Excluding false negative error in certification of quantum channels

Certification of quantum channels is based on quantum hypothesis testing and involves also preparation of an input state and choosing the final measurement. This work primarily focuses on the scenario when the false negative error cannot occur, even if it leads to the growth of the probability of false positive error. We establish a condition when it is possible to exclude false negative error after a finite number of queries to the quantum channel in parallel, and we provide an upper bound on the number of queries. On top of that, we found a class of channels which allow for excluding false negative error after a finite number of queries in parallel, but cannot be distinguished unambiguously. Moreover, it will be proved that parallel certification scheme is always sufficient, however the number of steps may be decreased by the use of adaptive scheme. Finally, we consider examples of certification of various classes of quantum channels and measurements.

Feasibility of FT-NIR spectroscopy and Vis/NIR hyperspectral imaging for sorting unsound chestnuts

Authors explored the potential use of Vis/NIR hyperspectral imaging (HSI) and Fourier-transform Near-Infrared (FT-NIR) spectroscopy to be used as in-line tools for the detection of unsound chestnut fruits (i.e. infected and/or infested) in comparison with the traditional sorting technique. For the intended purpose, a total of 720 raw fruits were collected from a local company. Chestnut fruits were preliminarily classified into sound (360 fruits) and unsound (360 fruits) batches using a proprietary floating system at the facility along with manual selection performed by expert workers. The two batches were stored at 4 ± 1 °C until use. Samples were left at ambient temperature for at least 12 h before measurements. Subsequently, fruits were subjected to non-destructive measurements (i.e. spectral analysis) immediately followed by destructive analyses (i.e. microbiological and entomological assays). Classification models were trained using the Partial Least Squares Discriminant Analysis (PLS-DA) by pairing the spectrum of each fruit with the categorical information obtained from its destructive assay (i.e., sound, Y = 0; unsound, Y = 1). Categorical data were also used to evaluate the classification performance of the traditional sorting method. The performance of each PLS-DA model was evaluated in terms of false positive error (FP), false negative error (FN) and total error (TE) rates. The best result (8% FP, 14% FN, 11% TE) was obtained using Savitzky-Golay first derivative with a 5-points window of smoothing on the dataset of raw reflectance spectra scanned from the hilum side of fruit using the Vis/NIR HSI setup. This model showed similarity in terms of False Negative error rate with the best one computed using data from the FT-NIR setup (i.e. 15% FN), which, however, had the lowest global performance (17% TE) due to the highest False Positive error rate (19%). Finally, considering that the total error rate committed by the traditional sorting system was about 14.5% with a tendency of misclassifying unsound fruits, the results indicate the feasibility of a rapid, in-line detection system based on spectroscopic measurements.

NI-07 VALIDATION OF MACHINE LEARNING BASED HIGH GRADE GLIOMA MR SEGMENTATION VIA METHIONINE PET

Treatment planning and lesion-follow up are generally conducted by contrast-enhanced MRI in glioma patient care. On the other hand, there are, however, substantial concerns whether MRI actually reflects the extension or activity of this neoplasm, which information should be fundamentally important at every step when treating this disease. As a matter of fact, the authors of this investigation have already shown that there is no difference in tumor cell density within areas with and without contrast enhancement (J Neurosurg. 2016,125(5):1136–1142.) and furthermore that the geometry of MRI based-radiation treatment planning is significantly altered when methionine PET is integrated for this purpose (J Neurosurg. 2018 published on-line). Regardless of these concerns, there is great interest in the research community to construct a machine learning based fully automated brain tumor segmentation tool specific for gliomas using MRI. The authors attempted to validate this method by comparing MRI-based automated brain tumor segmentation and methionine PET. Consecutively collected 45 high-grade gliomas (GBM-26, grade3-19) were analyzed. BraTumIA, an automated brain tumor segmentation tool, was used for machine learning based lesion segmentation. At the same time, lesions were segmented using various thresholds on methionine PET. The authors observed 40% of pseudo-positive and 90% of pseudo-negative error on BraTumIA based lesion segmentation when methionine PET was considered as ground truth with a cut-off of 1.3 in T/N ratio. Pseudo-negative error was as high as 60% even if the threshold was elevated to 2.0. Although machine learning based glioma segmentation is expected to expand in both research and clinical use, the observed results caution the use of MRI as ground truth of spatial extension of glioma and researchers should be reminded that this imaging modality may obscure the true behavior of the disease within the patient in some cases.