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Gaussian Processes (GPs) are powerful kernelized methods for non-parameteric regression used in many applications. However, their use is limited to a few thousand of training samples due to their cubic time complexity. In order to scale GPs…

Machine Learning · Statistics 2021-12-20 Manuel Schürch , Dario Azzimonti , Alessio Benavoli , Marco Zaffalon

We present an efficient general method for realizing a quantum walk operator corresponding to an arbitrary sparse classical random walk. Our approach is based on Grover and Rudolph's method for preparing coherent versions of efficiently…

Quantum Physics · Physics 2013-06-12 Chen-Fu Chiang , Daniel Nagaj , Pawel Wocjan

Single-spin quantum sensors, for example based on nitrogen-vacancy centres in diamond, provide nanoscale mapping of magnetic fields. In applications where the magnetic field may be changing rapidly, total sensing time is crucial and must be…

Quantum Physics · Physics 2021-05-26 K. Craigie , E. M. Gauger , Y. Altmann , C. Bonato

As a signal recovery algorithm, compressed sensing is particularly useful when the data has low-complexity and samples are rare, which matches perfectly with the task of quantum phase estimation (QPE). In this work we present a new…

Quantum Physics · Physics 2025-01-01 Changhao Yi , Cunlu Zhou , Jun Takahashi

This paper proposes novel noise-free Bayesian optimization strategies that rely on a random exploration step to enhance the accuracy of Gaussian process surrogate models. The new algorithms retain the ease of implementation of the classical…

Machine Learning · Computer Science 2024-07-18 Hwanwoo Kim , Daniel Sanz-Alonso

Bayesian online learning provides a coherent framework for sequential inference. However, its theoretical understanding remains limited, particularly in the one-pass setting. Existing theoretical guarantees typically require the mini-batch…

Statistics Theory · Mathematics 2026-05-01 Jeyong Lee , Junhyeok Choi , Dongguen Kim , Minwoo Chae

We propose a novel Bayesian methodology which uses random walks for rapid inference of statistical properties of undirected networks with weighted or unweighted edges. Our formalism yields high-accuracy estimates of the probability…

Physics and Society · Physics 2018-07-25 Willow B. Kion-Crosby , Alexandre V. Morozov

Inference and decision making under uncertainty are key processes in every autonomous system and numerous robotic problems. In recent years, the similarities between inference and decision making triggered much work, from developing unified…

Artificial Intelligence · Computer Science 2021-01-06 Elad I. Farhi , Vadim Indelman

Bayesian filtering is a general framework for recursively estimating the state of a dynamical system. Classical solutions such that Kalman filter and Particle filter are introduced in this report. Gaussian processes have been introduced as…

Information Theory · Computer Science 2010-11-04 Mr. Chong Han , Dr. Ido Nevat , Dr. Gareth Peters , Prof. Jinhong Yuan

Quantum phase estimation is a paradigmatic problem in quantum sensing andmetrology. Here we show that adaptive methods based on classical machinelearning algorithms can be used to enhance the precision of quantum phase estimation when noisy…

Quantum Physics · Physics 2021-09-01 Nelson Filipe Costa , Yasser Omar , Aidar Sultanov , Gheorghe Sorin Paraoanu

Bayesian analysis is a framework for parameter estimation that applies even in uncertainty regimes where the commonly used local (frequentist) analysis based on the Cram\'er-Rao bound is not well defined. In particular, it applies when no…

Quantum Physics · Physics 2021-03-17 Simon Morelli , Ayaka Usui , Elizabeth Agudelo , Nicolai Friis

Bayesian learning using Gaussian processes provides a foundational framework for making decisions in a manner that balances what is known with what could be learned by gathering data. In this dissertation, we develop techniques for…

Machine Learning · Statistics 2022-04-29 Alexander Terenin

We consider a two-mode bosonic state with fixed photon number $n \in \mathbb{N}$, whose upper and lower modes pick up a phase $\phi$ and $-\phi$ respectively. We compute the optimal Fock coefficients of the input state, such that the mean…

Quantum Physics · Physics 2024-10-30 Boyu Zhou , Saikat Guha , Christos N. Gagatsos

The measurement of physical parameters is one of the main pillars of science. A classic example is the measurement of the optical phase enabled by optical interferometry where the best sensitivity achievable with N photons scales as 1/N -…

Conditional particle filters (CPFs) are powerful smoothing algorithms for general nonlinear/non-Gaussian hidden Markov models. However, CPFs can be inefficient or difficult to apply with diffuse initial distributions, which are common in…

Computation · Statistics 2020-11-23 Santeri Karppinen , Matti Vihola

To accurately reproduce measurements from the real world, simulators need to have an adequate model of the physical system and require the parameters of the model be identified. We address the latter problem of estimating parameters through…

Robotics · Computer Science 2022-03-01 Eric Heiden , Christopher E. Denniston , David Millard , Fabio Ramos , Gaurav S. Sukhatme

Quantum phase estimation (QPE) is a central algorithmic primitive that estimates eigenvalues of a Hamiltonian up to precision $\epsilon$ in Heisenberg-limited time $T=\Theta(1/\epsilon)$. Standard gate-based implementations of QPE require…

Quantum Physics · Physics 2026-05-22 Alexander Schmidhuber , Seth Lloyd

This paper researches how the systematic errors in phase inversions affect the success rate and the number of iterations in optimized quantum random-walk search algorithm. Through geometric description of this algorithm, the model of the…

Quantum Physics · Physics 2015-01-12 Yu-Chao Zhang , Wan-Su Bao , Xiang Wang , Xiang-Qun Fu

Our article deals with Bayesian inference for a general state space model with the simulated likelihood computed by the particle filter. We show empirically that the partially or fully adapted particle filters can be much more efficient…

Methodology · Statistics 2010-06-11 Michael Pitt , Ralph Silva , Paolo Giordani , Robert Kohn

We propose a new sampling-based approach for approximate inference in filtering problems. Instead of approximating conditional distributions with a finite set of states, as done in particle filters, our approach approximates the…

Machine Learning · Computer Science 2020-03-03 Xuan Su , Wee Sun Lee , Zhen Zhang
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