Specification of fact calculations within the MetaMIS approach

Based on recent work on the so called MetaMIS approach we show how fact calculations can be specified from a management point of view. The MetaMIS approach's intention is to specify management views on business processes. It comprises a language, a representation formalism and guidelines to define information required for management decisions. Information in general should have pragmatic meaning for the management user. Beyond the task of specifying information in this sense, fact calculations are required to manipulate information. Respective analyzing tasks typically deal with variances, growth rates and other relevant aspects of business processes. We extend the MetaMIS approach to enable the specification of fact calculations in data mart environments. <br

Measuring the Loschmidt amplitude for finite-energy properties of the Fermi-Hubbard model on an ion-trap quantum computer

Calculating the equilibrium properties of condensed matter systems is one of the promising applications of near-term quantum computing. Recently, hybrid quantum-classical time-series algorithms have been proposed to efficiently extract these properties from a measurement of the Loschmidt amplitude $\langle \psi| e^{-i \hat H t}|\psi \rangle$ from initial states $|\psi\rangle$ and a time evolution under the Hamiltonian $\hat H$ up to short times $t$. In this work, we study the operation of this algorithm on a present-day quantum computer. Specifically, we measure the Loschmidt amplitude for the Fermi-Hubbard model on a $16$-site ladder geometry (32 orbitals) on the Quantinuum H2-1 trapped-ion device. We assess the effect of noise on the Loschmidt amplitude and implement algorithm-specific error mitigation techniques. By using a thus-motivated error model, we numerically analyze the influence of noise on the full operation of the quantum-classical algorithm by measuring expectation values of local observables at finite energies. Finally, we estimate the resources needed for scaling up the algorithm.Comment: 18 pages, 12 figure