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Paper 1
Quantum Operating System Support for Quantum Trusted Execution Environments
Theodoros Trochatos, Jakub Szefer
- Year
- 2024
- Journal
- arXiv preprint
- DOI
- arXiv:2410.08486
- arXiv
- 2410.08486
With the growing reliance on cloud-based quantum computing, ensuring the confidentiality and integrity of quantum computations is paramount. Quantum Trusted Execution Environments (QTEEs) have been proposed to protect users' quantum circuits when they are submitted to remote cloud-based quantum computers. However, deployment of QTEEs necessitates a Quantum Operating Systems (QOS) that can support QTEEs hardware and operation. This work introduces the first architecture for a QOS to support and enable essential steps required for secure quantum task execution on cloud platforms.
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Direct Variational Calculation of Two-Electron Reduced Density Matrices via Semidefinite Machine Learning
Luis H. Delgado-Granados, David A. Mazziotti
- Year
- 2026
- Journal
- arXiv preprint
- DOI
- arXiv:2603.05524
- arXiv
- 2603.05524
We introduce a data-driven framework for approximating the convex set of $N$-representable two-electron reduced density matrices (2-RDMs). Traditional approaches characterize this set through linear matrix inequalities that define its supporting hyperplanes. Here, we instead learn a vertex-based approximation to its boundary from molecular data and use this information to improve the set defined by low-order positivity constraints, without explicitly constructing higher-order conditions. The resulting semidefinite machine learning approach -- combining an input convex neural network with semidefinite programming -- drives a direct variational calculation of the 2-RDM with enhanced accuracy at computational cost comparable to two-positivity calculations. Applications to the potential energy curves of ${\rm C}_2^{2-}$, ${\rm N}_2$, and ${\rm O}_2^{2+}$ demonstrate these systematic improvements as well as close agreement with complete active space configuration interaction results. Overall, semidefinite machine learning interweaves data-driven boundary information with semidefinite positivity constraints to yield more accurate energies and 2-RDMs without explicit higher-order positivity conditions.
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