Complete MUB ensembles are optimal for isotropic Gaussian random-Hamiltonian width among d+1 basis unions, enabling adaptive MUB-XRot QAOA that is non-worse than standard QAOA in 80% of 1500 benchmark cases.
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representative citing papers
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.
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.
A compression protocol for controlled time evolution of local translationally invariant Hamiltonians achieves O(t polylog(t N/ε)) circuit depth with additive control overhead, demonstrated via 414 CNOT gates for iterative phase estimation on a 6×6 triangular lattice and sub-1% energy errors on a 4×4
Quantum circuits for coherent multilayer neural network inference achieve quadratic to polylogarithmic speedups over classical methods depending on quantum data access models for inputs and weights.
Search-based approximate diagonalization followed by analytical inversion yields high-precision multi-qubit Clifford+T circuits with 95% fewer non-Clifford gates on real-algorithm benchmarks.
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.
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.
A single-ancilla Power-Cosine QSP filter on time-evolution operators achieves deterministic many-body ground state preparation with exponential excited-state suppression and O(Δ^{-2} log(1/ε)) depth scaling.
An auxiliary-fermion encoding removes Jordan-Wigner strings for sparse non-local fermion models, achieving asymptotically optimal Trotter circuit depth on qubits after one-time state preparation.
DMET combined with SQD on IBM Eagle hardware achieves chemical accuracy for ground-state energies of low-symmetry ligand-like molecules.
ZAPT2 frozen natural orbitals reduce virtual space for systematic convergence of open-shell T1-S0 gaps in CASCI and iQCC quantum eigensolvers, demonstrated on H2O2, O2, CH2 and Ir(ppy)3.
Gaussian randomized rounding on two-qubit marginals of depth-D circuits with local depolarizing noise p yields samples whose expected Max-Cut cost matches the noisy quantum device up to an approximation ratio of 1-O[(1-p)^D].
Tensor-network fractional-step method simulates incompressible flows in curvilinear coordinates with up to 20x field compression and 1000x operator compression while keeping errors below 0.3% versus finite differences.
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.
citing papers explorer
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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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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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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.