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The performances of Low Gain Avalanche diode (LGAD) sensors from a neutron irradiation campaign with fluences of 0.8 x 10^15, 15 x 10^15 and 2.5 x 10^15 neq/cm2 are reported in this article. These LGAD sensors are developed by the Institute of High Energy Physics, Chinese Academy of Sciences and the Novel Device Laboratory for the High Granularity Timing Detector of the High Luminosity Large Hadron Collider. The timing resolution and collected charge of the LGAD sensors were measured with electrons from a beta source. After irradiation with a fluence of 2.5 x 10^15 neq/cm2, the collected charge decreases from 40 fC to 7 fC, the signal-to-noise ratio deteriorates from 48 to 12, and the timing resolution increases from 29 ps to 39 ps.

We report precise TCAD simulations of IHEP-IME-v1 Low Gain Avalanche Diode (LGAD) calibrated by secondary ion mass spectroscopy (SIMS). Our setup allows us to evaluate the leakage current, capacitance, and breakdown voltage of LGAD, which agree with measurements' results before irradiation. And we propose an improved LGAD Radiation Damage Model (LRDM) which combines local acceptor removal with global deep energy levels. The LRDM is applied to the IHEP-IME-v1 LGAD and able to predict the leakage current well at -30 $^{\circ}$C after an irradiation fluence of $ \Phi_{eq}=2.5 \times 10^{15} ~n_{eq}/cm^{2}$. The charge collection efficiency (CCE) is under development.

The variational quantum eigensolver (VQE) is one of the most representative quantum algorithms in the noisy intermediate-size quantum (NISQ) era, and is generally speculated to deliver one of the first quantum advantages for the ground-state simulations of some non-trivial Hamiltonians. However, short quantum coherence time and limited availability of quantum hardware resources in the NISQ hardware strongly restrain the capacity and expressiveness of VQEs. In this Letter, we introduce the variational quantum-neural hybrid eigensolver (VQNHE) in which the shallow-circuit quantum ansatz can be further enhanced by classical post-processing with neural networks. We show that VQNHE consistently and significantly outperforms VQE in simulating ground-state energies of quantum spins and molecules given the same amount of quantum resources. More importantly, we demonstrate that for arbitrary post-processing neural functions, VQNHE only incurs an polynomial overhead of processing time and represents the first scalable method to exponentially accelerate VQE with non-unitary post-processing that can be efficiently implemented in the NISQ era., Comment: 4.6 pages, 3 figures, with supplemental materials

We address the determination of the time resolution for the $\rm 100~\mu m$ 4H-SiC PIN detectors fabricated by Nanjing University (NJU). The time response to $\rm \beta$ particles from a $\rm ^{90}$Sr source is investigated for the detection of the minimum ionizing particles (MIPs). We study the influence of different reverse voltages, which correspond to different carrier velocities and device sizes, and how this correlates with the detector capacitance. We determine a time resolution $\rm (94\pm1)~ps$ for $\rm 100~\mu m$ 4H-SiC PIN detector. A fast simulation software, termed RASER (RAdiation SEmiconductoR), is developed, and validated by comparing the waveform obtained from simulated and measured data. The simulated time resolution is $\rm (73\pm 1)~ps$ after considering the intrinsic leading contributions of the detector to time resolution.

In a commodity market, revenue adequate prices refer to compensations that ensure that a market participant has a non-negative profit. In this article, we study the problem of deriving revenue adequate prices for an electricity market-clearing model with uncertainties resulting from the use of variable renewable energy sources (VRES). To handle the uncertain nature of the problem, we use a chance-constrained optimization (CCO) approach, which has recently become very popular choice when constructing dispatch electricity models with penetration of VRES (or other sources of uncertainty). Then, we show how prices that satisfy revenue adequacy in expectation for the market administrator, and cost recovery in expectation for all conventional and VRES generators, can be obtained from the optimal dual variables associated with the deterministic equivalent of the CCO market-clearing model. These results constitute a novel contribution to the research of research on revenue adequate, equilibrium, and other types of pricing schemes that have been derived in the literature when the market uncertainties are modeled using stochastic or robust optimization approaches. Unlike in the stochastic approach, the CCO market-clearing model studied here produces uncertainty uniform real-time prices that do not depend on the real-time realization of the VRES generation outcomes. To illustrate our results, we consider a case study electricity market, and contrast the market prices obtained using a revenue adequate stochastic approach and the proposed revenue adequate CCO approach.

Two-dimensional polarity is intriguing but remains in the early stage. Here a structural evolution diagram is established for GeS monolayer, which leads a noncollinear ferrielectric $\delta$-phase energetically as stable as the ferroelectric $\alpha$-phase. Its ferrielectricity is induced by the phonon frustration, i.e., the competition between ferroelectric and antiferroelectric modes, providing more routes to tune its polarity. Besides its prominent properties like large band gap, large polarization, and high Curie temperature, more interestingly, the morphotropic phase boundary between $\alpha$- and $\delta$-phases is highly possible, which is crucial to obtain giant piezoelectricity for lead-free applications., Comment: 6 pages, 5 figures

The precision of the yield measurement of the Higgs boson decaying into a pair of $Z$ bosons process at the Circular Electron Positron Collider (CEPC) is evaluated. Including the recoil $Z$ boson associated with the Higgs production (Higgsstrahlung) total three $Z$ bosons are involved for this channel, from which final states characterized by the presence of a pair of leptons, quarks, and neutrinos are chosen for the signal. Two analysis approaches are compared and the final precision of ${\sigma}_{\mathrm{ZH}}{\cdot}$BR($H \rightarrow ZZ^{*}$) is estimated to be 7.9% using a multivariate analysis technique, based on boosted decision trees. The relative precision of the Higgs boson width, using this $H \rightarrow ZZ^{*}$ decay topology, is estimated by combining the obtained result with the precision of the inclusive \textit{ZH} cross section measurement.

Variational quantum algorithms (VQAs) are widely speculated to deliver quantum advantages for practical problems under the quantum-classical hybrid computational paradigm in the near term. Both theoretical and practical developments of VQAs share many similarities with those of deep learning. For instance, a key component of VQAs is the design of task-dependent parameterized quantum circuits (PQCs) as in the case of designing a good neural architecture in deep learning. Partly inspired by the recent success of AutoML and neural architecture search (NAS), quantum architecture search (QAS) is a collection of methods devised to engineer an optimal task-specific PQC. It has been proven that QAS-designed VQAs can outperform expert-crafted VQAs under various scenarios. In this work, we propose to use a neural network based predictor as the evaluation policy for QAS. We demonstrate a neural predictor guided QAS can discover powerful PQCs, yielding state-of-the-art results for various examples from quantum simulation and quantum machine learning. Notably, neural predictor guided QAS provides a better solution than that by the random-search baseline while using an order of magnitude less of circuit evaluations. Moreover, the predictor for QAS as well as the optimal ansatz found by QAS can both be transferred and generalized to address similar problems., Comment: 10.2 pages + supplemental materials, 10 figures