Optimal FALQON optimizes per-layer δ_k and M_k via classical methods, yielding statistically significant gains in success probability and efficiency over standard FALQON on 94 non-isomorphic 3-regular graphs with 12 vertices.
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The theory of variational hybrid quantum-classical algorithms
8 Pith papers cite this work, alongside 1,858 external citations. Polarity classification is still indexing.
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Zero-noise extrapolation has a finite-shot help-harm boundary below which it increases local mean-squared error due to variance penalties outweighing bias reduction.
Local tensor-train surrogates approximate quantum machine learning models via Taylor polynomials and tensor networks, delivering polynomial parameter scaling and explicit generalization bounds controlled by patch radius.
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.
A gate-based adiabatic quantum simulation framework for the SSHH model, validated by classical circuit simulations, shows topological signatures remain robust to weak Hubbard interactions but collapse beyond a symmetry-breaking threshold, with polynomial resource scaling.
VQE applied to deuteron, triton, and helium-3 in lattice pionless EFT yields energies matching classical exact diagonalization after fitting two- and three-body constants, with a noisy simulation example for triton.
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.
Chemical properties and symmetries, not variational energy, should guide UHF trial selection for ph-AFQMC on iron-sulfur clusters, yielding accurate energies despite suboptimal sampling and bias compensation.
citing papers explorer
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Optimal FALQON for Quantum Approximate Optimization via Layer-wise Parameter Tuning
Optimal FALQON optimizes per-layer δ_k and M_k via classical methods, yielding statistically significant gains in success probability and efficiency over standard FALQON on 94 non-isomorphic 3-regular graphs with 12 vertices.
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The finite-shot help-harm boundary of zero-noise extrapolation
Zero-noise extrapolation has a finite-shot help-harm boundary below which it increases local mean-squared error due to variance penalties outweighing bias reduction.
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Local tensor-train surrogates for quantum learning models
Local tensor-train surrogates approximate quantum machine learning models via Taylor polynomials and tensor networks, delivering polynomial parameter scaling and explicit generalization bounds controlled by patch radius.
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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.
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Adiabatic Quantum Simulation of the Topological Su--Schrieffer--Heeger--Hubbard Model
A gate-based adiabatic quantum simulation framework for the SSHH model, validated by classical circuit simulations, shows topological signatures remain robust to weak Hubbard interactions but collapse beyond a symmetry-breaking threshold, with polynomial resource scaling.
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Systematic VQE Benchmarking of the Deuteron, Triton, and Helium-3 within Lattice Pionless Effective Field Theory
VQE applied to deuteron, triton, and helium-3 in lattice pionless EFT yields energies matching classical exact diagonalization after fitting two- and three-body constants, with a noisy simulation example for triton.
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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.
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Selecting optimal unrestricted Hartree-Fock trial wavefunctions for phaseless auxiliary-field quantum Monte Carlo: Accuracy and limitations in modeling three iron-sulfur clusters
Chemical properties and symmetries, not variational energy, should guide UHF trial selection for ph-AFQMC on iron-sulfur clusters, yielding accurate energies despite suboptimal sampling and bias compensation.