B-VQE is a biorthogonal variational quantum eigensolver with exceptional-point detection and importance sampling that simulates non-Hermitian many-body models on NISQ hardware with reported energy errors below 5e-3.
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representative citing papers
Complete MUB ensembles are optimal for isotropic Gaussian random-Hamiltonian width among d+1 basis unions, and adaptive MUB-XRot QAOA is non-worse than standard QAOA in 80% of 1500 benchmark cases across MaxCut, MIS, and knapsack.
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.
The paper proposes an eigenstate filtering (EF) variant of quantum inverse power iteration (QIPI) that uses symmetric QSVT polynomials to robustly target excited states, showing better convergence than Chebyshev or Fourier approaches on H2, LiH, and BeH2.
Quantum algorithm for photodissociation wavefunction propagation on quantum computers via split-operator, QFT, dilated non-unitary absorber, and Hadamard-test autocorrelation, matching benchmarks on NOCl under ideal conditions with noise robustness.
CV-ADAPT-VQE with tailored symmetry-preserving pools achieves significantly shallower circuits than Hamiltonian-based VQE for bosonic lattice models in GPU classical simulations.
Non-unitary variational ansatze restore finite gradients under noise in VQAs for open quantum steady states, shown on an infinite-range dissipative Ising model and applied to first-principles electron transport in OPE-SMe.
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.
citing papers explorer
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High-Precision Multi-Qubit Clifford+T Synthesis by Unitary Diagonalization
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.
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Accelerating Quantum Eigensolver Algorithms With Machine Learning
XGBoost models trained on ≤16-qubit data predict eigensolver hyperparameters and reduce error by 0.12% on 28-qubit systems.