Quantum state preparation is a central primitive in many quantum algorithms, yet it is generally resource intensive, with efficient constructions known only for structured families of states. This work introduces a method for preparing quantum states whose amplitudes are given by a degree-d polynomial, using circuits with logarithmic depth in the number n of qubits and only 𝒪(n) ancilla qubits, improving previous approaches that required linear-depth circuits. The construction first relies on a block-encoding of an affine diagonal operator based on its Pauli-basis decomposition, which involves only n terms. A modified linear-combination-of-unitaries (LCU) technique is introduced to implement this decomposition in logarithmic depth, together with a novel circuit for the EXACT-one oracle that flags basis states in which exactly one qubit is in the state |1⟩. It then uses a generalized quantum eigenvalue transformation (GQET) to promote this affine operator to an arbitrary degree polynomial. Theoretical analysis and numerical simulations are reported along with a proof-of-principle implementation on a trapped-ion quantum processor using 14 qubits and more than 500 primitive quantum gates. Because polynomial approximations are ubiquitous in scientific computing, this construction provides a scalable and resource-efficient approach to quantum state preparation, further improving the potential of quantum algorithms in fields such as chemistry, physics, engineering, and finance.
Low-temperature plasmas (LTPs) are essential to both fundamental scientific research and critical industrial applications. As in many areas of science, numerical simulations have become a vital tool for uncovering new physical phenomena and guiding technological development. Code benchmarking remains crucial for verifying implementations and evaluating performance. This work continues the Landmark benchmark initiative, a series specifically designed to support the verification of LTP codes. In this study, seventeen simulation codes from a collaborative community of nineteen international institutions modeled a partially magnetized E x B Penning discharge. The emergence of large scale coherent structures, or rotating plasma spokes, endows this configuration with an enormous range of time scales, making it particularly challenging to simulate. The codes showed excellent agreement on the rotation frequency of the spoke as well as key plasma properties, including time-averaged ion density, plasma potential, and electron temperature profiles. Achieving this level of agreement came with challenges, and we share lessons learned on how to conduct future benchmarking campaigns. Comparing code implementations, computational hardware, and simulation runtimes also revealed interesting trends, which are summarized with the aim of guiding future plasma simulation software development.
In this study, direct numerical simulation of a turbulent flame-wall interaction (FWI) has been done for premixed H $_2/$ air and NH $_3/$ H $_2/$ air flames in a fully developed channel flow at Re $_\tau$ $\approx$ 300. Both isothermal and adiabatic walls are considered. The results contribute to further clarification of the underlying mechanisms of FWIs. First, the underlying mechanism for the rapid increase of chemical flame thickness near the wall is found to be the zero-flux boundary condition for diffusion. Effects of wall heat loss and wall turbulence are minor. Then, a ridge-based flame surface identification method is proposed to track the flame front, which is found to be more accurate than an isosurface of $C$ (the progress variable), especially during FWIs. Using this technique, the near-wall flame geometry and orientation are correctly captured. It is found that the flames are laminarised near the wall and almost parallel to the isothermal wall shortly before quenching. Flame-vortex interactions lead to entrained flame pockets for H $_2$ as a fuel and to a distributed reaction zone for the case of NH $_3/$ H $_2$ . Finally, the turbulent combustion regime is investigated by checking wall-distance-dependent Reynolds number and Karlovitz number. It is found that the flames enter the laminar flame regime shortly before wall quenching, instead of the broken reaction regime suggested in previous studies. To support the analysis, the turbulent flame dynamics, including turbulent burning rate, turbulent flame surface area, flame stretch factor, local displacement speed, flame dilatation, flame strain rate (both tangential and normal) and flame alignment with the principal strain rate are quantified, providing a full picture of near-wall turbulent flames for the considered conditions.
Current disaster and emergency management services produce flood maps within hours using satellite data. To handle large-scale events efficiently, a reliable automated method is needed to generate an initial flood extent map, which can then be refined manually. Launched in December 2022, the Surface Water and Ocean Topography (SWOT) satellite, equipped with the Ka-band Radar Interferometer (KaRIn), provides high-resolution radar observations used here for flood detection. While not initially designed for detailed flood mapping in vegetated or urban regions, the performance of SWOT's Pixel Cloud products was assessed during four major flood events in Greece, France, Brazil, and the USA. Each event is paired with Sentinel-1 or Sentinel-2 imagery within a 3-hour time frame, providing a valuable opportunity to compare and evaluate SWOT's flood detection capabilities. Three radar variables of the Pixel Cloud products are studied for extracting flood extents: sigma 0, coherent power, and interferometric coherence - which is computed from the two complex interferograms. They are compared to the built-in classification and flood masks computed from Sentinel-1/2. The study demonstrates the capabilities of the SWOT satellite in detecting flooded vegetation, flooded urban areas, and even regions with high snow cover. However, limitations are observed: (1) when high soil moisture is observed, causing signal saturation, (2) SWOT can be sensitive to the incidence angle, both of which lead to less reliable flood extent estimation. These findings highlight the potential of SWOT satellite for improving global flood mapping, as well as the need for further exploration to address current limitations and enhance flood monitoring capabilities in the near future.
Floods are one of the most common and devastating natural disasters worldwide. The contribution of remote sensing is important for reducing the impact of flooding both during the event itself and for improving hydrodynamic models by reducing their associated uncertainties. This article presents the innovative capabilities of the Surface Water and Ocean Topography (SWOT) mission, especially its river node products, to enhance the accuracy of riverine flood reanalysis, performed on a 50-km stretch of the Garonne River. The challenge addressed here is quantifying how SWOT river observations, alone and in combination with in-situ gauges, can improve hydraulic parameter estimation and river water level prediction in flood reanalysis. The experiments incorporate various data assimilation strategies, based on the ensemble Kalman filter, which allows for sequential updates of model parameters based on available observations. The experimental results show that while SWOT data alone offers some improvements, combining it with in-situ water level measurements provides the most accurate representation of flood dynamics, both at gauge stations and along the river. The study also investigates the impact of different SWOT revisit frequencies on the model's performance, revealing that assimilating more frequent SWOT observations leads to more reliable flood reanalyses. In the real event, it was demonstrated that the assimilation of SWOT and in-situ data accurately reproduces the water level dynamics, offering promising prospects for future flood monitoring systems. Results show that in the OSSE framework, assimilation reduced water level errors by an order of magnitude, while in the real 2024 event the errors were reduced to below 17 cm, demonstrating the reliability of the approach. This study underscores the complementary role of Earth Observation data in enhancing flood dynamics representation in the riverbed and the floodplains.
