We are excited to share that our team “TIAKong” secured leading positions in the recent PUMALink opens in a new window (Panoptic segmentation of nuclei and tissue in advanced Melanoma) Challenge, organized by the Department of Medical Oncology, University Medical Center Utrecht, in the Netherlands. With over 300 participants from around the globe, this challenge aimed to advance automated panoptic segmentation techniques for H&E-stained melanoma tissue images.

Led by our PhD students Jiaqi Lv and YiJie Zhu, and supported by Brinder Singh Chohan, Shan E Ahmed Raza, with an external collaborator Carmen Guadalupe Colin Tenorio from the Medical University of Vienna. TIAKong achieved first place in Track 1 and second place in Track 2. This outstanding performance underscores the team’s dedication to pushing the boundaries of medical imaging and improving our understanding of advanced melanoma.

We look forward to building on these results and sharing further developments of our panoptic segmentation model in the near future.

We are excited to announce that our team “TIAKong” secured leading positions in the recent Monkey Grand ChallengeLink opens in a new window, organized by the Department of Pathology, Radboudumc, Nijmegen, The Netherlands. Drawing more than 400 participants from around the globe, the challenge focused on automated detection and classification of mononuclear leukocytes in PAS-stained transplant kidney biopsy images.

Led by our PhD student Jiaqi Lv, and supported by Esha Nasir, Kesi Xu, Mostafa Jahanifar, Brinder Singh Chohan, Behnaz Elhaminia, and Shan E Ahmed Raza, TIAKong’s cell detection and classification model finished first place in the overall detection track and second place in the detection classification track.

The team is currently evaluating the model for publishing and sharing the code through open-source platforms. We look forward to sharing more updates in the near future.

A paper co-authored by Matthias C. Caro has been featured on the cover of PRX Quantum. PRX Quantum is a premier journal for quantum information science and technology research. The work was a collaboration with Haimeng Zhao (Caltech & Tsinghua), Laura Lewis (Caltech & Google), Ishaan Kannan (Caltech), Yihui Quek (Harvard & MIT) and Hsin-Yuan Huang (Caltech, Google & MIT).

Characterizing a quantum system by learning its state or unitary evolution is a key tool in developing quantum devices, with applications in practical quantum machine learning, benchmarking, and error mitigation. However, in general, this task requires exponentially many resources. Prior knowledge is required to circumvent this exponential bottleneck. The paper pinpoints the complexity for learning states and unitaries that can be implemented by quantum circuits with a bounded number of gates, a broad setting that is topical for current quantum technologies. When measuring efficiency with respect to the number of accesses to the unknown quantum state or unitary, the paper presents and implements algorithms that are provably optimally efficient. Thereby, this work establishes the equivalence between the complexity of learning quantum states or unitaries and the complexity of creating them. However, it also shows that the data processing necessarily requires exponential computation time under reasonable cryptographic assumptions.