Bias in SMO-VQE can be estimated without extra measurements; a regularization method that mimics error accumulation while preserving unbiased estimates improves performance across system sizes and Hamiltonians.
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15 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 15representative citing papers
Riemannian modified Newton optimization on quantum search achieves quadratic convergence and O(√(N/M) log log(1/ε)) complexity when M/N is known.
A nonlinear custom penalty without slack variables plus CVaR sampling improves optimality gaps and consistency on knapsack instances for quantum constrained optimization.
Directly training soft-unitary matrices with a unitarity regularization term and converting them to circuits via alignment enables faster training and lower loss than gate-based optimization on small quantum classification and reinforcement learning tasks.
QFTLM computes thermal expectation values on quantum computers by merging quantum Krylov methods with efficient typical-state preparation for trace estimation.
Adaptive VQE exhibits exponential growth in iterations and circuit depth with system size, accurately predicted by classical Rényi entropy on molecules with 4-10 orbitals.
H-EFT-VA enforces a UV-cutoff initialization to guarantee inverse-polynomial gradient variance while preserving volume-law entanglement and near-Haar purity in variational quantum algorithms.
Orthogonal FDM with rectangular pulses suppresses interference to enable high-fidelity simultaneous gates on multiple qubits via a single microwave line.
A variational framework assisted by matrix product states prepares approximate thermal Gibbs states for 1D lattices up to 30 sites and 2D lattices up to 6x6 using up to 44 qubits, with a demonstration on IBM Heron hardware.
Demonstrates a quantum wire encoding using Rydberg atom chains to solve MWIS and QUBO problems on neutral atom arrays with reduced ancilla overhead and experimental validation.
A quantum solver for PDEs is introduced via flexible matrix product operator representations with mid-circuit measurements and state-dependent norm correction to handle non-unitary dynamics.
QuNetQFL is a quantum federated learning protocol using distributed quantum keys for secure aggregation, experimentally validated on a four-client quantum network with scalability simulations to 200 clients and applications to quantum datasets and hybrid language models.
Classical kernelisation fully reduces many small and sparse unit-disk graphs for MIS and MWIS native to Rydberg arrays, but dense graphs retain finite irreducible kernels, with vertex weights increasing reducibility and extended interaction ranges suppressing it.
Brillouin-Wigner perturbation theory plus Hartree-Fock mean-field approximation upgrades quasiparticle nuclear Hamiltonians, yielding <0.2% and ~2% ground-state energy errors versus exact shell-model results in the sd shell while preserving qubit efficiency.
AL-QHD benchmarks on nonconvex test functions and ACOPF power problems show useful accuracy at fixed qubit cost but require roughly 10^8 T gates for realistic instances.
citing papers explorer
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Bias Analysis and Regularization of Sequential Minimal Optimization in Variational Quantum Eigensolvers
Bias in SMO-VQE can be estimated without extra measurements; a regularization method that mimics error accumulation while preserving unbiased estimates improves performance across system sizes and Hamiltonians.
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Achieving double-logarithmic precision dependence in optimization-based quantum unstructured search
Riemannian modified Newton optimization on quantum search achieves quadratic convergence and O(√(N/M) log log(1/ε)) complexity when M/N is known.
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CVaR-Assisted Custom Penalty Function for Constrained Optimization
A nonlinear custom penalty without slack variables plus CVaR sampling improves optimality gaps and consistency on knapsack instances for quantum constrained optimization.
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Soft-Quantum Algorithms
Directly training soft-unitary matrices with a unitarity regularization term and converting them to circuits via alignment enables faster training and lower loss than gate-based optimization on small quantum classification and reinforcement learning tasks.
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Quantum Finite Temperature Lanczos Method
QFTLM computes thermal expectation values on quantum computers by merging quantum Krylov methods with efficient typical-state preparation for trace estimation.
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Exponential Scaling Barriers for Variational Quantum Eigensolvers
Adaptive VQE exhibits exponential growth in iterations and circuit depth with system size, accurately predicted by classical Rényi entropy on molecules with 4-10 orbitals.
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H-EFT-VA: An Effective-Field-Theory Variational Ansatz with Provable Barren Plateau Avoidance
H-EFT-VA enforces a UV-cutoff initialization to guarantee inverse-polynomial gradient variance while preserving volume-law entanglement and near-Haar purity in variational quantum algorithms.
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Orthogonal frequency-division multiplexing for simultaneous gate operations on multiple qubits via a shared control line
Orthogonal FDM with rectangular pulses suppresses interference to enable high-fidelity simultaneous gates on multiple qubits via a single microwave line.
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Variational Thermal State Preparation on Digital Quantum Processors Assisted by Matrix Product States
A variational framework assisted by matrix product states prepares approximate thermal Gibbs states for 1D lattices up to 30 sites and 2D lattices up to 6x6 using up to 44 qubits, with a demonstration on IBM Heron hardware.
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A quantum wire approach to weighted combinatorial graph optimisation problems
Demonstrates a quantum wire encoding using Rydberg atom chains to solve MWIS and QUBO problems on neutral atom arrays with reduced ancilla overhead and experimental validation.
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Tensor-Programmable Quantum Circuits for Solving Differential Equations
A quantum solver for PDEs is introduced via flexible matrix product operator representations with mid-circuit measurements and state-dependent norm correction to handle non-unitary dynamics.
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Experimentally validated quantum-secure federated learning over a multi-user quantum network
QuNetQFL is a quantum federated learning protocol using distributed quantum keys for secure aggregation, experimentally validated on a four-client quantum network with scalability simulations to 200 clients and applications to quantum datasets and hybrid language models.
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Reducibility of native weighted graphs on Rydberg Arrays
Classical kernelisation fully reduces many small and sparse unit-disk graphs for MIS and MWIS native to Rydberg arrays, but dense graphs retain finite irreducible kernels, with vertex weights increasing reducibility and extended interaction ranges suppressing it.
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Improved quasiparticle nuclear Hamiltonians for quantum computing
Brillouin-Wigner perturbation theory plus Hartree-Fock mean-field approximation upgrades quasiparticle nuclear Hamiltonians, yielding <0.2% and ~2% ground-state energy errors versus exact shell-model results in the sd shell while preserving qubit efficiency.
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Benchmarking and Resource Analysis for Augmented-Lagrangian Quantum Hamiltonian Descent
AL-QHD benchmarks on nonconvex test functions and ACOPF power problems show useful accuracy at fixed qubit cost but require roughly 10^8 T gates for realistic instances.