Quantum Recommendation Systems....2017/01/17 · Quantum Recommendation Systems. Iordanis Kerenidis...

Quantum Recommendation Systems.

Iordanis Kerenidis 1 Anupam Prakash 2

1CNRS, Universite Paris Diderot, Paris, France.

2Nanyang Technological University, Singapore.

January 17, 2017

Iordanis Kerenidis , Anupam Prakash QIP-2017, Seattle

The HHL algorithm

Utilize intrinsic linear algebra capabilities of quantumcomputers for exponential speedups.

Vector state |x〉 =∑

i xi |i〉 where x ∈ Rn is a unit vector.

Given sparse matrix A ∈ Rn×n and |b〉 there is a quantumalgorithm to prepare |A−1b〉 in time polylog(n). [Harrow,Hassidim, Lloyd]

Assumptions: |b〉 can be prepared polylog(n) time and A ispolylog(n) sparse.

Incomparable to classical linear system solver which returnsvector x ∈ Rn as opposed to |x〉.

The HHL algorithm

Quantum Machine Learning

HHL led to several proposals for quantum machine learningalgorithms.

Principal components analysis, classification with `2-SVMs,k-means clustering, perceptron, nearest neighbors... [Lloyd,Mohseni, Rebentrost, Wiebe, Kapoor, Svore]

Algorithms achieve exponential speedups only forsparse/well-conditioned data.

Sometimes a variant of the classical problem is solved: `1 vs`2-SVM.

Incomparable with classical.

Quantum Recommendation Systems

Open problem: A quantum machine learning algorithm withexponential worst case speedup for classical problem.

Quantum recommendation systems.

An exponential speedup over classical with similarassumptions and guarantees.

An end to end application with no assumptions on the dataset.

Solves the ’same’ problem as a classical recommendationsystem.

The Recommendation Problem

The preference matrix P.

P1 P2 P3 P4 · · · · · · Pn−1 Pn

.8 .9 · · · .2

.2 .6 · · · .85

.75 .2

.1 .4 · · · .9

? ? ??

· · · · · · · · · · · · · · · · · · · · · · · ·

· · · · · ·

Pij is the value of item j for user i . Samples from P arrive inan online manner.

The assumption that P has a good rank-k approximation forsmall k is widely used.

P1 P2 P3 P4 · · · · · · Pn−1 Pn

.8 .9 · · · .2

.2 .6 · · · .85

.75 .2

.1 .4 · · · .9

? ? ??

· · · · · · · · · · · · · · · · · · · · · · · ·

· · · · · ·

P1 P2 P3 P4 · · · · · · Pn−1 Pn

.8 .9 · · · .2

.2 .6 · · · .85

.75 .2

.1 .4 · · · .9

? ? ??

· · · · · · · · · · · · · · · · · · · · · · · ·

· · · · · ·

The Netflix problem

Reconstruction vs sampling

Matrix reconstruction algorithms reconstruct P ≈ P using thelow rank assumption and require time poly(mn).

A reconstruction based recommendation system requires timepoly(n), even with pre-computation.

Matrix sampling suffices to obtain good recommendations.

Quantum algorithms can perform matrix sampling.

Theorem

There is a quantum recommendation algorithm with running timeO(poly(k)polylog(mn)).

Theorem

Computational Model

Samples from P arrive in an online manner and are stored indata structure with update time O(log2 mn).

The quantum algorithm has oracle access to binary tree datastructure storing additional metadata.

0.2 0.1

0.6 0.1

We use the standard memory model used for algorithms likeGrover search.

Users arrive into system in an online manner and systemprovides recommendations in time poly(k)polylog(mn).

Computational Model

0.2 0.1

0.6 0.1

Computational Model

0.2 0.1

0.6 0.1

Computational Model

0.2 0.1

0.6 0.1

Singular value estimation

The singular value decomposition for matrix A is written asA =

∑i σiuiv

The rank-k approximation Ak =∑

i∈[k] σiuivti minimizes

‖A− Ak‖F .

Quantum singular value estimation:

Theorem

There is an algorithm with running time O(polylog(mn)/ε) thattransforms

∑i αi |vi 〉 →

∑i αi |vi 〉 |σi 〉 where σi ∈ σi ± ε ‖A‖F

with probability at least 1− 1/poly(n).

∑i σiuiv

‖A− Ak‖F .

Theorem

∑i αi |vi 〉 →

∑i σiuiv

‖A− Ak‖F .

Theorem

∑i αi |vi 〉 →

∑i σiuiv

‖A− Ak‖F .

Theorem

∑i αi |vi 〉 →

Matrix Sampling

Let T be a 0/1 matrix such that Tij = 1 if item j is ’good’recommendation for user i .

