Channel-based framework for phase esimation of multiple eigenvalues
Yuan-De Jin, Shi-Yu Zhang, Wen-Long Ma

TL;DR
This paper introduces a quantum phase estimation framework that efficiently estimates multiple eigenvalues without requiring initial eigenstate preparation by leveraging measurement backaction and sequential quantum channels.
Contribution
It develops a theoretical framework for multi-eigenvalue QPE using sequential quantum channels and active measurement backaction, applicable to arbitrary initial states.
Findings
Repetitive and adaptive schemes achieve quantum limits in eigenvalue estimation.
Sequential measurements steer the system toward eigenstates, enabling eigenvalue extraction.
Simulation shows robustness and performance of the proposed methods.
Abstract
Quantum phase estimation (QPE) of the eigenvalues of a unitary operator on a target quantum system is a crucial subroutine in various quantum algorithms. Conventional QPE is often expensive to implement as it requires a large number of ancilla qubits and the ability to perform quantum Fourier transform. Recent developments in iterative QPE reduce the implementation cost by repetitive uses of a single ancilla and classical post-processing. However, both conventional and iterative schemes often require preparation of the target system in an eigenstate of the unitary operator, while it remains ambiguous to achieve QPE of multiple eigenvalues with no need of initial state preparation. Here we clarify this issue by developing a theoretical framework based on sequential quantum channels for iterative QPE. We find that QPE of multiple eigenvalues can be efficiently realized for arbitrary…
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Taxonomy
TopicsQuantum Mechanics and Applications · Quantum Information and Cryptography · Advanced Thermodynamics and Statistical Mechanics
