A bilevel optimization method turns QAOA into a fully connected QBM that achieves 0.9559 target state probability noiseless and retains top probability under realistic noise levels.
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Pith papers citing it
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2026 10representative citing papers
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.
Integrating amplitude estimation into QNN readout achieves O(1/N) estimation error with one shot instead of the usual O(1/sqrt(N)) Monte Carlo scaling.
Quantum PINNs using tensor-rank polynomials solve the Merton portfolio optimization PDE more accurately and with far fewer parameters than classical neural networks.
A compact 2-qubit QNN approximates Black-Scholes-Merton option prices with usable accuracy when executed on multiple commercial NISQ quantum processors.
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.
Optimizing eight circuit parameters via gradient descent on classical Fisher information yields up to 1598% CFI gains and 133x more useful events per pulse for N=5 NOON states, pushing performance closer to the Heisenberg limit than the Afek et al. 2010 setup.
A 4-qubit QCNN classifies entanglement thresholds from fermion density profiles in the Thirring model more effectively than comparable classical CNNs.
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.
citing papers explorer
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Breaking QAOA's Fixed Target Hamiltonian Barrier: A Fully Connected Quantum Boltzmann Machine via Bilevel Optimization
A bilevel optimization method turns QAOA into a fully connected QBM that achieves 0.9559 target state probability noiseless and retains top probability under realistic noise levels.
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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.
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Single-shot quantum neural networks with amplitude estimation
Integrating amplitude estimation into QNN readout achieves O(1/N) estimation error with one shot instead of the usual O(1/sqrt(N)) Monte Carlo scaling.
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Learning PDEs for Portfolio Optimization with Quantum Physics-Informed Neural Networks
Quantum PINNs using tensor-rank polynomials solve the Merton portfolio optimization PDE more accurately and with far fewer parameters than classical neural networks.
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Option Pricing on Noisy Intermediate-Scale Quantum Computers: A Quantum Neural Network Approach
A compact 2-qubit QNN approximates Black-Scholes-Merton option prices with usable accuracy when executed on multiple commercial NISQ quantum processors.
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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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Quantum-Enhanced Single-Parameter Phase Estimation with Adaptive NOON States
Optimizing eight circuit parameters via gradient descent on classical Fisher information yields up to 1598% CFI gains and 133x more useful events per pulse for N=5 NOON states, pushing performance closer to the Heisenberg limit than the Afek et al. 2010 setup.
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Quantum Machine Learning for particle scattering entanglement classification
A 4-qubit QCNN classifies entanglement thresholds from fermion density profiles in the Thirring model more effectively than comparable classical CNNs.
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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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