Synopsis: Quantum Computers Have a Fit



Synopsis: Quantum Computers Have a Fit
http://physics.aps.org/synopsis-for/10.1103/PhysRevLett.109.050505

As researchers were snaring the Higgs boson at CERN, the LHC machines were cranking out gigabytes of data each second. Even with the uninteresting bits filtered out, modern large-scale science creates mind-boggling amounts of data, causing standard techniques like curve fitting to run into a brick wall. Quantum computing—harnessing nonlocality and entanglement to make solving really hard problems more efficient—might have the prescription for this headache. In a paper in Physical Review Letters, Nathan Wiebe at the University of Waterloo, Canada, and colleagues propose an algorithm to improve the data analyzer’s best friend, least-squares fitting, on a quantum computer.

The authors built upon earlier theoretical work by Harrow et al. [see Phys. Rev. Lett. 103, 150502 (2009)] investigating a quantum method for finding expectation values of the solutions to systems of linear equations. Wiebe et al. adapt this algorithm to estimate the quality of a least-squares fit to an exponentially large data set (the kind that stymies classical computers) without having to obtain a full solution first and without having to fully characterize the state of the quantum computer (a process called quantum state tomography).

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-Sam Wormley
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