Publications by authors named "M Degroote"

The phase estimation algorithm is crucial for computing the ground-state energy of a molecular electronic Hamiltonian on a quantum computer. Its efficiency depends on the overlap between the Hamiltonian's ground state and an initial state, which tends to decay exponentially with system size. We showcase a practical orbital optimization scheme to alleviate this issue.

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Quantum phase estimation based on qubitization is the state-of-the-art fault-tolerant quantum algorithm for computing ground-state energies in chemical applications. In this context, the 1-norm of the Hamiltonian plays a fundamental role in determining the total number of required iterations and also the overall computational cost. In this work, we introduce the symmetry-compressed double factorization (SCDF) approach, which combines a CDF of the Hamiltonian with the symmetry shift technique, significantly reducing the 1-norm value.

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We present the first hardware implementation of electrostatic interaction energies by using a trapped-ion quantum computer. As test system for our computation, we focus on the reduction of NO to NO catalyzed by a nitric oxide reductase (NOR). The quantum computer is used to generate an approximate ground state within the NOR active space.

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In this work, we test a recently developed method to enhance classical auxiliary-field quantum Monte Carlo (AFQMC) calculations with quantum computers against examples from chemistry and material science, representative of classes of industry-relevant systems. As molecular test cases, we calculate the energy curve of H4 and the relative energies of ozone and singlet molecular oxygen with respect to triplet molecular oxygen, which is industrially relevant in organic oxidation reactions. We find that trial wave functions beyond single Slater determinants improve the performance of AFQMC and allow it to generate energies close to chemical accuracy compared to full configuration interaction or experimental results.

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The types of outcomes studied in children on the autism spectrum include clinical characteristics, such as social functioning, communication, language, or autism symptoms. Research that measures these outcomes at multiple timepoints is useful to improve our understanding of what to expect as children develop. In trajectory studies, researchers assess outcomes at three or more timepoints.

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