While chemical systems containing hundreds to thousands of electrons remain beyond the reach of quantum devices, hybrid quantum-classical algorithms present a promising pathway toward a quantum advantage. Hybrid algorithms treat the exponentially scaling part of the calculation-the static correlation-on the quantum computer and the non-exponentially scaling part-the dynamic correlation-on the classical computer. While a variety of algorithms have been proposed, the dependence of many methods on the total wave function limits the development of easy-to-use classical post-processing implementations. Here, we present a novel combination of quantum and classical algorithms, which computes the all-electron energy of a strongly correlated molecular system on the classical computer from the 2-electron reduced density matrix (2-RDM) evaluated on the quantum device. Significantly, we circumvent the wave function in the all-electron calculations by using density matrix methods that only require input of the statically correlated 2-RDM. Although the algorithm is completely general, we test it with two classical density matrix methods, the anti-Hermitian contracted Schrödinger equation (ACSE) and multiconfiguration pair-density functional theories, using the recently developed quantum ACSE method for simulating the statically correlated 2-RDM. We obtain experimental accuracy for the relative energies of all three benzyne isomers and thereby demonstrate the ability of the developed algorithm to achieve chemically relevant and accurate results on noisy intermediate-scale quantum devices.
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http://dx.doi.org/10.1063/5.0074842 | DOI Listing |
J Phys Chem A
January 2025
School of Applied Science and Humanities, Haldia Institute of Technology, ICARE Complex, Haldia 721657, India.
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Fischell Department of Bioengineering, University of Maryland, College Park, USA.
The development of optical sensors for label-free quantification of cell parameters has numerous uses in the biomedical arena. However, using current optical probes requires the laborious collection of sufficiently large datasets that can be used to calibrate optical probe signals to true metabolite concentrations. Further, most practitioners find it difficult to confidently adapt black box chemometric models that are difficult to troubleshoot in high-stakes applications such as biopharmaceutical manufacturing.
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January 2025
State Key Lab of New Ceramics and Fine Processing, School of Materials Science and Engineering, Tsinghua University, Beijing, China.
Polymer dielectric materials are widely used in electrical and electronic systems, and there have been increasing demands on their dielectric properties at high temperatures. Incorporating inorganic nanoparticles into polymers is an effective approach to improving their dielectric properties. However, the agglomeration of inorganic nanoparticles and the destabilization of the organic-inorganic interface at high temperatures have limited the development of nanocomposites toward large-scale industrial production.
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Duquesne University Graduate School for Pharmaceutical Sciences, Pittsburgh, PA 15282, United States; Duquesne Center for Pharmaceutical Technology, Duquesne University, Pittsburgh, PA 15282, United States. Electronic address:
The adoption of pure component models, such as iterative optimization technology (IOT) algorithms, is gaining significant interest in the pharmaceutical industry, primarily because of their calibration-free/minimal calibration requirements for process analytical technology applications. The IOT methods have recently demonstrated great potential for monitoring the quality of continuous powder mixtures by Near-infrared (NIR) spectroscopy. However, the dynamic conditions of continuous manufacturing processes may limit the effectiveness of such approaches.
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Guangzhou Huadu district drainage management center, Guangzhou 510800, China.
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