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Tejas Shinde

Publications and source records attributed to Tejas Shinde.

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Quantum algorithms for second-order boundary value problems

Second-order boundary value problems are central to computational science, yet standard matrix-based numerical formulations can obscure the local geometric structure that quantum circuits may exploit. Here we introduce a framework for explicitly constructing finite-dimensional counterparts of continuous differential operators in a form compatible with quantum computation. Starting from the exterior derivative, its adjoint, and the Hodge operator, we derive discrete realizations of second-order operators on primal--dual cell complexes and reformulate them as star-local update rules expressed through explicit functions that return the relevant bounding chains rather than through matrix representations. This yields simple, uniform, and scalable quantum circuits. We demonstrate the construction for div--grad and curl--curl operators, showing that the same star-local compilation principle extends across different operators, cell complexes, and manifold dimensions within a common framework. More generally, the framework provides a systematic route to quantum algorithms for partial differential equations.

quant-ph

Utilizing classical programming principles in the Intel Quantum SDK: implementation of quantum lattice Boltzmann method

We explore the use of classical programming techniques in implementing the quantum lattice Boltzmann method in the Intel Quantum SDK -- a software tool for quantum circuit creation and execution on Intel quantum hardware. As hardware access is limited, we use the state vector simulator provided by the SDK. The novelty of this work lies in leveraging classical techniques for the implementation of quantum algorithms. We emphasize the refinement of algorithm implementation and devise strategies to enhance quantum circuits for better control over problem variables. To this end, we adopt classical principles such as modularization, which allows for systematic and controlled execution of complex algorithms. Furthermore, we discuss how the same implementation could be expanded from state vector simulations to execution on quantum hardware with minor adjustments in these configurations.

quant-ph