Quantumness of quantum phase estimation algorithms

Osama M. Raisuddin, Suvranu De

Vamsi Parasa, Marek A. Perkowski

This papers presents the generalization of the quantum phase estimation (QPE) algorithm to multivalued logic. QPE is a very important subroutine used in many quantum algorithms of practical importance. The QPE using qudits has certain advantages when compared to the version using qubits. The QPE using qudits is more accurate and requires lesser number of qudits (as the value of the radix d increases) to estimate the phase up to a given accuracy with a given success probability. Since the existing quantum computer implementations use only few qubits, it is important to have alternative versions of the QPE which use as few qubits as possible. In this regard, the iterative quantum phase estimation algorithm (IQPE) which uses only single qubit to store the phase information has been developed. The number of iterations involved in the IQPE is directly proportional to the required accuracy in the estimated phase. In this regard, IQPE using qudits requires (logarithmically) lesser number of iterations (and hence is faster) compared to the one using qubits. In this paper, we also develop the multivalued logic version of the IQPE and describe its implementation.

Jincheng Zhang

Quantum phase estimation (QPE) is a fundamental technique for determining the phase of a quantum state, crucial for various quantum algorithms and quantum measurements. This paper introduces a novel adaptive optimization algorithm, dubbed the "Adaptive Quantum Phase Estimation" (AQPE) algorithm, designed to enhance the efficiency of QPE by dynamically adjusting parameters during the state estimation process. We demonstrate the effectiveness of AQPE through rigorous theoretical analysis and extensive numerical simulations, achieving significantly improved convergence rates compared to traditional optimization techniques. The algorithm leverages a feedback mechanism based on the observed phase error during the estimation, allowing it to iteratively refine the parameter set and ultimately locate the optimal phase estimation. We present preliminary results demonstrating the algorithm's capability to mitigate the challenges associated with conventional QPE, particularly in the presence of noisy data. The implications of this research extend to broader advancements in quantum algorithm design and computational power.

Hiu Yung Wong

Hiu Yung Wong

Valentin Gebhart, Augusto Smerzi, Luca Pezzè

Quantum phase estimation (QPE) is ubiquitous in quantum technologies. It 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 single phase, one can envisage applications where the simultaneous estimation of multiple phases may bring substantial advantages, for instance in presence of spatial or temporal constraints. In this work, we study a Bayesian QPE algorithm for the parallel (simultaneous) estimation of multiple arbitrary phases. The algorithm allows the estimation of each phase, as well as any arbitrary linear combination, with Heisenberg-limited sensitivity with respect to the total number of quantum resources. The parallel estimation protocol gives access to correlations in the Bayesian multiphase distribution that allow to surpass the sensitivity of sequential single phase estimation strategies for optimal combinations of phases. Furthermore, the algorithm proves a certain noise resilience, and can be implemented using single photons and standard optical elements in currently accessible experiments.