An Iterative Method to Improve the Precision of Quantum Phase Estimation Algorithm
arXiv:2402.14191 · doi:10.1103/PhysRevA.109.032606
Abstract
Here we revisit the quantum phase estimation (QPE) algorithm, and devise an iterative method to improve the precision of QPE with propagators over a variety of time spans. For a given propagator and a certain eigenstate as input, QPE with propagator is introduced to estimate the phase corresponding to an eigenenergy. Due to the periodicity of the complex exponential, we can pinpoint the eigenenergy in a branch of comb-like ranges by applying QPE with propagators over longer time spans. Thus, by picking up appropriate time spans, the iterative QPE with corresponding propagators can enable us to pinpoint the eigenenergy more precisely. Moreover, even if there are only few qubits as ancilla qubits, high precision is still available by the proposed iterative method. Our work provides a feasible and promising means toward precise estimations of eigenvalue on noisy intermediate-scale quantum (NISQ) devices.
arXiv comment7 pages, 4 figures
References in corpus (21)
- Quantum Computing in the NISQ era and beyond
- Supplementary information for "Quantum supremacy using a programmable superconducting processor"
- Quantum Machine Learning
- Quantum algorithm for solving linear systems of equations
- Quantum computational advantage using photons
- Quantum Convolutional Neural Networks
- Simulated Quantum Computation of Molecular Energies
- Quantum Algorithms Revisited
- Characterizing Quantum Supremacy in Near-Term Devices
- Scalable Quantum Simulation of Molecular Energies
- Towards Quantum Chemistry on a Quantum Computer
- Quantum algorithms for quantum chemistry and quantum materials science
- Determining eigenstates and thermal states on a quantum computer using quantum imaginary time evolution
- Quantum Simulators: Architectures and Opportunities
- Simulation of Electronic Structure Hamiltonians Using Quantum Computers
- Quantum advantage with shallow circuits
- Simulating Physical Phenomena by Quantum Networks
- Quantum critical dynamics in a 5000-qubit programmable spin glass
- Quantum Machine Learning for Chemistry and Physics
- Optimizing quantum phase estimation for the simulation of Hamiltonian eigenstates
- Towards Perturbation Theory Methods on a Quantum Computer