Considerations for evaluating thermodynamic properties with hybrid quantum-classical computing work flows

Stober, Spencer T.; Harwood, Stuart M.; Greenberg, Donny; Gujarati, Tanvi P.; Mostame, Sarah; Trenev, Dimitar

doi:10.1103/physreva.105.012425

Cited by 9 publications

(3 citation statements)

References 42 publications

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“…Certainly, there are already glimpses that they could provide for a transformative change in the way thermophysical properties are predicted. 53 This is, however, a very long-term prediction which would be foolish to appraise. 14…”

Section: Hardwarementioning

confidence: 99%

Molecular modelling of the thermophysical properties of fluids: expectations, limitations, gaps and opportunities

Tillotson

Diamantonis

Buda³

et al. 2023

Phys. Chem. Chem. Phys.

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Section: Hardwarementioning

confidence: 99%

Molecular modelling of the thermophysical properties of fluids: expectations, limitations, gaps and opportunities

Tillotson

Diamantonis

Buda³

et al. 2023

Phys. Chem. Chem. Phys.

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“…Meanwhile, some organizations have identified the chemical industry to be in a privileged position to take early advantage of QC. 20,21 Research is progressing, and some early experiments are using QC to address chemical engineering problems on model systems, [22][23][24] as well as estimating resource requirements for full-scale applications. [25][26][27] Chemical and biomolecular product design may also benefit from QC-assisted computational tools for applications, including quantum chemical property calculations, molecular reaction dynamics, and others.…”

Section: Introductionmentioning

confidence: 99%

Perspectives of quantum computing for chemical engineering

et al. 2022

Self Cite

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Quantum computing has been attracting public attention recently. This interest is driven by the advancements in hardware, software, and algorithms required for its successful usage and the promise that it entails the potential acceleration of computational tasks compared to classical computing. This perspective article presents a short review on quantum computing, how this computational approach solves problems, and three fields that quantum computing can potentially impact the most while relevant to chemical engineering: computational chemistry, optimization, and machine learning. Here, we present a series of chemical engineering applications, the developments, potential improvements with respect to classical computing, and challenges that quantum computing faces for each of these fields. This article intends to provide a clear picture of the challenges and potential advantages that quantum technology may yield for chemical engineering, together with an invitation for our colleagues to join us in the adoption and development of quantum computing.

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“…It has also been shown that computing first-and second-order energy derivatives of ground and excited state energies can be performed using variational methods [6,18,20]. Second-order energy deriva- * jack.ceroni@mail.utoronto.ca tives can be used to characterize the vibrational structure of molecules, which allows for the prediction of chemical properties such as vibronic spectra [21], simulating local mode dynamics [22,23] and electron transport [24], and computing thermodynamic observables [25].…”

Section: Introductionmentioning

confidence: 99%

Tailgating quantum circuits for high-order energy derivatives

Ceroni¹,

Delgado²,

Jahangiri³

et al. 2022

Preprint

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To understand the chemical properties of molecules, it is often important to study derivatives of energies with respect to nuclear coordinates or external fields. Quantum algorithms for computing energy derivatives have been proposed, but only limited work has been done to address the specific challenges that arise in this context, where calculations are more complicated and involve more stringent requirements on accuracy compared to single-point energy calculations. In this work, we introduce a technique to improve the performance of variational quantum circuits calculating energy derivatives. The method, which we refer to as tailgating, is an adaptive procedure that selects gates based on their gradient with respect to the expectation value of Hamiltonian derivatives. These gates are then added at the end of a quantum circuit originally designed to calculate ground-or excited-state energies. A distinguishing feature of this approach is that the appended gates do not need to be optimized: their parameters can be set to zero and varied only for the purpose of computing energy derivatives, via calculating derivatives with respect to circuit parameters. We support the validity of this method by establishing sufficient conditions for a circuit to compute accurate energy gradients. This is achieved through a connection between energy derivatives and eigenstates of Taylor approximations of the Hamiltonian. We illustrate the advantages of the tailgating approach by performing simulations calculating the vibrational modes of beryllium hydride and water: quantities that depend on second-order energy derivatives.

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Considerations for evaluating thermodynamic properties with hybrid quantum-classical computing work flows

Cited by 9 publications

References 42 publications

Molecular modelling of the thermophysical properties of fluids: expectations, limitations, gaps and opportunities

Molecular modelling of the thermophysical properties of fluids: expectations, limitations, gaps and opportunities

Perspectives of quantum computing for chemical engineering

Tailgating quantum circuits for high-order energy derivatives

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