outsourcing flexibility to allow the client firms to consider their goals have been achieved and beyond their expectations. By doing so, the effect of goal performance can be maximized

NetBooster: Empowering Tiny Deep Learning By Standing on the Shoulders of Deep Giants

Tiny deep learning has attracted increasing attention driven by the substantial demand for deploying deep learning on numerous intelligent Internet-of-Things devices. However, it is still challenging to unleash tiny deep learning's full potential on both large-scale datasets and downstream tasks due to the under-fitting issues caused by the limited model capacity of tiny neural networks (TNNs). To this end, we propose a framework called NetBooster to empower tiny deep learning by augmenting the architectures of TNNs via an expansion-then-contraction strategy. Extensive experiments show that NetBooster consistently outperforms state-of-the-art tiny deep learning solutions

Dynamical invariants in non-Markovian quantum state diffusion equation

We find dynamical invariants for open quantum systems described by the non-Markovian quantum state diffusion (QSD) equation. In stark contrast to closed systems where the dynamical invariant can be identical to the system density operator, these dynamical invariants no longer share the equation of motion for the density operator. Moreover, the invariants obtained with from bi-orthonormal basis can be used to render an exact solution to the QSD equation and the corresponding non-Markovian dynamics without using master equations or numerical simulations. Significantly we show that we can apply these dynamic invariants to reverse-engineering a Hamiltonian that is capable of driving the system to the target state, providing a novel way to design control strategy for open quantum systems.Comment: 6 pages, 2 figure