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.
citing papers explorer
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Fully Analytic Nuclear Gradients for the Bethe--Salpeter Equation
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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Optimizing ground state preparation protocols with autoresearch
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.
-
Excited-State Quantum Chemistry on Qumode-Based Processors via Variational Quantum Deflation
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.
-
Coupled-Cluster Imaginary-Time Evolution and the Coupled-Cluster Energy Variance
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.
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Compactifying the Electronic Wavefunction II: Quantum Estimators for Spin-Coupled Generalized Valence Bond Wavefunctions Applied to H4
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.
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Accurate and scalable exchange-correlation with deep learning
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.
-
Low-rank compression of two-electron reduced density matrices
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.
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Nuclear Spin Isomers and the Pauli Principle in Polaritonic Chemistry
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.
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Advancing Practical Quantum Embedding Simulations via Operator Commutativity Based State Preparation for Complex Chemical Systems
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 for Restoring $N$-Representability in Quantum Simulation
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.
-
How to use quantum computers for biomolecular free energies
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.
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Quantum Simulation of Magnetic Materials: from Ab-Initio to NISQ
NISQ quantum simulation of spin-wave spectra in 2D chromium tri-halide magnets achieves agreement with classical benchmarks at quasi-constant wall-time scaling.
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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.
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Chaos Gated Tunneling Drives Molecular Reactivity in Astrophysical Environments
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.
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Accuracy-Cost Trade-offs for Reference VQE Calculations of H$_2$ on IBM Quantum Hardware
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.
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CovAngelo: A hybrid quantum-classical computing platform for accurate and scalable drug discovery
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.
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Resource Estimation for VQE on Small Molecules: Impact of Fermion Mappings and Hamiltonian Reductions
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.