Phase Estimation of Local Hamiltonians on NISQ Hardware

• URL: http://arxiv.org/abs/2110.13584v1
• Date: Tue, 26 Oct 2021 11:33:54 GMT
• Title: Phase Estimation of Local Hamiltonians on NISQ Hardware
• Authors: Laura Clinton, Johannes Bausch, Joel Klassen, Toby Cubitt
• Abstract summary: We investigate a binned version of Quantum Phase Estimation (QPE) set out by [Somma 2019] and known as the Quantum Eigenvalue Estimation Problem (QEEP) Within this framework, we find that our techniques reduce the threshold at which it becomes possible to perform the minimum two-bin instance of this algorithm by an order of magnitude. We propose an application, which we call Randomized Quantum Eigenvalue Estimation Problem (rQeep)
• Score: 6.0409040218619685
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work we investigate a binned version of Quantum Phase Estimation (QPE) set out by [Somma 2019] and known as the Quantum Eigenvalue Estimation Problem (QEEP). Specifically, we determine whether the circuit decomposition techniques we set out in previous work, [Clinton et al 2020], can improve the performance of QEEP in the NISQ regime. To this end we adopt a physically motivated abstraction of NISQ device capabilities as in [Clinton et al 2020]. Within this framework, we find that our techniques reduce the threshold at which it becomes possible to perform the minimum two-bin instance of this algorithm by an order of magnitude. This is for the specific example of a two dimensional spin Fermi-Hubbard model. For example, given a $10\%$ acceptable error on a $3\times 3$ spin Fermi-Hubbard model, with a depolarizing noise rate of $10^{-6}$, we find that the phase estimation protocol of [Somma 2019] could be performed with a bin width of approximately $1/9$ the total spectral range at the circuit depth where traditional gate synthesis methods would yield a bin width that covers the entire spectral range. We explore possible modifications to this protocol and propose an application, which we call Randomized Quantum Eigenvalue Estimation Problem (rQeep). rQeep outputs estimates on the fraction of eigenvalues which lie within randomly chosen bins and upper bounds the total deviation of these estimates from the true values. One use case we envision for this algorithm is resolving density of states features of local Hamiltonians, such as spectral gaps.

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