A thermodynamically consistent quasi-particle model without density-dependent infinity of the vacuum zero point energy

In this paper, we generalize the improved quasi-particle model proposed in J. Cao et al., [ Phys. Lett. B {\bf711}, 65 (2012)] from finite temperature and zero chemical potential to the case of finite chemical potential and zero temperature, and calculate the equation of state (EOS) for (2+1) flavor Quantum Chromodynamics (QCD) at zero temperature and high density. We first calculate the partition function at finite temperature and chemical potential, then go to the limit $T=0$ and obtain the equation of state (EOS) for cold and dense QCD, which is important for the study of neutron stars. Furthermore, we use this EOS to calculate the quark-number density, the energy density, the quark-number susceptibility and the speed of sound at zero temperature and finite chemical potential and compare our results with the corresponding ones in the existing literature

Optimizing measurement-based cooling by reinforcement learning

Conditional cooling-by-measurement holds a significant advantage over its unconditional (nonselective) counterpart in the average-population-reduction rate. However, it has a clear weakness with respect to the limited success probability of finding the detector in the measured state. In this work, we propose an optimized architecture to cool down a target resonator, which is initialized as a thermal state, using an interpolation of conditional and unconditional measurement strategies. An optimal measurement-interval $\tau_{\rm opt}^u$ for unconditional measurement is analytically derived for the first time, which is inversely proportional to the collective dominant Rabi frequency $\Omega_d$ as a function of the resonator's population in the end of the last round. A cooling algorithm under global optimization by the reinforcement learning results in the maximum value for the cooperative cooling performance, an indicator to measure the comprehensive cooling efficiency for arbitrary cooling-by-measurement architecture. In particular, the average population of the target resonator under only $16$ rounds of measurements can be reduced by four orders in magnitude with a success probability about $30\%$

An almost deterministic cooling by measurements

Nondeterministic measurement-based techniques are efficient in reshaping the population distribution of a quantum system but suffer from a limited success probability of holding the system in the target state. To reduce the experimental cost, we exploit the state-engineering mechanisms of both conditional and unconditional measurements and propose a two-step protocol assisted by a qubit to cool a resonator down to the ground state with a near-unit probability. In the first step, the unconditional measurements on the ancillary qubit are applied to reshape the target resonator from a thermal state to a reserved Fock state. The measurement sequence is optimized by reinforcement learning for a maximum fidelity. In the second step, the population on the reserved state can be faithfully transferred in a stepwise way to the resonator's ground state with a near-unit fidelity by the conditional measurements on the qubit. Intrinsic nondeterminacy of the projection-based conditional measurement is effectively inhibited by properly spacing the measurement sequence, which makes the Kraus operator act as a lowering operator for neighboring Fock states. Through dozens of measurements, the initial thermal average occupation of the resonator can be reduced by five orders in magnitude with a success probability over $95\%$.Comment: 11 pages, 5 figure

Charging by quantum measurement

We propose a quantum charging scheme fueled by measurements on ancillary qubits serving as disposable chargers. A stream of identical qubits are sequentially coupled to a quantum battery of $N+1$ levels and measured by projective operations after joint unitary evolutions of optimized intervals. If charger qubits are prepared in excited state and measured on ground state, then their excitations (energy) can be near-perfectly transferred to battery by iteratively updating the optimized measurement intervals. Starting from its ground state, the battery could be constantly charged to an even higher energy level. Starting from a thermal state, the battery could also achieve a near-unit ratio of ergotropy and energy through less than $N$ measurements, when a population inversion is realized by measurements. If charger qubits are prepared in ground state and measured on excited state, useful work extracted by measurements alone could transform the battery from a thermal state to a high-ergotropy state before the success probability vanishes. Our operations in charging are more efficient than those without measurements and do not invoke the initial coherence in both battery and chargers. Particularly, our finding features quantum measurement in shaping nonequilibrium systems

Online Updating of Statistical Inference in the Big Data Setting

We present statistical methods for big data arising from online analytical processing, where large amounts of data arrive in streams and require fast analysis without storage/access to the historical data. In particular, we develop iterative estimating algorithms and statistical inferences for linear models and estimating equations that update as new data arrive. These algorithms are computationally efficient, minimally storage-intensive, and allow for possible rank deficiencies in the subset design matrices due to rare-event covariates. Within the linear model setting, the proposed online-updating framework leads to predictive residual tests that can be used to assess the goodness-of-fit of the hypothesized model. We also propose a new online-updating estimator under the estimating equation setting. Theoretical properties of the goodness-of-fit tests and proposed estimators are examined in detail. In simulation studies and real data applications, our estimator compares favorably with competing approaches under the estimating equation setting.Comment: Submitted to Technometric

An optical fiber tip micrograting thermometer

An ~12 µm long Bragg grating was engraved in an ~5 µm diameter optical fiber tip by focused ion beam (FIB) milling. An ~10-dB extinction was achieved at 1570 nm with only 11 indentations. The grating was used for temperature sensing, and it exhibited a temperature sensitivity of ~22 pm/°C

N′-(2-Hy­droxy-4-meth­oxy­benzyl­idene)-4-methyl­benzohydrazide

The asymmetric unit of the title compound, C16H16N2O3, contains four independent mol­ecules with different conformations; the dihedral angles between the two benzene rings in the mol­ecules are 39.7 (3), 45.4 (3), 50.6 (3) and 51.6 (3)°. Intramolecular O—H⋯N hydrogen bonds are observed in the molecule. In the crystal, N—H⋯O hydrogen bonds link the mol­ecules into two crystallographically independent chains propagating in [010], and each chain is formed by two alternating independent mol­ecules. Weak C—H⋯O inter­actions also occur