First quantum-hardware demonstration of Hamiltonian simulation for time-domain Maxwell's equations via Schrödingerisation, with signed field measurements and agreement to classical benchmarks in 2D/3D.
Variational quantum algorithms.Nature Reviews Physics, 3(9):625–644
6 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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quant-ph 6years
2026 6verdicts
UNVERDICTED 6representative citing papers
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.
Adversaries perturbing shared entanglement in distributed VQAs can manipulate a new Kraus expressibility metric to keep gradients large but steer training to incorrect solutions.
Reinforcement learning policy for qubit mapping reduces SWAP overhead by 65-85% versus standard quantum compilers on MQTBench and Queko benchmark circuits.
QEL is the first quantum end-to-end learning framework for contextual combinatorial optimization using QAOA with a context re-uploading phase-separator, achieving competitive performance with fewer parameters.
MZeQAS accelerates quantum architecture search for VQAs by replacing full training of candidates with a zero-shot performance estimate derived from QNTK Gram-matrix convergence.
citing papers explorer
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Hardware Realization of a Hamiltonian Simulation Algorithm for Time-Domain Maxwells Equations
First quantum-hardware demonstration of Hamiltonian simulation for time-domain Maxwell's equations via Schrödingerisation, with signed field measurements and agreement to classical benchmarks in 2D/3D.
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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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CO-MAP: A Reinforcement Learning Approach to the Qubit Allocation Problem
Reinforcement learning policy for qubit mapping reduces SWAP overhead by 65-85% versus standard quantum compilers on MQTBench and Queko benchmark circuits.
-
Quantum End-to-End Learning for Contextual Combinatorial Optimization
QEL is the first quantum end-to-end learning framework for contextual combinatorial optimization using QAOA with a context re-uploading phase-separator, achieving competitive performance with fewer parameters.
-
Zero-shot Quantum Neural Architecture Search
MZeQAS accelerates quantum architecture search for VQAs by replacing full training of candidates with a zero-shot performance estimate derived from QNTK Gram-matrix convergence.