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Quantum gradient descent and Newton's method for constrained polynomial optimization
Patrick Rebentrost, Maria Schuld, Leonard Wossnig +2
Optimization problems in disciplines such as machine learning are commonly solved with iterative methods. Gradient descent algorithms find local minima by moving along the directio…
Robustness and fragility of Markovian dynamics in a qubit dephasing channel
Filip A. Wudarski, Francesco Petruccione
The Markovian dynamics of a qubit is investigated in the scheme of random unitary dynamics, where Kraus operators are changed by an extra noise. The behavior of Markovianity is exp…
Minimalistic analytical approach to non-Markovian open quantum systems
Vitalii Semin, Francesco Petruccione
The dynamics of finite dimension open quantum systems is studied with the help of the simplest possible form of projection operators, namely the ones which project only onto one di…
Quantum force estimation in arbitrary non-Markovian Gaussian bath
Camille Lombard Latune, Ilya Sinayskiy, Francesco Petruccione
The force estimation problem in quantum metrology with an arbitrary non-Markovian Gaussian bath is considered. No assumptions are made on the bath spectrum and coupling strength wi…
Digital quantum simulation of many-body non-Markovian dynamics
R. Sweke, M. Sanz, I. Sinayskiy +2
We present an algorithmic method for the digital quantum simulation of many-body locally indivisible non-Markovian open quantum systems. It consists of two parts: firstly, a Suzuki…
Prediction by linear regression on a quantum computer
Maria Schuld, Ilya Sinayskiy, Francesco Petruccione
We give an algorithm for prediction on a quantum computer which is based on a linear regression model with least squares optimisation. Opposed to related previous contributions suf…