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Quantum Phase Estimation (QPE) routines are known to fail probabilistically even with perfect gates and input states. This effect stems from an incompatibility of finite-sized quantum registers to capture a phase within QPE with phase…

Quantum Physics · Physics 2025-08-12 Harriet Apel , Cristian L. Cortes , Jessica Lemieux , Mark Steudtner

Filtering is an important technique in quantum computing used for isolating or enhancing some specific states of quantum many-body systems. In this paper, we analyze the performance of filters based on the quantum phase estimation (QPE)…

Efficient state preparation is essential for implementing efficient quantum algorithms. Whilst several techniques for low-cost state preparation exist, this work facilitates further classes of states, whose amplitudes are well approximated…

Quantum Physics · Physics 2025-07-09 Oliver O'Brien , Christoph Sünderhauf

The problem of Phase Estimation (or Amplitude Estimation) admits a quadratic quantum speedup. Wang, Higgott and Brierley [2019, Phys. Rev. Lett. 122 140504] have shown that there is a continuous trade-off between quantum speedup and circuit…

Quantum Physics · Physics 2023-05-30 Duarte Magano , Miguel Murça

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

Quantum phase estimation (QPE) is one of the core algorithms for quantum computing. It has been extensively studied and applied in a variety of quantum applications such as the Shor's factoring algorithm, quantum sampling algorithms and the…

Quantum Phase Estimation (QPE) is a cornerstone algorithm in quantum computing, with applications ranging from integer factorization to quantum chemistry simulations. However, the resource demands of standard QPE, which require a large…

Quantum Physics · Physics 2026-03-24 Alok Shukla , Prakash Vedula

Quantum computing can provide speedups in solving many problems as the evolution of a quantum system is described by a unitary operator in an exponentially large Hilbert space. Such unitary operators change the phase of their eigenstates…

Quantum Physics · Physics 2024-01-23 Youle Wang , Lei Zhang , Zhan Yu , Xin Wang

We provide a modification to the quantum phase estimation algorithm (QPEA) inspired on classical windowing methods for spectral density estimation. From this modification we obtain an upper bound in the cost that implies a cubic improvement…

Quantum Physics · Physics 2022-08-31 Gumaro Rendon , Taku Izubuchi , Yuta Kikuchi

Quantum Phase Estimation (QPE) is a cornerstone algorithm for fault-tolerant quantum computation, especially for electronic structure calculations of chemical systems. To accommodate the diverse characteristics of quantum chemical systems,…

Quantum Physics · Physics 2025-10-03 Calvin Ku , Yu-Cheng Chen , Alice Hu , Min-Hsiu Hsieh

Due to its significance as a subroutine, in this work, we consider the coherent version of the quantum phase estimation problem, where given an arbitrary input state and black-box access to unitaries $U$ and controlled-$U$, the goal is to…

Quantum Physics · Physics 2026-04-20 Dhrumil Patel , Shi Jie Samuel Tan , Yigit Subasi , Andrew T. Sornborger

Quantum phase estimation (QPE) is one of the most important subroutines in quantum computing. In general applications, current QPE algorithms either suffer an exponential time overload or require a set of - notoriously quite fragile - GHZ…

Quantum Physics · Physics 2021-10-04 Luca Pezzè , Augusto Smerzi

The quantum phase estimation (QPE) is one of the fundamental algorithms based on the quantum Fourier transform. It has applications in order-finding, factoring, and finding the eigenvalues of unitary operators. The major challenge in…

Quantum Physics · Physics 2023-12-05 Muhammad Faizan , Muhammad Faryad

The VQE algorithm has turned out to be quite expensive to run given the way we currently access quantum processors (i.e. over the cloud). In order to alleviate this issue, we introduce Quantum Sampling Regression (QSR), an alternative…

Quantum Physics · Physics 2020-12-07 Pedro Rivero , Ian C. Cloët , Zack Sullivan

Two-stage stochastic programming is a problem formulation for decision-making under uncertainty. In the first stage, the actor makes a best "here and now" decision in the presence of uncertain quantities that will be resolved in the future,…

Quantum singular value transformation (QSVT) enables the application of polynomial functions to the singular values of near arbitrary linear operators embedded in unitary transforms, and has been used to unify, simplify, and improve most…

Quantum Physics · Physics 2023-04-28 Zane M. Rossi , Victor M. Bastidas , William J. Munro , Isaac L. Chuang

This paper is concerned with the phase estimation algorithm in quantum computing algorithms, especially the scenarios where (1) the input vector is not an eigenvector; (2) the unitary operator is not exactly implemented; (3) random…

Quantum Physics · Physics 2022-08-10 Xiantao Li

Many researchers have been heavily investigated on quantum phase estimation (QPE) algorithms to find the unknown phase, since QPE is the core building block of the most quantum algorithms such as the Shor's factoring algorithm, quantum…

Quantum Physics · Physics 2019-03-19 Hamed Mohammadbagherpoor , Young-Hyun Oh , Anand Singh , Xianqing Yu , Andy J. Rindos

The analysis of credit risk is crucial for the efficient operation of financial institutions. Quantum Amplitude Estimation (QAE) offers the potential for a quadratic speed-up over classical methods used to estimate metrics such as Value at…

Quantum algorithms offer significant speedups over their classical counterparts for a variety of problems. The strongest arguments for this advantage are borne by algorithms for quantum search, quantum phase estimation, and Hamiltonian…

Quantum Physics · Physics 2021-12-14 John M. Martyn , Zane M. Rossi , Andrew K. Tan , Isaac L. Chuang
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