P1 P2 P3 P4 · · · · · · Pn−1 Pn

1 1 · · · 0

0 0 · · · 1

? ? ??

· · · · · · · · · · · · · · · · · · · · · · · ·

· · · · · ·

Set the ?s to 0 and rescale to obtain a subsample matrix T .

Matrix Sampling

Let T be a 0/1 matrix such that Tij = 1 if item j is ’good’recommendation for user i .

P1 P2 P3 P4 · · · · · · Pn−1 Pn

1 1 · · · 0

0 0 · · · 1

? ? ??

· · · · · · · · · · · · · · · · · · · · · · · ·

· · · · · ·

Set the ?s to 0 and rescale to obtain a subsample matrix T .

Matrix Sampling

TPRounding

Low rankassumption

[Achlioptas, McSherry]extended

Uniform subsample

SVD,QSVE

Figure: Matrix sampling based recommendation system.

Matrix Sampling

T is the binary recommendation matrix obtained by roundingP.

T is a uniform subsample of T :

{Aij/p [with probability p]

0 [otherwise]

Tk and Tk are rank-k approximations for T and T .

The low rank assumption implies that ‖T − Tk‖ ≤ ε ‖T‖F forsmall k .

Analysis: Sampling from matrix ’close to’ Tk yields goodrecommendations.

Matrix Sampling

0 [otherwise]

Matrix Sampling

0 [otherwise]

Matrix Sampling

0 [otherwise]

Matrix Sampling

0 [otherwise]

Analysis

Samples from Tk are good recommendations, for largefraction of ’typical’ users.

Sampling from Tk suffices.

Theorem (AM02)

If A is obtained from a 0/1 matrix A by subsampling withprobability p = 16n/η ‖A‖2

F then with probability at least1− exp(−19(log n)4), for all k,

||A− Ak ||F ≤ ||A− Ak ||F + 3√ηk1/4||A||F

Analysis

Theorem (AM02)

F then with probability at least1− exp(−19(log n)4), for all k ,

||A− Ak ||F ≤ ||A− Ak ||F + 3√ηk1/4||A||F

Analysis

Theorem (AM02)

F then with probability at least1− exp(−19(log n)4), for all k ,

||A− Ak ||F ≤ ||A− Ak ||F + 3√ηk1/4||A||F

Analysis

The quantum algorithm samples from T≥σ,κ, a projection ontoall singular values ≥ σ and some in the range [(1− κ)σ, σ).

We extend AM02 to this setting showing that:

||T − Tσ,κ||F ≤ 9ε ‖T‖F

For most typical users, samples from (Tσ,κ)i are goodrecommendations with high probability.

Analysis

||T − Tσ,κ||F ≤ 9ε ‖T‖F

Analysis

||T − Tσ,κ||F ≤ 9ε ‖T‖F

Quantum Recommendation Algorithm

Prepare state |Ti 〉 corresponding to row for user i .

Apply quantum projection algorithm to |Ti 〉 to obtain|(T≥σ,κ)i 〉.Measure projected state in computational basis to getrecommendation.

The threshold σ =ε√p‖A‖F√

2kand κ = 1

Running time depends on the threshold and not the conditionnumber.

Apply quantum projection algorithm to |Ti 〉 to obtain|(T≥σ,κ)i 〉.

Measure projected state in computational basis to getrecommendation.

2kand κ = 1

The projection algorithm

Let A =∑

i σiuivti be the singular value decomposition, write

input |x〉 =∑

i αi |vi 〉.

Estimate singular values∑

i αi |vi 〉 |σi 〉 to additive error κσ/2.

Map to∑

i αi |vi 〉 |σi 〉 |t〉 where t = 1 if σi ≥ (1− κ/2)σ anderase σi .

Post-select on t = 1.

The output |A≥σ,κx〉 a projection the space of singular vectorswith singular values ≥ σ and some in the range [(1− κ)σ, σ).

Let A =∑

input |x〉 =∑

i αi |vi 〉.Estimate singular values

∑i αi |vi 〉 |σi 〉 to additive error κσ/2.

Map to∑

Let A =∑

input |x〉 =∑

Map to∑

Let A =∑

input |x〉 =∑

Map to∑

Let A =∑

input |x〉 =∑

Map to∑

Open Questions

Find a classical algorithm matrix sampling basedrecommendation algorithm that runs in timeO(poly(k)polylog(mn)).

Prove a lower bound to rule out such an algorithm.

Find more quantum machine learning algorithms.

Quantum Recommendation Systems....2017/01/17 · Quantum Recommendation Systems. Iordanis Kerenidis...

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