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Enhancing the accuracy and efficiency of sample-based quantum diagonalization with phaseless auxiliary-field quantum Monte Carlo
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abstract
Quantum Selected Configuration Interaction (QSCI) and an extended protocol known as Sample-based Quantum Diagonalization (SQD) have emerged as promising algorithms to solve the electronic Schr\"odinger equation with noisy quantum computers. In QSCI/SQD a quantum circuit is repeatedly prepared on the quantum device, and measured configurations form a subspace of the many-body Hilbert space in which the Hamiltonian is diagonalized classically. For the dissociation of N$_2$ and a model $\mathrm{[2Fe-2S]}$ cluster (correlating 10 electrons in 26 orbitals and 30 electrons in 20 orbitals, respectively) we show that a non-perturbative stochastic approach, phaseless auxiliary-field quantum Monte Carlo (ph-AFQMC), using truncated SQD trial wavefunctions obtained from quantum hardware can recover a substantial amount (e.g., O(100) mHa) of correlation energy and alleviate the possible sampling redundancy of the QSCI/SQD procedure. Extrapolation of the ph-AFQMC energy versus the energy variance of the SQD trial wavefunctions has the potential to further improve the energy accuracy.
Forward citations
Cited by 3 Pith papers
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Towards Chemically Accurate and Scalable Quantum Simulations on IQM Quantum Hardware: A Quantum-HPC Hybrid Approach
SQD with LUCJ (and a new LCNot-UCCSD variant) on IQM Sirius recovers chemically accurate energies, 1D/2D PES, and DMET-embedded ligand/amantadine results versus FCI/CASCI references.
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Coupled cluster method tailored by quantum selected configuration interaction
QSCI-TCC combines quantum-selected CI with tailored coupled-cluster to achieve accurate bond-breaking energies while using far fewer measurement shots.
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Machine-Learned Compact Subspace Generation for Quantum Selected Configuration Interaction within Density Matrix Embedding Framework
An RBM-guided selected-CI solver inside DMET reaches the DMET-CASCI energy within 1.6 mHa using ~4% of the symmetry-valid configuration subspace on an 11-fragment protein–ligand model.
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