CRiSP uses neural-guided MCTS and curriculum learning to insert Clifford prefixes before parameterized rotations in VQAs, yielding mean 3.17x and max 45x gains in energy accuracy on 22-qubit QAOA benchmarks versus prior Clifford initializers.
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Quantum computing in the nisq era and beyond.Quantum, 2:79, 2018
13 Pith papers cite this work. Polarity classification is still indexing.
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Adversaries perturbing shared entanglement in distributed VQAs can manipulate a new Kraus expressibility metric to keep gradients large but steer training to incorrect solutions.
Treating the replay buffer as a central lever in RL for quantum circuit optimization yields 4-32x sample efficiency gains, up to 67.5% faster episodes, and 85-90% fewer steps to accuracy on noisy molecular and compilation tasks.
CLP-ZNE performs zero-noise extrapolation by averaging over cyclic permutations of circuit layouts, requiring O(n) executions for 1D connectivity and at most O(n^2) for arbitrary connectivity, and reduces errors by an order of magnitude in n=12 qubit benchmarks modeled on IBM Torino hardware.
Bra-ket entanglement indicates a shift from coherence-dominated to magic-dominated entanglement generation as its value increases.
Experimental demonstration of a multiplexing trapped-ion QPU using sample-and-hold circuits achieves motional heating rates below 1 phonon/s and expected gate errors below 10^{-4} for sampling intervals under 50 ms.
An oracle-free Trotter-based quantum algorithm for nonadiabatic molecular dynamics achieves circuit depth advantages over QROM architectures and retains T-gate scalability compared to quantum signal processing.
Optimal control theory designs high-fidelity quantum gates in multilevel systems that incorporate thermal relaxation and enable targeted cooling or heating during operation.
Quasi-Monte Carlo integration in LCU-CPP produces lower errors than Monte Carlo or trapezoidal rules for quantum operator estimation in two numerical experiments.
The authors convert an automotive ILP to max-XORSAT and implement DQI with a quantum circuit for belief propagation decoding, benchmarking against Gurobi and random sampling.
A modular Quantum Sequential Model is proposed and compared against classical regression and symmetry-constrained quantum regressors for predicting hydration status from urinary biomarkers, highlighting opportunities and limitations of near-term quantum computing in digital health.
A position and survey paper that identifies convergence between neuroscience, AGI, and neuromorphic computing and outlines four key integration challenges.
A literature review of VQAs covering ansatz design, classical optimization, barren plateaus, error mitigation strategies, and theoretical adaptations for fault-tolerant quantum computing.
citing papers explorer
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Classical State Preparation for Variational Quantum Algorithms via Reinforcement Learning
CRiSP uses neural-guided MCTS and curriculum learning to insert Clifford prefixes before parameterized rotations in VQAs, yielding mean 3.17x and max 45x gains in energy accuracy on 22-qubit QAOA benchmarks versus prior Clifford initializers.
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Adversarial Effects on Expressibility and Trainability in Distributed Variational Quantum Algorithms
Adversaries perturbing shared entanglement in distributed VQAs can manipulate a new Kraus expressibility metric to keep gradients large but steer training to incorrect solutions.
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Replay-buffer engineering for noise-robust quantum circuit optimization
Treating the replay buffer as a central lever in RL for quantum circuit optimization yields 4-32x sample efficiency gains, up to 67.5% faster episodes, and 85-90% fewer steps to accuracy on noisy molecular and compilation tasks.
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Zero-Noise Extrapolation via Cyclic Permutations of Quantum Circuit Layouts
CLP-ZNE performs zero-noise extrapolation by averaging over cyclic permutations of circuit layouts, requiring O(n) executions for 1D connectivity and at most O(n^2) for arbitrary connectivity, and reduces errors by an order of magnitude in n=12 qubit benchmarks modeled on IBM Torino hardware.
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Bra-ket entanglement, an indicator bridging entanglement, magic, and coherence
Bra-ket entanglement indicates a shift from coherence-dominated to magic-dominated entanglement generation as its value increases.
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Demonstration of a Multiplexing Trapped Ion Quantum Processing Unit
Experimental demonstration of a multiplexing trapped-ion QPU using sample-and-hold circuits achieves motional heating rates below 1 phonon/s and expected gate errors below 10^{-4} for sampling intervals under 50 ms.
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An Oracle-Free Quantum Algorithm for Nonadiabatic Quantum Molecular Dynamics
An oracle-free Trotter-based quantum algorithm for nonadiabatic molecular dynamics achieves circuit depth advantages over QROM architectures and retains T-gate scalability compared to quantum signal processing.
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Optimal Control of thermally noisy quantum gates in a multilevel system
Optimal control theory designs high-fidelity quantum gates in multilevel systems that incorporate thermal relaxation and enable targeted cooling or heating during operation.
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Quasi-Monte Carlo Method for Linear Combination Unitaries via Classical Post-Processing
Quasi-Monte Carlo integration in LCU-CPP produces lower errors than Monte Carlo or trapezoidal rules for quantum operator estimation in two numerical experiments.
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Towards solving industrial integer linear programs with Decoded Quantum Interferometry
The authors convert an automotive ILP to max-XORSAT and implement DQI with a quantum circuit for belief propagation decoding, benchmarking against Gurobi and random sampling.
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Hydration Monitoring Using Urinary Biomarkers: A Hybrid Classical Quantum Predictive Modeling Framework
A modular Quantum Sequential Model is proposed and compared against classical regression and symmetry-constrained quantum regressors for predicting hydration status from urinary biomarkers, highlighting opportunities and limitations of near-term quantum computing in digital health.
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Bridging Brains and Machines: A Unified Frontier in Neuroscience, Artificial Intelligence, and Neuromorphic Systems
A position and survey paper that identifies convergence between neuroscience, AGI, and neuromorphic computing and outlines four key integration challenges.
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A Review of Variational Quantum Algorithms: Insights into Fault-Tolerant Quantum Computing
A literature review of VQAs covering ansatz design, classical optimization, barren plateaus, error mitigation strategies, and theoretical adaptations for fault-tolerant quantum computing.