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arXiv:1305.2253 · Physical Review A

Experimental performance of a quantum simulator: Optimizing adiabatic evolution and identifying many-body ground states

Philip Richerme, Crystal Senko, J. Smith, A. Lee, S. É. Korenblit, Christopher R. Monroe

Published Jul 31, 2013• 71 citations
arXivHTMLPDF

Abstract

We use local adiabatic evolution to experimentally create and determine the ground-state spin ordering of a fully connected Ising model with up to 14 spins. Local adiabatic evolution---in which the system evolution rate is a function of the instantaneous energy gap---is found to maximize the ground-state probability compared with other adiabatic methods while requiring knowledge only of the lowest $\ensuremath{\sim}$$N$ of the ${2}^{N}$ Hamiltonian eigenvalues. We also demonstrate that the ground-state ordering can be experimentally identified as the most probable of all possible spin configurations, even when the evolution is highly nonadiabatic.

DOI
10.1103/physreva.88.012334

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We use local adiabatic evolution to experimentally create and determine the ground-state spin ordering of a fully connected Ising model with up to 14 spins. Local adiabatic evolution---in which the system evolution rate is a function of the instantaneous energy gap---is found to maximize the ground-state probability compared with other adiabatic methods while requiring knowledge only of the lowest \ensuremath{\sim}$$N of the 2N{2}^{N}2N Hamiltonian eigenvalues. We also demonstrate that the ground-state ordering can be experimentally identified as the most probable of all possible spin configurations, even when the evolution is highly nonadiabatic.
We show that adiabatic evolution of a low-dimensional lattice of quantum spins with a spectral gap can be simulated efficiently. In particular, we show that as long as the spectral gap $\ensuremath{\Delta}E$ between the ground state and the first excited state is any constant independent of nnn, the total number of spins, then the ground-state expectation values of local operators, such as correlation functions, can be computed using polynomial space and time resources. Our results also imply that the local ground-state properties of any two spin models in the same quantum phase can be efficiently obtained from each other. A consequence of these results is that adiabatic quantum algorithms can be simulated efficiently if the spectral gap does not scale with nnn. The simulation method we describe takes place in the Heisenberg picture and does not make use of the finitely correlated state--matrix product state formalism.
We propose a strategy for modeling the behavior of an adiabatic quantum computer described by an Ising Hamiltonian with NNN sites and the coordination number ZZZ. The method is based on the 1/Z1/Z1/Z expansion for the density matrix of the system. In each order, the ground state energy is found neglecting the higher-order correlations between the sites, as long as the set of equations remains non-singular. The conditions of the appearance of a singularity, equivalent to the disappearance of energy gap in the given approximation, can be directly obtained from the equations. Then the next order in the expansion must be used, at the price of an NNN-fold increase in computational resources. This "martingale" strategy allows reducing the computational costs to a power of NNN rather than 2N2^N2N, with a finite probability of success. The strategy is illustrated by the case of a two-spin system and extended to a large number of qubits. Comparing the predictions to the experimental results obtained by using an adiabatic quantum computer would help quantify the importance of multi-site correlations, and the influence of decoherence, on its operation.
One of the goals in quantum simulation is to adiabatically generate the ground state of a complicated Hamiltonian by starting with the ground state of a simple Hamiltonian and slowly evolving the system to the complicated one. If the evolution is adiabatic and the initial and final ground states are connected due to having the same symmetry, then the simulation will be successful. But in most experiments, adiabatic simulation is not possible because it would take too long, and the system has some level of diabatic excitation. In this work, we quantify the extent of the diabatic excitation even if we do not know {\it a priori} what the complicated ground state is. Since many quantum simulator platforms, like trapped ions, can measure the probabilities to be in a product state, we describe techniques that can employ these measurements to estimate the probability of being in the ground state of the system after the diabatic evolution. These techniques do not require one to know any properties about the Hamiltonian itself, nor to calculate its eigenstate properties. All the information is derived by analyzing the product-state measurements as functions of time.
Abstract. Adiabatic quantum computation employs a slow change of a time-dependent control function (or functions) to interpolate between an initial and final Hamiltonian, which helps to keep the system in the instantaneous ground state. When the evolution time is finite, the degree of adiabaticity (quantified in this work as the average ground-state population during evolution) depends on the particulars of a dynamic trajectory associated with a given set of control functions. We use quantum optimal control theory with a composite objective functional to numerically search for controls that achieve the target final state with a high fidelity while simultaneously maximizing the degree of adiabaticity. Exploring properties of optimal adiabatic trajectories in model systems elucidates the dynamic mechanisms that suppress unwanted excitations from the ground state. Specifically, we discover that the use of multiple control functions makes it possible to access a rich set of dynamic trajectories, some of which attain a significantly improved performance (in terms of both fidelity and adiabaticity) through the increase of the energy gap during most of the evolution time. Submitted to: New J. Phys. † Author to whom any correspondence should be addressed. ar
.One of the goals in quantum simulation is to adiabatically generate the ground state of a complicated Hamiltonian by starting with the ground state of a simple Hamiltonian and slowly evolving the system to the complicated one. If the evolution is adiabatic and the initial and final ground states are connected due to having the same symmetry, then the simulation will be successful. But in most experiments, adiabatic simulation is not possible because it would take too long, and the system has some level of diabatic excitation. In this work, we quantify the extent of the diabatic excitation even if we do not know {\it a priori} what the complicated ground state is. Since many quantum simulator platforms, like trapped ions, can measure the probabilities to be in a product state, we describe techniques that can employ these simple measurements to estimate the probability of being in the ground state of the system after the diabatic evolution. These techniques do not require one to know any properties about the Hamiltonian itself, nor to calculate its eigenstate properties. All the information is derived by analyzing the product-state measurements as functions of time.
Adiabatic quantum computation employs a slow change of a time-dependent control function (or functions) to interpolate between an initial and final Hamiltonian, which helps to keep the system in the instantaneous ground state. When the evolution time is finite, the degree of adiabaticity (quantified in this work as the average ground-state population during evolution) depends on the particulars of a dynamic trajectory associated with a given set of control functions. We use quantum optimal control theory with a composite objective functional to numerically search for controls that achieve the target final state with a high fidelity while simultaneously maximizing the degree of adiabaticity. Exploring the properties of optimal adiabatic trajectories in model systems elucidates the dynamic mechanisms that suppress unwanted excitations from the ground state. Specifically, we discover that the use of multiple control functions makes it possible to access a rich set of dynamic trajectories, some of which attain a significantly improved performance (in terms of both fidelity and adiabaticity) through the increase of the energy gap during most of the evolution time.