Interest Rate Policy and Supply-side Adjustment Dynamics

In contrast to the present consensus view of stabilization policy, theoretical and empirical research strongly support the consideration of supply-side adjustment to pronounced variations of factor-utilization in order to trace a more realistic pattern of macroeconomic adjustment dynamics within simulation studies. Against this background, our paper seeks to illuminate the relevance of endogenous supply-side adjustment for monetary policy research. We modify a basic New Keynesian model by explicitly considering demand-side stimulus on the evolution of productive capacity and analyze stability, impulse response, and welfare issues if the central bank follows a simple monetary policy rule. Thereby, we control for the robustness of our policy implications by various states of output gap mismeasurement the central bank might be confronted with. We find that, in contrast to a basic New Keynesian Model, output gap stabilization plays a more prominent role when potential output is endogenous.monetary policy, factor-utilization, endogenous potential output, output gap mismeasurement

Interest rate policy and supply-side adjustment dynamics

In contrast to the present consensus view of stabilization policy, theoretical and empirical research strongly support the consideration of supply-side adjustment to pronounced variations of factor-utilization in order to trace a more realistic pattern of macroeconomic adjustment dynamics within simulation studies. Against this background, our paper seeks to illuminate the relevance of endogenous supply-side adjustment for monetary policy research. We modify a basic New Keynesian model by explicitly considering demand-side stimulus on the evolution of productive capacity and analyze stability, impulse response, and welfare issues if the central bank follows a simple monetary policy rule. Thereby, we control for the robustness of our policy implications by various states of output gap mismeasurement the central bank might be confronted with. We find that, in contrast to a basic New Keynesian Model, output gap stabilization plays a more prominent role when potential output is endogenous

Monetary Policy and Hysteresis in Potential Output

We show that actively stabilizing economic activity plays a more prominent role in the conduct of monetary policy when potential output is subject to hysteresis. We augment a basic New Keynesian model by hysteresis in potential output and contrast simulation outcomes of this extended model to the standard model. We find that considering hysteresis allows for a more realistic propagation of macroeconomic shocks and persistent movements in output after monetary shocks. Our central policy implication of active output gap stabilization arises from stability analyses and welfare considerations

Multi-zone trapped-ion qubit control in an integrated photonics QCCD device

Multiplexed operations and extended coherent control over multiple trapping sites are fundamental requirements for a trapped-ion processor in a large scale architecture. Here we demonstrate these building blocks using a surface electrode trap with integrated photonic components which are scalable to larger numbers of zones. We implement a Ramsey sequence using the integrated light in two zones, separated by 375 $\mu$m, performing transport of the ion from one zone to the other in 200 $\mu$s between pulses. In order to achieve low motional excitation during transport we developed techniques to measure and mitigate the effect of the exposed dielectric surfaces used to deliver the integrated light to the ion. We also demonstrate simultaneous control of two ions in separate zones with low optical crosstalk, and use this to perform simultaneous spectroscopy to correlate field noise between the two sites. Our work demonstrates the first transport and coherent multi-zone operations in integrated photonic ion trap systems, forming the basis for further scaling in the trapped-ion QCCD architecture.Comment: 15 pages, 10 figure

Unit cell of a Penning micro-trap quantum processor

Trapped ions in radio-frequency traps are among the leading approaches for realizing quantum computers, due to high-fidelity quantum gates and long coherence times. However, the use of radio-frequencies presents a number of challenges to scaling, including requiring compatibility of chips with high voltages, managing power dissipation and restricting transport and placement of ions. By replacing the radio-frequency field with a 3 T magnetic field, we here realize a micro-fabricated Penning ion trap which removes these restrictions. We demonstrate full quantum control of an ion in this setting, as well as the ability to transport the ion arbitrarily in the trapping plane above the chip. This unique feature of the Penning micro-trap approach opens up a modification of the Quantum CCD architecture with improved connectivity and flexibility, facilitating the realization of large-scale trapped-ion quantum computing, quantum simulation and quantum sensing

Penning micro-trap for quantum computing

Trapped ions in radio-frequency traps are among the leading approaches for realizing quantum computers, because of high-fidelity quantum gates and long coherence times1–3. However, the use of radio-frequencies presents several challenges to scaling, including requiring compatibility of chips with high voltages4, managing power dissipation5 and restricting transport and placement of ions6. Here we realize a micro-fabricated Penning ion trap that removes these restrictions by replacing the radio-frequency field with a 3 T magnetic field. We demonstrate full quantum control of an ion in this setting, as well as the ability to transport the ion arbitrarily in the trapping plane above the chip. This unique feature of the Penning micro-trap approach opens up a modification of the quantum charge-coupled device architecture with improved connectivity and flexibility, facilitating the realization of large-scale trapped-ion quantum computing, quantum simulation and quantum sensing

TensorBank:Tensor Lakehouse for Foundation Model Training

Storing and streaming high dimensional data for foundation model training became a critical requirement with the rise of foundation models beyond natural language. In this paper we introduce TensorBank, a petabyte scale tensor lakehouse capable of streaming tensors from Cloud Object Store (COS) to GPU memory at wire speed based on complex relational queries. We use Hierarchical Statistical Indices (HSI) for query acceleration. Our architecture allows to directly address tensors on block level using HTTP range reads. Once in GPU memory, data can be transformed using PyTorch transforms. We provide a generic PyTorch dataset type with a corresponding dataset factory translating relational queries and requested transformations as an instance. By making use of the HSI, irrelevant blocks can be skipped without reading them as those indices contain statistics on their content at different hierarchical resolution levels. This is an opinionated architecture powered by open standards and making heavy use of open-source technology. Although, hardened for production use using geospatial-temporal data, this architecture generalizes to other use case like computer vision, computational neuroscience, biological sequence analysis and more