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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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.
Hybrid Path-Sums offer a new symbolic framework with rewriting rules and assertions to represent, simplify, and verify properties of hybrid quantum-classical programs.
Global Bradley-Terry rankings of LLMs are misleading due to structured heterogeneity in user preferences, and small (λ, ν)-portfolios recover coherent subpopulations that cover over 96% of votes with just five rankings.
QTL unifies expectation-value minimization with CVaR and Gibbs heuristics under one tunable operator, amplifying gradients in structured cases while preserving global minima and shifting the bottleneck to measurement variance.
CCV-QAOA is a new complex-valued continuous-variable variant of QAOA that solves real and complex multivariate optimization problems via a variational framework.
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.
Meta-learning with 24 classical complexity metrics predicts the optimal quantum encoding circuit among 9 candidates with up to 85.7% top-3 accuracy.
Structure-aware VQE ansatze for long-range Ising models cut required circuit layers by 2.5x to 3.8x in non-local regimes while two-qubit gate counts scale quadratically with system size, consistent with the number of Hamiltonian terms.
A necessary condition for variational quantum circuits to reach exact ground states requires matching module projection norms between input and solution, enabling classical O(n^5) exact solvers for problems like MaxCut.
A new QNN architecture with unified graph, HAL, and ONNX pipeline enables cross-framework and cross-hardware QML with training time within 8% of native implementations and identical accuracy on Iris, Wine, and MNIST-4 tasks.
QuantumXCT learns parameterized quantum circuits to model interaction-induced unitary transformations between non-interacting and interacting cellular state distributions from transcriptomic profiles.
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.
A VQE quantum-computing method for nuclear lattice models shows ground-state energies for 2H, 3H, and 4He approaching experimental values with increasing lattice size.
DistributedEstimator demonstrates that circuit cutting preserves test accuracy and robustness in QNN training on Iris and MNIST while revealing that classical reconstruction dominates runtime and exponential subcircuit growth limits scaling.
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.
VQE with EfficientSU2 and Hamiltonian Variational ansatze approximates TFIM ground states and entanglement in up to 3D lattices of 27 spins, with accuracy tied to ansatz expressivity and optimization success.
New merge booster and diagonal detector components, combined with cache blocking and gate fusion, deliver up to 160x speedup on circuit benchmarks and 34x on diagonal-heavy gates versus prior simulators.
citing papers explorer
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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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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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Hybrid Path-Sums for Hybrid Quantum Programs
Hybrid Path-Sums offer a new symbolic framework with rewriting rules and assertions to represent, simplify, and verify properties of hybrid quantum-classical programs.
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Why Global LLM Leaderboards Are Misleading: Small Portfolios for Heterogeneous Supervised ML
Global Bradley-Terry rankings of LLMs are misleading due to structured heterogeneity in user preferences, and small (λ, ν)-portfolios recover coherent subpopulations that cover over 96% of votes with just five rankings.
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Quantum Tilted Loss in Variational Optimization: Theory and Applications
QTL unifies expectation-value minimization with CVaR and Gibbs heuristics under one tunable operator, amplifying gradients in structured cases while preserving global minima and shifting the bottleneck to measurement variance.
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A Complex-Valued Continuous-Variable Quantum Approximation Optimization Algorithm (CCV-QAOA)
CCV-QAOA is a new complex-valued continuous-variable variant of QAOA that solves real and complex multivariate optimization problems via a variational framework.
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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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Towards Automated Selection of Quantum Encoding Circuits via Meta-Learning
Meta-learning with 24 classical complexity metrics predicts the optimal quantum encoding circuit among 9 candidates with up to 85.7% top-3 accuracy.
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Scaling of Quantum Resources for Simulating a Long-Range System
Structure-aware VQE ansatze for long-range Ising models cut required circuit layers by 2.5x to 3.8x in non-local regimes while two-qubit gate counts scale quadratically with system size, consistent with the number of Hamiltonian terms.
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Reachability Constraints in Variational Quantum Circuits: Optimization within Polynomial Group Module
A necessary condition for variational quantum circuits to reach exact ground states requires matching module projection norms between input and solution, enabling classical O(n^5) exact solvers for problems like MaxCut.
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Eliminating Vendor Lock-In in Quantum Machine Learning via Framework-Agnostic Neural Networks
A new QNN architecture with unified graph, HAL, and ONNX pipeline enables cross-framework and cross-hardware QML with training time within 8% of native implementations and identical accuracy on Iris, Wine, and MNIST-4 tasks.
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QuantumXCT: Learning Interaction-Induced State Transformation in Cell-Cell Communication via Quantum Entanglement and Generative Modeling
QuantumXCT learns parameterized quantum circuits to model interaction-induced unitary transformations between non-interacting and interacting cellular state distributions from transcriptomic profiles.
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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.
-
Quantum computing for effective nuclear lattice model
A VQE quantum-computing method for nuclear lattice models shows ground-state energies for 2H, 3H, and 4He approaching experimental values with increasing lattice size.
-
DistributedEstimator: Distributed Training of Quantum Neural Networks via Circuit Cutting
DistributedEstimator demonstrates that circuit cutting preserves test accuracy and robustness in QNN training on Iris and MNIST while revealing that classical reconstruction dominates runtime and exponential subcircuit growth limits scaling.
-
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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Ans\"atz Expressivity and Optimization in Variational Quantum Simulations of Transverse-field Ising Model Across System Sizes
VQE with EfficientSU2 and Hamiltonian Variational ansatze approximates TFIM ground states and entanglement in up to 3D lattices of 27 spins, with accuracy tied to ansatz expressivity and optimization success.
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Large-Scale Quantum Circuit Simulation on HPC Cluster via Cache Blocking, Boosting, and Gate Fusion Optimization
New merge booster and diagonal detector components, combined with cache blocking and gate fusion, deliver up to 160x speedup on circuit benchmarks and 34x on diagonal-heavy gates versus prior simulators.