A large group of algorithms that were developed for quantum computers can now be easier understood and optimized, using a new framework developed by QuSoft researcher András Gilyén. In his PhD thesis Gilyén provides a framework that unifies quantum algorithms, while optimizing their hardware requirements. On 29 May he will publicly defend his thesis ‘Quantum Singular Value Transformation And Its Algorithmic Applications’ at the University of Amsterdam.
Quantum computers provide a new way of computation that is very different from classical computers. For certain tasks, quantum computers may be able to operate much more efficiently than classical computers, opening door to a whole new world of possibilities. However, so far the number of practically relevant applications is limited, and most of them require large quantum computers that are far from today’s quantum technology.
While better quantum hardware is gradually becoming available, researchers look for ways to gain a better understanding of how quantum computers will actually help solving important problems. During his PhD, András Gilyén developed tools that can speed up that process. He studied how efficiently quantum computers can solve various problems, and how large speed-ups can be achieved compared to classical computers.
Unifying quantum algorithms
Based on these insights, Gilyén developed a generic quantum algorithmic framework, called quantum singular value transformation. The framework unifies a large number of prominent quantum algorithms. These algorithms can be applied to a wide range of computational problems, e.g. performing machine learning tasks more efficiently, or understanding quantum mechanical properties of materials using quantum computers.
The framework not only makes it easier to understand existing quantum algorithms. It also allows researchers to design new, more efficient algorithms. ‘I hope it will make recent developments in quantum algorithms more accessible, even to people less familiar with quantum computing’, says Gilyén.
The new framework has the potential to become part of the generic toolbox used for quantum algorithm development, adds Gilyén. ‘Owing to its clarity, it could help discover new quantum algorithms, that can also be easily optimized and adapted to upcoming quantum hardware.’
Gilyén is part of the QuSoft team, a collaboration between CWI, the University of Amsterdam, and the Vrije Universiteit Amsterdam. In 2016 he was awarded the second prize at the Microsoft Quantum Challenge.
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