Ilya Sinayskiy

Ilya Sinayskiy

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Research Focus & Metrics

Academic Career Length

19Years (2008-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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15 October 2014 in Contemporary Physics
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Journal Article
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MSMaria Schuld
Maria Schuld
ISIlya Sinayskiy
Ilya Sinayskiy
FPFrancesco Petruccione
Francesco Petruccione
Machine learning algorithms learn a desired input-output relation from examples in order to interpret new inputs. This is important for tasks such as image and speech recognition or strategy optimisation, with growing applications in the IT industry. In the last couple of years, researchers investigated if quantum computing can help to improve classical machine learning algorithms. Ideas range from running computationally costly algorithms or their subroutines efficiently on a quantum computer to the translation of stochastic methods into the language of quantum theory. This contribution gives a systematic overview of the emerging field of quantum machine learning. It presents the approaches as well as technical details in an accessible way, and discusses the potential of a future theory of quantum learning.

26 August 2014 in Quantum Information Processing
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Journal Article
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MSMaria Schuld
Maria Schuld
ISIlya Sinayskiy
Ilya Sinayskiy
FPFrancesco Petruccione
Francesco Petruccione

30 August 2016 in Physical Review A
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Journal Article
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MSMaria Schuld
Maria Schuld
ISIlya Sinayskiy
Ilya Sinayskiy
FPFrancesco Petruccione
Francesco Petruccione
We give an algorithm for prediction on a quantum computer which is based on a linear regression model with least-squares optimization. In contrast to related previous contributions suffering from the problem of reading out the optimal parameters of the fit, our scheme focuses on the machine-learning task of guessing the output corresponding to a new input given examples of data points. Furthermore, we adapt the algorithm to process nonsparse data matrices that can be represented by low-rank approximations, and significantly improve the dependency on its condition number. The prediction result can be accessed through a single-qubit measurement or used for further quantum information processing routines. The algorithm's runtime is logarithmic in the dimension of the input space provided the data is given as quantum information as an input to the routine.

14 November 2018 in Journal of The Royal Society Interface
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Journal Article
Open access
AMAdriana Marais
Adriana Marais
BABetony Adams
Betony Adams
ARAndrew K. Ringsmuth
Andrew K. Ringsmuth
MFMarco Ferretti
Marco Ferretti
JGJ. Michael Gruber
J. Michael Gruber
RGRienk van Grondelle
Rienk van Grondelle
Biological systems are dynamical, constantly exchanging energy and matter with the environment in order to maintain the non-equilibrium state synonymous with living. Developments in observational techniques have allowed us to study biological dynamics on increasingly small scales. Such studies have revealed evidence of quantum mechanical effects, which cannot be accounted for by classical physics, in a range of biological processes. Quantum biology is the study of such processes, and here we provide an outline of the current state of the field, as well as insights into future directions.

31 May 2012 in Journal of Statistical Physics
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Journal Article
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SAS. Attal
S. Attal
FPFrancesco Petruccione
Francesco Petruccione
CSC. Sabot
C. Sabot
ISIlya Sinayskiy
Ilya Sinayskiy

1 March 2015 in Physics Letters A
Verified
Journal Article
Closed access
MSMaria Schuld
Maria Schuld
ISIlya Sinayskiy
Ilya Sinayskiy
FPFrancesco Petruccione
Francesco Petruccione