Speaker
Description
Quantum computing offers a promising route to solving many-body Hamiltonians in quantum chemistry, astrophysical molecular ions, and nuclear physics. However, as the basis set or active space grows, the configuration space increases combinatorially, making full diagonalization impractical. Variational Quantum Eigensolver approaches also face major bottlenecks from Pauli-term measurements, iterative classical optimization, gate depth, and hardware noise. In this talk, I present Sample-based Quantum Diagonalization (SQD) as a compact alternative to deep VQE workflows. Rather than optimizing a full variational ansatz, SQD uses quantum circuits to sample physically important configurations, constructs a reduced subspace, and classically diagonalizes the projected Hamiltonian. Benchmark applications to HeH⁺ and ArH⁺, astrophysically relevant molecular ions, show that SQD can reproduce CCSD-level results within a few mHa in the same basis. I will also discuss how this sampling-and-subspace strategy may provide a natural bridge from molecular electronic structure to nuclear many-body Hamiltonians.
