The ICoMPAC 2021 will feature four keynote speakers addressing "Mathematics of Quantum Computing" as the theme of ICoMPAC 2021. The plenary keynotes are intended to fire up the conference attendees at the beginning of the conference day and provide new insight to stimulate discussion for networking in the end of the day.
Nanyang Technological University
Title: A Brief Intro to Quantum Stabilizer
Turning large-scale quantum computing into practical reality is massively challenging. It requires techniques for error control that are much more complex than those implemented effectively in classical systems. Quantum error-control is a set of methods to protect quantum information from decoherence. A firm bridge between classical coding theory and quantum error control comes in the form of the stabilizer formalism, allowing the use of classical codewords to model quantum error operators. Well-researched tools and results in classical coding theory are useful to design good quantum codes. Research problems triggered by quantum error-control issues highlight previously less explored topics in classical codes.
Tokyo Institute of Technology
Title: Quantum Annealing for Balance Optimization Subset Selection
A/B Testing of experimental design is widely used in various fields, for example, in medicine, it is used to evaluate the impact of age and obesity after surgery. A key to improving the performance of A/B Testing is a balanced split of samples into two groups. Nikolaev et al. formulated such a problem as balance optimization subset selection (BOSS), but it is shown that BOSS is an NP-hard problem, hence, solving BOSS demands a long computation time.
In this talk, we consider a numerical method for BOSS that utilizes quantum annealing. BOSS can be reformulated as a sum-of-ratios optimization problem with binary variables. We combine quantum annealing, alternating direction method of multipliers (ADMM) and Dinkelbach’s algorithm. We also report preliminary numerical results to compare simulated annealing and quantum annealing.
Institut Teknologi Sepuluh Nopember
Title: State of the art of machine learning: an overview of the past, currently and the future research trends in the area of quantum computing
We review the historical trends of machine learning research across one of the biggest platforms for learning and competition in analyzing and modeling data, namely Kaggle.com. We analyze the historical of methods frequently used in machine learning to predict, classify, categorize and explore data sets. In addition, we also analyze the use of the most effective tools and frameworks that are widely used for other sources, namely publications in indexed journals limited to sciencedirect.com. The two steps that we take are as follows: First, the analysis is carried out on discussion forum data for the last ten years based on the data available in meta-Kaggle. Second, to look at future trends of machine learning models, we analyzed the abstracts of articles available on Elsevier's search page and extracted information from them using machine learning methods. We utilize the results of extracting information from the two sources above to gain popularity and research trends that describe quantum computing.