MCTS discovers superior data encoding circuits for QCCNNs that outperform standard encodings on medical datasets, with effective rank of feature maps serving as a performance predictor.
Quantum Circuit Design using a Progres- sive Widening Enhanced Monte Carlo Tree Search
8 Pith papers cite this work. Polarity classification is still indexing.
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Adversarially encoded measurement deviations as small as 0.23% can produce false certification of high-dimensional entanglement in provably separable systems, demonstrated experimentally with classical photonic states up to 61 dimensions.
Full end-to-end hybrid training decouples trainability from PQC expressibility, unlike pure PQCs which show only a weak regime-dependent trade-off.
Introduces tunable partial-SWAP for controllable memory capacity in quantum reservoir networks, modeled as controlled amplitude-damping and validated via STMC and NARMA-5 benchmarks on simulators and IBM QPUs.
SpinTune applies reinforcement learning to discover adaptive dynamical decoupling sequences that outperform standard methods at preserving coherence in simulated Carbon-13 spin bath environments.
Derives two-film scattering theory for planar cavity magnonics that enables geometry-controlled bright-channel enhancement and symmetry-breaking effects on mode visibility.
Hardware transpilation of parameterized quantum circuits produces ansatz-dependent shifts in expressibility (up to 125%) and trainability (up to 25%), altering the expected trade-off between them.
A synthesis of van der Waals Josephson junction research showing how 2D material diversity and symmetry control open routes to novel quantum devices and sensors.
citing papers explorer
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Discovering Data Encoding Strategies for Quantum-Classical Neural Networks Using Monte Carlo Tree Search
MCTS discovers superior data encoding circuits for QCCNNs that outperform standard encodings on medical datasets, with effective rank of feature maps serving as a performance predictor.
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Faking entanglement with imperceptible measurement deviations
Adversarially encoded measurement deviations as small as 0.23% can produce false certification of high-dimensional entanglement in provably separable systems, demonstrated experimentally with classical photonic states up to 61 dimensions.
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Rethinking Expressibility-Trainability Trade-off in Hybrid Quantum Neural Networks
Full end-to-end hybrid training decouples trainability from PQC expressibility, unlike pure PQCs which show only a weak regime-dependent trade-off.
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Controllable Quantum Memory Capacity in Quantum Reservoir Networks with Tunable partial-SWAPs
Introduces tunable partial-SWAP for controllable memory capacity in quantum reservoir networks, modeled as controlled amplitude-damping and validated via STMC and NARMA-5 benchmarks on simulators and IBM QPUs.
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SpinTune: Improving the Reliability of Quantum Sensor Networks for Practical Quantum-Classical Utility
SpinTune applies reinforcement learning to discover adaptive dynamical decoupling sequences that outperform standard methods at preserving coherence in simulated Carbon-13 spin bath environments.
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Geometry-controlled magnon-polaritons of double magnetic films in planar cavities
Derives two-film scattering theory for planar cavity magnonics that enables geometry-controlled bright-channel enhancement and symmetry-breaking effects on mode visibility.
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Beyond Logical Circuits: Hardware-Aware Analysis of Expressibility and Trainability in Variational Quantum Algorithms
Hardware transpilation of parameterized quantum circuits produces ansatz-dependent shifts in expressibility (up to 125%) and trainability (up to 25%), altering the expected trade-off between them.
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New frontiers in quantum science and technology using van der Waals Josephson junctions
A synthesis of van der Waals Josephson junction research showing how 2D material diversity and symmetry control open routes to novel quantum devices and sensors.