First analytic nuclear gradients derived and implemented for BSE@G0W0, validated on excited-state geometries and adiabatic energies against wavefunction benchmarks.
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AI coding agents evolve simple ground-state protocols into improved versions for VQE, DMRG, and AFQMC on spin models and molecules by using executable energy scores under fixed compute budgets.
QumVQD enables excited-state quantum chemistry calculations on bosonic qumode hardware by enforcing particle-number symmetry and using Hamiltonian fragmentation, achieving chemical accuracy on H2 and spectroscopic accuracy on vibrational modes with far fewer entangling gates than qubit equivalents.
Imaginary-time evolution in coupled-cluster theory reaches standard amplitude solutions when they exist but supplies additional regularization information via minima of a newly defined energy variance.
An ancilla-free quantum measurement scheme using local Clifford rotations and Pauli observables evaluates SCGVB matrix elements, demonstrated on H4 dissociation with results matching classical references.
Skala is a neural XC functional trained on wavefunction data that beats state-of-the-art hybrids on main-group chemistry benchmarks at semi-local computational cost.
A structure-preserving low-rank factorization of 2RDMs achieves linear rank scaling with system size and ~99% compression while retaining chemical accuracy for correlated states.
The Pauli principle and nuclear spin isomers of ammonia molecules significantly reshape collective light-matter coupling in infrared cavities, demonstrated via numerical simulations for two molecules and an analytical model for ensembles.
A commutativity-based dynamic ansatz within DMET enables ground-state simulations of molecules up to 144 qubits using at most 20 qubits at a time with improved accuracy and lower gate counts than standard approaches.
Correlated purification via bi-objective semidefinite programming restores N-representability to noisy 2-RDMs from fermionic shadow tomography and achieves chemical accuracy on hydrogen chain dissociation curves.
A two-fold quantum embedding strategy combined with machine learning integrates accurate quantum-mechanical energies into free energy calculations for biomolecular complexes and analyzes requirements for quantum computers to enhance such modeling.
NISQ quantum simulation of spin-wave spectra in 2D chromium tri-halide magnets achieves agreement with classical benchmarks at quasi-constant wall-time scaling.
Electronic excitations in SrCu2(BO3)2 include d-d transitions at 1.8-2.4 eV and charge-transfer onsets at 1.2-1.6 eV, matching quantum chemistry and DFT+U calculations.
Suppressed quantum chaos at the transition state enhances tunneling in H3+ and H5+ formation, quantified by a new fragility index derived from adiabatic gauge potential slopes.
A standardized VQE benchmark on IBM hardware shows tapered circuit mappings give the most consistent accuracy gains for H2, while resilience level 1 adds cost and session execution increases billed time without accuracy benefit.
CovAngelo implements a QM/QM/MM embedding model using quantum-information metrics to compute reaction energy profiles and barriers for covalent drug binding at lower cost than conventional methods, demonstrated on zanubrutinib to BTK.
Fermion mappings combined with Z2 tapering and frozen-core approximations reduce qubit counts by up to 50%, gate counts by up to 27.5x, and Pauli strings by up to 2.75x for VQE on small molecules.
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Electronic excitations in the Shastry-Sutherland compound SrCu$_2$(BO$_3$)$_2$
Electronic excitations in SrCu2(BO3)2 include d-d transitions at 1.8-2.4 eV and charge-transfer onsets at 1.2-1.6 eV, matching quantum chemistry and DFT+U calculations.