▁projects ▁to ▁choose ▁from ; ▁among ▁them ▁was ▁the ▁recommendation ▁problem . ▁ ▁Research ▁ ▁Before ▁Tang ' s ▁work , ▁the ▁best ▁known ▁classical ▁algorithms ▁solving ▁some ▁linear ▁algebra ▁problems ▁where ▁expon entially ▁slower , ▁under ▁some ▁assumptions , ▁than ▁the ▁best ▁quantum ▁algorithm ▁for ▁the ▁same ▁ ▁problem . ▁In sp ired ▁by ▁the ▁quantum ▁solution , ▁based ▁on ▁the ▁H HL ▁algorithm , ▁she ▁found ▁classical ▁algorithms ▁solving ▁these ▁problem ▁in ▁a ▁similar ▁time ▁than ▁the ▁quantum ▁algorithms , ▁under ▁similar ▁assumptions , ▁thus ▁" de quant izing " ▁them ▁and ▁expon entially ▁impro ving ▁over ▁the ▁best ▁known ▁classical ▁algorithms . ▁ ▁Her ▁first ▁work ▁in ▁quantum ▁computing ▁was ▁her ▁ 2 0 1 8 ▁th esis ▁dis sert ation ▁titled ▁A ▁quantum - in sp ired ▁classical ▁algorithm ▁for ▁recommendation ▁systems , ▁super vised ▁by ▁Scott ▁A ar ons on , ▁allowing ▁her ▁to ▁complete ▁two ▁under grad uate ▁degrees ▁in ▁computer ▁science ▁and ▁in ▁pure ▁mathematics ▁from ▁the ▁U T ▁Austin . ▁This ▁work ▁details ▁a ▁new ▁algorithm ▁that ▁solves ▁the ▁recommendation ▁problem ; ▁for ▁example , ▁how ▁does ▁Amazon ▁or ▁Net fli x ▁predict ▁which ▁books ▁or ▁mov ies ▁a ▁specific ▁consumer ▁will ▁personally ▁enjoy ? ▁ ▁A ▁linear ▁algebraic ▁approach ▁of ▁the ▁problem ▁is ▁the ▁following : ▁given ▁m ▁users , ▁and ▁n ▁products , ▁alongside ▁incomplete ▁data ▁about ▁which ▁products ▁the ▁users ▁prefer ▁( organ ised ▁in ▁a ▁binary ▁tree ▁data ▁structure ) ▁where ▁there ▁are ▁not ▁too ▁many ▁ways ▁the ▁users ▁can ▁vary