Quantum-inspired low-rank stochastic regression with logarithmic dependence on the dimension

Add to your list(s) Download to your calendar using vCal

• Andras Gilyen, QuSoft / CWI / University of Amsterdam
• Thursday 13 June 2019, 14:15-15:15
• MR13, Centre for Mathematical Sciences, Wilberforce Road, Cambridge.

If you have a question about this talk, please contact Johannes Bausch.

I will present an efficient classical analogue of the quantum matrix inversion algorithm (HHL) for low-rank matrices. Inspired by recent work of Tang, assuming length-square sampling access to input data, we implement the pseudoinverse of a low-rank matrix and sample from the solution to the problem Ax=b using fast sampling techniques. We implement the pseudo-inverse by finding an approximate singular value decomposition of A via subsampling, then inverting the singular values. In principle, the approach can also be used to apply any desired “smooth” function to the singular values. Since many quantum algorithms can be expressed as a singular value transformation problem, our result suggests that more low-rank quantum algorithms can be effectively “dequantised” into classical length-square sampling algorithms.

Joint work with: Seth Lloyd and Ewin Tang – https://arxiv.org/abs/1811.04909

This talk is part of the CQIF Seminar series.

This talk is included in these lists:

• All CMS events
• CMS Events
• CQIF Seminar
• DAMTP info aggregator
• Hanchen DaDaDash
• Interested Talks
• MR13, Centre for Mathematical Sciences, Wilberforce Road, Cambridge
• bld31

Note that ex-directory lists are not shown.