Band structure renormalization and weak pseudogap behavior in Na_{0.33}CoO_2: Fluctuation exchange study based on a single band model

Based on a single band Hubbard model and the fluctuation exchange approximation, the effective mass and the energy band renormalization in Na$_{0.33}$CoO$_2$ is elaborated. The renormalization is observed to exhibit certain kind of anisotropy, which agrees qualitatively with the angle-resolved photoemission spectroscopy (ARPES) measurements. Moreover, the spectral function and density of states (DOS) in the normal state are calculated, with a weak pseudogap behavior being seen, which is explained as a result of the strong Coulomb correlations. Our results suggest that the large Fermi surface (FS) associated with the $a_{1g}$ band plays likely a central role in the charge dynamics.Comment: 5 pages, 5 figure

Orbital-transverse density-wave instabilities in iron-based superconductors

Besides the conventional spin-density-wave (SDW) state, a new kind of orbital-transverse density-wave (OTDW) state is shown to exist generally in multi-orbital systems. We demonstrate that the orbital character of Fermi surface nesting plays an important role in density responses. The relationship between antiferromagnetism and structural phase transition in LaFeAsO (1111) and BaFe$_2$As$_2$ (122) compounds of iron-based superconductors may be understood in terms of the interplay between the SDW and OTDW with a five-orbital Hamiltonian. We propose that the essential difference between 1111 and 122 compounds is crucially determined by the presence of the two-dimensional $d_{xy}$-like Fermi surface around (0,0) being only in 1111 parent compounds.Comment: several parts were rewritten for clarity. 6 pages, 3 figures, 1 tabl

Possible singlet and triplet superconductivity on honeycomb lattice

We study the possible superconducting pairing symmetry mediated by spin and charge fluctuations on the honeycomb lattice using the extended Hubbard model and the random-phase-approximation method. From $2\%$ to $20\%$ doping levels, a spin-singlet $d_{x^{2}-y^{2}}+id_{xy}$-wave is shown to be the leading superconducting pairing symmetry when only the on-site Coulomb interaction $U$ is considered, with the gap function being a mixture of the nearest-neighbor and next-nearest-neighbor pairings. When the offset of the energy level between the two sublattices exceeds a critical value, the most favorable pairing is a spin-triplet $f$-wave which is mainly composed of the next-nearest-neighbor pairing. We show that the next-nearest-neighbor Coulomb interaction $V$ is also in favor of the spin-triplet $f$-wave pairing.Comment: 6 pages, 4 figure

LMSFC: A Novel Multidimensional Index based on Learned Monotonic Space Filling Curves

The recently proposed learned indexes have attracted much attention as they can adapt to the actual data and query distributions to attain better search efficiency. Based on this technique, several existing works build up indexes for multi-dimensional data and achieve improved query performance. A common paradigm of these works is to (i) map multi-dimensional data points to a one-dimensional space using a fixed space-filling curve (SFC) or its variant and (ii) then apply the learned indexing techniques. We notice that the first step typically uses a fixed SFC method, such as row-major order and z-order. It definitely limits the potential of learned multi-dimensional indexes to adapt variable data distributions via different query workloads. In this paper, we propose a novel idea of learning a space-filling curve that is carefully designed and actively optimized for efficient query processing. We also identify innovative offline and online optimization opportunities common to SFC-based learned indexes and offer optimal and/or heuristic solutions. Experimental results demonstrate that our proposed method, LMSFC, outperforms state-of-the-art non-learned or learned methods across three commonly used real-world datasets and diverse experimental settings.Comment: Extended Version. Accepted by VLDB 202

Training variational quantum algorithms with random gate activation

Variational quantum algorithms (VQAs) hold great potentials for near-term applications and are promising to achieve quantum advantage on practical tasks. However, VQAs suffer from severe barren plateau problem as well as have a large probability of being trapped in local minima. In this Letter, we propose a novel training algorithm with random quantum gate activation for VQAs to efficiently address these two issues. This new algorithm processes effectively much fewer training parameters than the conventional plain optimization strategy, which efficiently mitigates barren plateaus with the same expressive capability. Additionally, by randomly adding two-qubit gates to the circuit ansatz, the optimization trajectories can escape from local minima and reach the global minimum more frequently due to more sources of randomness. In real quantum experiments, the new training algorithm can also reduce the quantum computational resources required and be more quantum noise resilient. We apply our training algorithm to solve variational quantum simulation problems for ground states and present convincing results that showcase the advantages of our novel strategy where better performance is achieved by the combination of mitigating barren plateaus, escaping from local minima, and reducing the effect of quantum noises. We further propose that the entanglement phase transition could be one underlying reason why our RA training is so effective.Comment: 4.5 pages + references + supplemental, 4 figure