clifford_qc is a Python research toolkit for constructing quantum models, comparing variational and subspace eigensolvers, and estimating measurement resources. A common sparse Pauli algebra connects Hamiltonians, density operators, gates, and candidate-selection observables, while dedicated interfaces handle circuit p...
Electronic-structure and embedding workflows terminate in effective many-body Hamiltonians, whereas quantum eigensolver studies often start from hand-built qubit models. We present the Adaptive Clifford-Algebra Subspace Eigensolver (A-CASE), a single-reference, operator-generated Rayleigh--Ritz method. Overlap, Hamilto...
A sparse operator-centric realization of $n-qubit variational quantum algorithms in the complex Clifford algebra, a density-operator derivation and implementation of the real-sector pool filter, and a reproducible study of measurement-limited adaptive selection are developed.
Quantum subspace methods are often compared by basis dimension or energyerror, although their dominant experimental costs arise from different statepreparations, measurement settings, and shot allocations. We present theDyadic Adaptive Clifford-Algebra Subspace Eigensolver (DA-CASE), whose basisstates are virtual direc...
The seamless integration of Density Functional Theory (DFT) with quantum variational algorithms is essential for the predictive simulation of strongly correlated materials. In this work, we present an end-to-end computational pipeline - comprising DFT geometry relaxation, non-self-consistent field (NSCF) calculations,...
The Variational Quantum Eigensolver (VQE) and its adaptive variants, such as ADAPT-VQE, are central to the study of strongly correlated quantum systems. However, the classical simulation of the ansatz growth process remains constrained by the exponential scaling of operator space and the associated computational cost o...
H. Dipojono· 1 citation· ⚡1
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