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Seth Lloyd from @MIT is visiting us at LIP6 and presents his work with András Gilyén (from @QuSoftAmsterdam ) and @ewintang (from @UW) on Quantum-inspired low-rank stochastic regression with logarithmic dependence on the dimension arXiv:1811.04909 arxiv.org/abs/1811.04909
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#LTQI
@MIT @QuSoftAmsterdam @ewintang @UW Seth Lloyd: presents the recommendation problem. For m users, n movies, a n×m matrix A encodes the movie preferences. The goal is to recommend movies a user likes, given their previous preferences.
A is assumed to be low rank (the rank is the effective number of genres).
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A is assumed to be low rank (the rank is the effective number of genres).
#LTQI
@MIT @QuSoftAmsterdam @ewintang @UW Seth Lloyd: The @netflix way to solve this problem is through singular value decomposition of matrix A.
A=∑_l σ_l U^l V^l ^T
σ_l: singular values
U^l and V^l are left/right singular vectors
A V^l= σ_l U^l V^l
In short V^l^T b = p_l encodes weighted preference for genre l
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A=∑_l σ_l U^l V^l ^T
σ_l: singular values
U^l and V^l are left/right singular vectors
A V^l= σ_l U^l V^l
In short V^l^T b = p_l encodes weighted preference for genre l
#LTQI
@MIT @QuSoftAmsterdam @ewintang @UW @netflix Seth Lloyd: This method works only with low rank matrices/operation. It works for sampling, but e.g. you cannot classically “simulate” QFT of the vector (⇒you cannot dequantize Shor), you probably cannot use it for quantum simulation (needs exponential of high rank matrix).#LTQI
@MIT @QuSoftAmsterdam @ewintang @UW @netflix I think I misunderstood Seth Lloyd. In his portfolio quantum algorithm arXiv:1811.04909 arxiv.org/abs/1811.04909 needs the evaluation of a high rank observable O, so @ewintang’s does not apply.
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#LTQI
@MIT @QuSoftAmsterdam @ewintang @UW @netflix Seth Lloyd: «In short, @ewingtang does not harm much of quantum computing community, except myself, and maybe Kerenidis and Prakash»
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#LTQI
@MIT @QuSoftAmsterdam @ewintang @UW @netflix @EwingTang Seth Lloyd now explains how the algorithm works. The pseudo inverse is
A⁺=∑_l σ_l⁻¹ U^l V^l ^T
min |Ax-b| is given by x=A⁺b
Goal: given b, a_ij elements of A, samples from entries x_i of x with probabilities ∝x_i² (l² samples)
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A⁺=∑_l σ_l⁻¹ U^l V^l ^T
min |Ax-b| is given by x=A⁺b
Goal: given b, a_ij elements of A, samples from entries x_i of x with probabilities ∝x_i² (l² samples)
#LTQI
@MIT @QuSoftAmsterdam @ewintang @UW @netflix @EwingTang Seth Lloyd: Assume l² sampling access to rows, columns of A, entries of b, can we sample from
x=A⁺b==∑_l σ_l⁻¹ U^l (V^l ^T b)
Fact; FKV (2004) + @ewintang : can do this by sampling a constant # of samples from A, b, indep of m, n, dependent on rankk, κ=max(σ_l⁻¹) #LTQI
x=A⁺b==∑_l σ_l⁻¹ U^l (V^l ^T b)
Fact; FKV (2004) + @ewintang : can do this by sampling a constant # of samples from A, b, indep of m, n, dependent on rankk, κ=max(σ_l⁻¹) #LTQI
@MIT @QuSoftAmsterdam @ewintang @UW @netflix @EwingTang Seth Lloyd: l² sampling is key, can be seen as access to a database. The database preparation cost has a cost of at least nm (both for quantum and classical algorithm)
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#LTQI
@MIT @QuSoftAmsterdam @ewintang @UW @netflix @EwingTang Seth Lloyd: l² samples of r>k rows from A → r×n matrix R
Rⁱ=||A_F|| Aⁱ /(√r ||Aⁱ|| )
⟨R⁺R⟩=A⁺A
R’s SVD decomposition is an approximate SVD decomposition of the one of A
But this decomposition is still dependent of n.
#LTQI
Rⁱ=||A_F|| Aⁱ /(√r ||Aⁱ|| )
⟨R⁺R⟩=A⁺A
R’s SVD decomposition is an approximate SVD decomposition of the one of A
But this decomposition is still dependent of n.
#LTQI
@MIT @QuSoftAmsterdam @ewintang @UW @netflix @EwingTang Seth Loyd: So we now sample s columns of r, giving r×s matrix S , with approximately same singular values, and vectors. It allows to approximately sample from U^l, V^l.
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#LTQI
@MIT @QuSoftAmsterdam @ewintang @UW @netflix @EwingTang Seth Lloyd: Reminder: the goal is sampling from
x==∑_l σ_l⁻¹ U^l (V^l⁺ b).
@ewintang showed how to estimate V^l⁺ b from estimates of V^l, b
sampling from U^l and estiamting σ_l through the SVD procedure above the allows to sample from x
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x==∑_l σ_l⁻¹ U^l (V^l⁺ b).
@ewintang showed how to estimate V^l⁺ b from estimates of V^l, b
sampling from U^l and estiamting σ_l through the SVD procedure above the allows to sample from x
#LTQI
@MIT @QuSoftAmsterdam @ewintang @UW @netflix @EwingTang Seth Lloyd: the dependency is then polylog in n,m , but with prefactors with huge exponents in k and κ.
Õ(κ¹⁶ k⁶/ϵ⁶⋅log(mn))
The κ¹⁶ exponent seems pretty lose, but the k⁶ and ϵ⁶ seem pretty tight.
Quantum algorithms is
O(κk log(nm)log(1/ϵ)), which is much better #LTQI
Õ(κ¹⁶ k⁶/ϵ⁶⋅log(mn))
The κ¹⁶ exponent seems pretty lose, but the k⁶ and ϵ⁶ seem pretty tight.
Quantum algorithms is
O(κk log(nm)log(1/ϵ)), which is much better #LTQI
@MIT @QuSoftAmsterdam @ewintang @UW @netflix @EwingTang Seth Lloyd: actually, the nasty prefactors means that currently, even usual classical algorithms are often better than this. #LTQI
@MIT @QuSoftAmsterdam @ewintang @UW @netflix @EwingTang Seth Lloyd: These quantum inspired classical algorithms makes quantum information much richer, suggesting new ways to solve problems, even if we do not have access to a quantum computer.
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#LTQI
@MIT @QuSoftAmsterdam @ewintang @UW @netflix @EwingTang Seth Lloyd: A remark from Jonas (from @IRIF_Paris ) to which everyone agreed. Given the improvement in the k and κ exponents, if Kerenidis and Prakash’s work had come after @ewintang’s it would have been seen as a good quantum speedup, much better than Grover’s
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#LTQI
