SAFE ma-QAOA achieves 64.3% fewer active parameters and 94.5% lower estimated QPU workload via surrogate pre-training and parameter distillation on Sherrington-Kirkpatrick, 2D spin glass, and Max-Cut instances.
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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.
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.
COO co-optimizes orbitals with TrimCI to absorb many-body correlations into the basis, cutting determinant count by orders of magnitude for iron-sulfur clusters versus localized bases or DMRG.
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.
Presents a tensor-parallel distributed MPS method with block-cyclic partitioning and pivoted QR that emulates Google's RCS benchmark at bond dimension 16384 on 32 nodes, claiming three orders of magnitude better accuracy than prior methods.
Bayesian PSR with Gaussian processes and GradCoRe accelerates VQE SGD by reusing observations and minimizing per-step costs while reducing to standard PSR in special cases.
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.
Noise in LUCJ sampling for QSCI on N2 expands the configuration space beyond the ideal ansatz and, when paired with recovery, produces more accurate CI energies than noiseless sampling.
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.
JUQCS-50 achieves the first 50-qubit universal quantum computer simulation on the JUPITER supercomputer via CPU-GPU memory extension, adaptive encoding, and network optimization, delivering a 16.6-fold speedup over the prior 48-qubit record.
A quantum circuit prepares approximate Gaussian states via single-qubit rotations followed by QFT, achieving high fidelity with optional angle pruning for O(n) gate cost.
Swarm methods such as PSO, FIPSO, and QPSO yield lower approximation gaps and more stable convergence than Adam, COBYLA, or SPSA when tuning QAOA parameters on weighted MaxCut instances, especially under noise and limited shots.
citing papers explorer
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SAFE ma-QAOA: Surrogate-Assisted and Fine-Tuning Enhanced Multi-Angle QAOA with Parameter Distillation
SAFE ma-QAOA achieves 64.3% fewer active parameters and 94.5% lower estimated QPU workload via surrogate pre-training and parameter distillation on Sherrington-Kirkpatrick, 2D spin glass, and Max-Cut instances.
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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.
-
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.
-
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.
-
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.
-
Absorbing Many-Body Correlations into Core-Optimized Orbitals
COO co-optimizes orbitals with TrimCI to absorb many-body correlations into the basis, cutting determinant count by orders of magnitude for iron-sulfur clusters versus localized bases or DMRG.
-
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.
-
Tensor-Parallel Emulation of Quantum Circuits with Block-Cyclic Distributed Matrix Product States
Presents a tensor-parallel distributed MPS method with block-cyclic partitioning and pivoted QR that emulates Google's RCS benchmark at bond dimension 16384 on 32 nodes, claiming three orders of magnitude better accuracy than prior methods.
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Bayesian Parameter Shift Rule in Variational Quantum Eigensolvers
Bayesian PSR with Gaussian processes and GradCoRe accelerates VQE SGD by reusing observations and minimizing per-step costs while reducing to standard PSR in special cases.
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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.
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Noise and Configuration Recovery Impact on Quantum Selected Configuration Interaction
Noise in LUCJ sampling for QSCI on N2 expands the configuration space beyond the ideal ansatz and, when paired with recovery, produces more accurate CI energies than noiseless sampling.
-
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.
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Universal Quantum Computer Simulation of 50 Qubits on Europe`s First Exascale Supercomputer Harnessing Its Heterogeneous CPU-GPU Architecture
JUQCS-50 achieves the first 50-qubit universal quantum computer simulation on the JUPITER supercomputer via CPU-GPU memory extension, adaptive encoding, and network optimization, delivering a 16.6-fold speedup over the prior 48-qubit record.
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Efficient Gaussian State Preparation in Quantum Circuits
A quantum circuit prepares approximate Gaussian states via single-qubit rotations followed by QFT, achieving high fidelity with optional angle pruning for O(n) gate cost.
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Benchmarking Swarm Optimization Algorithms for Parameter Initialization in the Quantum Approximate Optimization Algorithm
Swarm methods such as PSO, FIPSO, and QPSO yield lower approximation gaps and more stable convergence than Adam, COBYLA, or SPSA when tuning QAOA parameters on weighted MaxCut instances, especially under noise and limited shots.