Bisection Grover's Search Algorithm

Ping Ma
University of Georgia
Tuesday 2nd July 2024
B.5.3, Edificio 14, Politecnico di Milano
Recent breakthroughs in quantum computers have shown quantum advantage (aka quantum supremacy), i.e., quantum computers outperform classic computers for solving specific problems. These problems are highly physics-oriented and may not necessarily appeal to researchers in other fields. For these researchers, A more relevant fact is that there are already general-purpose programmable quantum computing devices available to the public, e.g., IBM Quantum Experience, Microsoft Quantum, and Amazon Braket. Such quantum devices are commonly known as noisy intermediate-scale quantum (NISQ) devices (Preskill, 2018, Quantum). Although current NISQ devices only have a handful of qubits, it is expected that NISQ with thousands of qubits will be available in the next couple of years. Since many statistical problems involve computationally intensive tasks, statisticians are particularly intrigued by the potential of quantum computers. Consequently, a natural question for statisticians is whether these computers will benefit the statisticians in solving some statistics or data science problems. If the answer is affirmative, what kind of statistics problems should statisticians resort to quantum computers? Unfortunately, the general answer to this question remains elusive. In this talk, I answer this question by showing the benefit of quantum computing in data science problems that statisticians have been working on extensively. The visit of Prof Ping Ma is part of the activities of the PRIN research project CoEnv - Complex Environmental Data and Modeling, funded by the Italian Ministry for University and Research and by the NextGenerationEU programme of the European Union.