ECCC-Report TR18-128https://eccc.weizmann.ac.il/report/2018/128Comments and Revisions published for TR18-128en-usThu, 09 May 2019 08:04:28 +0300
Revision 3
| A quantum-inspired classical algorithm for recommendation systems |
Ewin Tang
https://eccc.weizmann.ac.il/report/2018/128#revision3A recommendation system suggests products to users based on data about user preferences. It is typically modeled by a problem of completing an $m \times n$ matrix of small rank $k$. We give the first classical algorithm to produce a recommendation in $O(\text{poly}(k)\text{polylog}(m,n))$ time, which is an exponential improvement on previous algorithms that run in time linear in $m$ and $n$. Our strategy is inspired by a quantum algorithm by Kerenidis and Prakash: like the quantum algorithm, instead of reconstructing a user's full list of preferences, we only seek a randomized sample from the user's preferences. Our main result is an algorithm that samples high-weight entries from a low-rank approximation of the input matrix in time independent of $m$ and $n$, given natural sampling assumptions on that input matrix. As a consequence, we show that Kerenidis and Prakash's quantum machine learning (QML) algorithm, one of the strongest candidates for provably exponential speedups in QML, does not in fact give an exponential speedup over classical algorithms.Thu, 09 May 2019 08:04:28 +0300https://eccc.weizmann.ac.il/report/2018/128#revision3
Revision 2
| A quantum-inspired classical algorithm for recommendation systems |
Ewin Tang
https://eccc.weizmann.ac.il/report/2018/128#revision2We give a classical analogue to Kerenidis and Prakash's quantum recommendation system, previously believed to be one of the strongest candidates for provably exponential speedups in quantum machine learning. Our main result is an algorithm that, given an $m \times n$ matrix in a data structure supporting certain $\ell^2$-norm sampling operations, outputs an $\ell^2$-norm sample from a rank-$k$ approximation of that matrix in time $O(\text{poly}(k)\log(mn))$, only polynomially slower than the quantum algorithm. As a consequence, Kerenidis and Prakash's algorithm does not in fact give an exponential speedup over classical algorithms. Further, under strong input assumptions, the classical recommendation system resulting from our algorithm produces recommendations exponentially faster than previous classical systems, which run in time linear in $m$ and $n$.
The main insight of this work is the use of simple routines to manipulate $\ell^2$-norm sampling distributions, which play the role of quantum superpositions in the classical setting. This correspondence indicates a potentially fruitful framework for formally comparing quantum machine learning algorithms to classical machine learning algorithms.Mon, 22 Apr 2019 06:01:42 +0300https://eccc.weizmann.ac.il/report/2018/128#revision2
Revision 1
| A quantum-inspired classical algorithm for recommendation systems |
Ewin Tang
https://eccc.weizmann.ac.il/report/2018/128#revision1We give a classical analogue to Kerenidis and Prakash's quantum recommendation system, previously believed to be one of the strongest candidates for provably exponential speedups in quantum machine learning. Our main result is an algorithm that, given an $m \times n$ matrix in a data structure supporting certain $\ell^2$-norm sampling operations, outputs an $\ell^2$-norm sample from a rank-$k$ approximation of that matrix in time $O(\operatorname{poly}(k)\log(mn))$, only polynomially slower than the quantum algorithm. As a consequence, Kerenidis and Prakash's algorithm does not in fact give an exponential speedup over classical algorithms. Further, under strong input assumptions, the classical recommendation system resulting from our algorithm produces recommendations exponentially faster than previous classical systems, which run in time linear in $m$ and $n$.
The main insight of this work is the use of simple routines to manipulate $\ell^2$-norm sampling distributions, which play the role of quantum superpositions in the classical setting. This correspondence indicates a potentially fruitful framework for formally comparing quantum machine learning algorithms to classical machine learning algorithms.Sat, 13 Apr 2019 12:02:18 +0300https://eccc.weizmann.ac.il/report/2018/128#revision1
Paper TR18-128
| A quantum-inspired classical algorithm for recommendation systems |
Ewin Tang
https://eccc.weizmann.ac.il/report/2018/128A recommendation system suggests products to users based on data about user preferences. It is typically modeled by a problem of completing an $m\times n$ matrix of small rank $k$. We give the first classical algorithm to produce a recommendation in $O(\text{poly}(k)\text{polylog}(m,n))$ time, which is an exponential improvement on previous algorithms that run in time linear in $m$ and $n$. Our strategy is inspired by a quantum algorithm by Kerenidis and Prakash: like the quantum algorithm, instead of reconstructing a user's full list of preferences, we only seek a randomized sample from the user's preferences. Our main result is an algorithm that samples high-weight entries from a low-rank approximation of the input matrix in time independent of $m$ and $n$, given natural sampling assumptions on that input matrix. As a consequence, we show that Kerenidis and Prakash's quantum machine learning (QML) algorithm, one of the strongest candidates for provably exponential speedups in QML, does not in fact give an exponential speedup over classical algorithms.Wed, 11 Jul 2018 11:38:28 +0300https://eccc.weizmann.ac.il/report/2018/128