Quantum Algorithm for Approximating Maximum Independent Sets

We present a quantum algorithm for approximating maximum independent sets of a graph based on quantum non-Abelian adiabatic mixing in the sub-Hilbert space of degenerate ground states, which generates quantum annealing in a secondary Hamiltonian. For both sparse and dense graphs, our quantum algorithm on average can find an independent set of size very close to $\alpha(G)$, which is the size of the maximum independent set of a given graph $G$. Numerical results indicate that an $O(n^2)$ time complexity quantum algorithm is sufficient for finding an independent set of size $(1-\epsilon)\alpha(G)$. The best classical approximation algorithm can produce in polynomial time an independent set of size about half of $\alpha(G)$

Efficient separate quantification of state preparation errors and measurement errors on quantum computers and their mitigation

Current noisy quantum computers have multiple types of errors, which can occur in the state preparation, measurement/readout, and gate operation, as well as intrinsic decoherence and relaxation. Partly motivated by the booming of intermediate-scale quantum processors, measurement and gate errors have been recently extensively studied, and several methods of mitigating them have been proposed and formulated in software packages (e.g., in IBM Qiskit). Despite this, the state preparation error and the procedure to quantify it have not yet been standardized, as state preparation and measurement errors are usually considered not directly separable. Inspired by a recent work of Laflamme, Lin, and Mor [Phys. Rev. A 106, 012439 (2022)], we propose a simple and resource-efficient approach to quantify separately the state preparation and readout error rates. With these two errors separately quantified, we also propose methods to mitigate them separately, especially mitigating state preparation errors with linear (with the number of qubits) complexity. As a result of the separate mitigation, we show that the fidelity of the outcome can be improved by an order of magnitude compared to the standard measurement error mitigation scheme. We also show that the quantification and mitigation scheme is resilient against gate noise and can be immediately applied to current noisy quantum computers. To demonstrate this, we present results from cloud experiments on IBM's superconducting quantum computers. The results indicate that the state preparation error rate is also an important metric for qubit metrology that can be efficiently obtained.Comment: 10 pages, 6 figure

Simulating large-size quantum spin chains on cloud-based superconducting quantum computers

Quantum computers have the potential to efficiently simulate large-scale quantum systems for which classical approaches are bound to fail. Even though several existing quantum devices now feature total qubit numbers of more than one hundred, their applicability remains plagued by the presence of noise and errors. Thus, the degree to which large quantum systems can successfully be simulated on these devices remains unclear. Here, we report on cloud simulations performed on several of IBM's superconducting quantum computers to simulate ground states of spin chains having a wide range of system sizes up to one hundred and two qubits. We find that the ground-state energies extracted from realizations across different quantum computers and system sizes reach the expected values to within errors that are small (i.e. on the percent level), including the inference of the energy density in the thermodynamic limit from these values. We achieve this accuracy through a combination of physics-motivated variational Ansatzes, and efficient, scalable energy-measurement and error-mitigation protocols, including the use of a reference state in the zero-noise extrapolation. By using a 102-qubit system, we have been able to successfully apply up to 3186 CNOT gates in a single circuit when performing gate-error mitigation. Our accurate, error-mitigated results for random parameters in the Ansatz states suggest that a standalone hybrid quantum-classical variational approach for large-scale XXZ models is feasible.Comment: 21 pages, 12 figures, 4 tables; title change; substantial revisio

Deep Domain Adversarial Adaptation for Photon-efficient Imaging

Photon-efficient imaging with the single-photon light detection and ranging (LiDAR) captures the three-dimensional (3D) structure of a scene by only a few detected signal photons per pixel. However, the existing computational methods for photon-efficient imaging are pre-tuned on a restricted scenario or trained on simulated datasets. When applied to realistic scenarios whose signal-to-background ratios (SBR) and other hardware-specific properties differ from those of the original task, the model performance often significantly deteriorates. In this paper, we present a domain adversarial adaptation design to alleviate this domain shift problem by exploiting unlabeled real-world data, with significant resource savings. This method demonstrates superior performance on simulated and real-world experiments using our home-built up-conversion single-photon imaging system, which provides an efficient approach to bypass the lack of ground-truth depth information in implementing computational imaging algorithms for realistic applications

The bracteatus pineapple genome and domestication of clonally propagated crops

Domestication of clonally propagated crops such as pineapple from South America was hypothesized to be a 'one-step operation'. We sequenced the genome of Ananas comosus var. bracteatus CB5 and assembled 513 Mb into 25 chromosomes with 29,412 genes. Comparison of the genomes of CB5, F153 and MD2 elucidated the genomic basis of fiber production, color formation, sugar accumulation and fruit maturation. We also resequenced 89 Ananas genomes. Cultivars 'Smooth Cayenne' and 'Queen' exhibited ancient and recent admixture, while 'Singapore Spanish' supported a one-step operation of domestication. We identified 25 selective sweeps, including a strong sweep containing a pair of tandemly duplicated bromelain inhibitors. Four candidate genes for self-incompatibility were linked in F153, but were not functional in self-compatible CB5. Our findings support the coexistence of sexual recombination and a one-step operation in the domestication of clonally propagated crops. This work guides the exploration of sexual and asexual domestication trajectories in other clonally propagated crops

NTIRE 2023 Quality Assessment of Video Enhancement Challenge

This paper reports on the NTIRE 2023 Quality Assessment of Video Enhancement Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at CVPR 2023. This challenge is to address a major challenge in the field of video processing, namely, video quality assessment (VQA) for enhanced videos. The challenge uses the VQA Dataset for Perceptual Video Enhancement (VDPVE), which has a total of 1211 enhanced videos, including 600 videos with color, brightness, and contrast enhancements, 310 videos with deblurring, and 301 deshaked videos. The challenge has a total of 167 registered participants. 61 participating teams submitted their prediction results during the development phase, with a total of 3168 submissions. A total of 176 submissions were submitted by 37 participating teams during the final testing phase. Finally, 19 participating teams submitted their models and fact sheets, and detailed the methods they used. Some methods have achieved better results than baseline methods, and the winning methods have demonstrated superior prediction performance

Household, community, sub-national and country-level predictors of primary cooking fuel switching in nine countries from the PURE study

Introduction. Switchingfrom polluting (e.g. wood, crop waste, coal)to clean (e.g. gas, electricity) cooking fuels can reduce household air pollution exposures and climate-forcing emissions.While studies have evaluated specific interventions and assessed fuel-switching in repeated cross-sectional surveys, the role of different multilevel factors in household fuel switching, outside of interventions and across diverse community settings, is not well understood. Methods.We examined longitudinal survey data from 24 172 households in 177 rural communities across nine countries within the Prospective Urban and Rural Epidemiology study.We assessed household-level primary cooking fuel switching during a median of 10 years offollow up (∼2005–2015).We used hierarchical logistic regression models to examine the relative importance of household, community, sub-national and national-level factors contributing to primary fuel switching. Results. One-half of study households(12 369)reported changing their primary cookingfuels between baseline andfollow up surveys. Of these, 61% (7582) switchedfrom polluting (wood, dung, agricultural waste, charcoal, coal, kerosene)to clean (gas, electricity)fuels, 26% (3109)switched between different polluting fuels, 10% (1164)switched from clean to polluting fuels and 3% (522)switched between different clean fuels