Prediction of progression in idiopathic pulmonary fibrosis using CT scans atbaseline: A quantum particle swarm optimization - Random forest approach

Idiopathic pulmonary fibrosis (IPF) is a fatal lung disease characterized by an unpredictable progressive declinein lung function. Natural history of IPF is unknown and the prediction of disease progression at the time ofdiagnosis is notoriously difficult. High resolution computed tomography (HRCT) has been used for the diagnosisof IPF, but not generally for monitoring purpose. The objective of this work is to develop a novel predictivemodel for the radiological progression pattern at voxel-wise level using only baseline HRCT scans. Mainly, thereare two challenges: (a) obtaining a data set of features for region of interest (ROI) on baseline HRCT scans andtheir follow-up status; and (b) simultaneously selecting important features from high-dimensional space, andoptimizing the prediction performance. We resolved the first challenge by implementing a study design andhaving an expert radiologist contour ROIs at baseline scans, depending on its progression status in follow-upvisits. For the second challenge, we integrated the feature selection with prediction by developing an algorithmusing a wrapper method that combines quantum particle swarm optimization to select a small number of featureswith random forest to classify early patterns of progression. We applied our proposed algorithm to analyzeanonymized HRCT images from 50 IPF subjects from a multi-center clinical trial. We showed that it yields aparsimonious model with 81.8% sensitivity, 82.2% specificity and an overall accuracy rate of 82.1% at the ROIlevel. These results are superior to other popular feature selections and classification methods, in that ourmethod produces higher accuracy in prediction of progression and more balanced sensitivity and specificity witha smaller number of selected features. Our work is the first approach to show that it is possible to use onlybaseline HRCT scans to predict progressive ROIs at 6 months to 1year follow-ups using artificial intelligence

First-principles thermodynamic modeling of atomic ordering in yttria-stabilized zirconia

Yttria-stabilized zirconia YSZ is modeled using a cluster expansion statistical thermodynamics method built upon a density-functional theory database. The reliability of cluster expansions in predicting atomic ordering is explored by comparing with the extensive experimental database. The cluster expansion of YSZ is utilized in lattice Monte Carlo simulations to compute the ordering of dopant and oxygen vacancies as a function of concentration. Cation dopants show a strong tendency to aggregate and vacate significantly sized domains below 9 mol % Y_2O_3, which is likely important for YSZ aging processes in ionic conductivity. Evolution of vibrational and underlying electronic properties as a function of Y doping is explored

Physical and chemical conditions in methanol maser selected hot-cores and UCHII regions

We present the results of a targeted 3-mm spectral line survey towards the eighty-three 6.67 GHz methanol maser selected star forming clumps observed by Purcell et al. 2006. In addition to the previously reported measurements of HCO+ (1 - 0), H13CO+ (1 - 0), and CH3CN (5 - 4) & (6 -5), we used the Mopra antenna to detect emission lines of N2H+ (1 - 0), HCN (1 - 0) and HNC (1 - 0) towards 82/83 clumps (99 per cent), and CH3OH (2 - 1) towards 78/83 clumps (94 per cent). The molecular line data have been used to derive virial and LTE masses, rotational temperatures and chemical abundances in the clumps, and these properties have been compared between sub-samples associated with different indicators of evolution. The greatest differences are found between clumps associated with 8.6 GHz radio emission, indicating the presence of an Ultra-Compact HII region, and `isolated' masers (without associated radio emission), and between clumps exhibiting CH3CN emission and those without. In particular, thermal CH3OH is found to be brighter and more abundant in Ultra-Compact HII (UCHII) regions and in sources with detected CH3CN, and may constitute a crude molecular clock in single dish observations. Clumps associated with 8.6 GHz radio emission tend to be more massive and more luminous than clumps without radio emission. This is likely because the most massive clumps evolve so rapidly that a Hyper-Compact HII or UCHII region is the first visible tracer of star-formation. The gas-mass to sub-mm/IR luminosity relation for the combined sample was found to be L proportional to M**0.68, considerably shallower than expected for massive main-sequence stars

Quantum circuit architecture search: error mitigation and trainability enhancement for variational quantum solvers

Quantum error mitigation techniques are at the heart of quantum hardware implementation, and are the key to performance improvement of the variational quantum learning scheme (VQLS). Although VQLS is partially robust to noise, both empirical and theoretical results exhibit that noise would rapidly deteriorate the performance of most variational quantum algorithms in large-scale problems. Furthermore, VQLS suffers from the barren plateau phenomenon---the gradient generated by the classical optimizer vanishes exponentially with respect to the qubit number. Here we devise a resource and runtime efficient scheme, the quantum architecture search scheme (QAS), to maximally improve the robustness and trainability of VQLS. In particular, given a learning task, QAS actively seeks an optimal circuit architecture to balance benefits and side-effects brought by adding more quantum gates. Specifically, while more quantum gates enable a stronger expressive power of the quantum model, they introduce a larger amount of noise and a more serious barren plateau scenario. Consequently, QAS can effectively suppress the influence of quantum noise and barren plateaus. We implement QAS on both the numerical simulator and real quantum hardware, via the IBM cloud, to accomplish data classification and quantum chemistry tasks. Numerical and experimental results show that QAS significantly outperforms conventional variational quantum algorithms with heuristic circuit architectures. Our work provides practical guidance for developing advanced learning-based quantum error mitigation techniques on near-term quantum devices.Comment: 8+9 pages. See also a concurrent paper that appeared yesterday [arXiv:2010.08561