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Related papers: Efficient Bayesian Phase Estimation

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Bayesian estimation approaches, which are capable of combining the information of experimental data from different likelihood functions to achieve high precisions, have been widely used in phase estimation via introducing a controllable…

Quantum Physics · Physics 2021-07-02 Yuxiang Qiu , Min Zhuang , Jiahao Huang , Chaohong Lee

Realisation of experiments even on small and medium-scale quantum computers requires an optimisation of several parameters to achieve high-fidelity operations. As the size of the quantum register increases, the characterisation of quantum…

Quantum Physics · Physics 2020-08-11 F. Martínez-García , D. Vodola , M. Müller

Phase estimation is known to be a robust method for single-qubit gate calibration in quantum computers, while Bayesian estimation is widely used in devising optimal methods for learning in quantum systems. We present Bayesian phase…

Quantum Physics · Physics 2025-05-06 Brennan de Neeve , Andrey V. Lebedev , Vlad Negnevitsky , Jonathan P. Home

Quantum phase estimation is a fundamental subroutine in many quantum algorithms, including Shor's factorization algorithm and quantum simulation. However, so far results have cast doubt on its practicability for near-term, non-fault…

Quantum-phase-estimation algorithms are critical subroutines in many applications for quantum computers and in quantum-metrology protocols. These algorithms estimate the unknown strength of a unitary evolution. By using coherence or…

Quantum Physics · Physics 2023-03-06 Joseph G. Smith , Crispin H. W. Barnes , David R. M. Arvidsson-Shukur

Quantum phase estimation is one of the most important tools in quantum algorithms. It can be made non-adaptive (meaning all applications of the unitary $U_\phi$ happen simultaneously) without using more applications of $U_\phi$, albeit at…

Quantum Physics · Physics 2025-11-10 Noah Linden , Ronald de Wolf

Phase estimation is a quantum algorithm for measuring the eigenvalues of a Hamiltonian. We propose and rigorously analyse a randomized phase estimation algorithm with two distinctive features. First, our algorithm has complexity independent…

Quantum Physics · Physics 2022-08-09 Kianna Wan , Mario Berta , Earl T. Campbell

We consider performing phase estimation under the following conditions: we are given only one copy of the input state, the input state does not have to be an eigenstate of the unitary, and the state must not be measured. Most quantum…

Quantum Physics · Physics 2022-12-13 Patrick Rall

We consider the task of estimating the randomly fluctuating phase of a continuous-wave beam of light. Using the theory of quantum parameter estimation, we show that this can be done more accurately when feedback is used (adaptive phase…

Quantum Physics · Physics 2009-11-10 D. T. Pope , H. M. Wiseman , N. K. Langford

Phase estimation protocols provide a fundamental benchmark for the field of quantum metrology. The latter represents one of the most relevant applications of quantum theory, potentially enabling the capability of measuring unknown physical…

Quantum phase estimation (QPE) is the key subroutine of several quantum computing algorithms as well as a central ingredient in quantum computational chemistry and quantum simulation. While QPE strategies have focused on the estimation of a…

Quantum Physics · Physics 2021-07-26 Valentin Gebhart , Augusto Smerzi , Luca Pezzè

Iterative phase estimation has long been used in quantum computing to estimate Hamiltonian eigenvalues. This is done by applying many repetitions of the same fundamental simulation circuit to an initial state, and using statistical…

Quantum Physics · Physics 2019-07-25 Ian D. Kivlichan , Christopher E. Granade , Nathan Wiebe

In recent years there has been substantial development in algorithms for quantum phase estimation. In this work we provide a new approach to online Bayesian phase estimation that achieves Heisenberg limited scaling that requires…

Quantum Physics · Physics 2022-08-10 Cassandra Granade , Nathan Wiebe

We introduce a novel Bayesian phase estimation technique based on adaptive grid refinement method. This method automatically chooses the number particles needed for accurate phase estimation using grid refinement and cell merging strategies…

Quantum Physics · Physics 2020-09-18 Ramakrishna Tipireddy , Nathan Wiebe

Optical phase estimation is a vital measurement primitive that is used to perform accurate measurements of various physical quantities like length, velocity and displacements. The precision of such measurements can be largely enhanced by…

It is well-known that adaptive homodyne estimation of continuously varying optical phase provides superior accuracy in the phase estimate as compared to adaptive or non-adaptive static estimation. However, most phase estimation schemes rely…

Optimization and Control · Mathematics 2015-09-14 Shibdas Roy , Ian R. Petersen , Elanor H. Huntington

Objective. We identify two linked problems related to estimating the phase of the alpha rhythm when the signal after a specific event is unknown (real-time case), or corrupted (offline analysis). We propose methods to estimate the phase…

Quantitative Methods · Quantitative Biology 2020-04-07 J. R. McIntosh , P. Sajda

The phase estimation algorithm is a powerful quantum algorithm with applications in cryptography, number theory, and simulation of quantum systems. We use this algorithm to simulate the time evolution of a system of two spin-1/2 particles…

Quantum Physics · Physics 2021-05-12 Scott Johnstun , Jean-François Van Huele

We give an iterative algorithm for phase estimation of a parameter theta, which is within a logarithmic factor of the Heisenberg limit. Unlike other methods, we do not need any entanglement or an extra rotation gate which can perform…

Quantum Physics · Physics 2010-01-22 Caleb J O'Loan

We show that certain computational algorithms can be simulated on a quantum computer with exponential efficiency and be insensitive to phase errors. Our explicit algorithm simulates accurately the classical chaotic dynamics for…

Quantum Physics · Physics 2007-05-23 B. Georgeot , D. L. Shepelyansky
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