Coherent-state propagation enables quasi-polynomial classical simulation of bosonic circuits with logarithmically many Kerr gates at exponentially small trace-distance error, with polynomial runtime in the weak-nonlinearity regime.
Title resolution pending
5 Pith papers cite this work, alongside 30 external citations. Polarity classification is still indexing.
5
Pith papers citing it
30
external citations · Crossref
citation-role summary
background 1
citation-polarity summary
fields
quant-ph 5verdicts
UNVERDICTED 5roles
background 1polarities
background 1representative 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.
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.
New scalable QRAM simulator reveals post-selection constraints on error filtration and produces refined near-deterministic performance criteria.
Edge-coloring eliminates automorphisms in low-weight stabilizer subgraphs of generalized bicycle codes, enabling improved anisotropic min-sum decoding.
citing papers explorer
-
Coherent-State Propagation: A Computational Framework for Simulating Bosonic Quantum Systems
Coherent-state propagation enables quasi-polynomial classical simulation of bosonic circuits with logarithmically many Kerr gates at exponentially small trace-distance error, with polynomial runtime in the weak-nonlinearity regime.
-
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.
-
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.
-
Refined Criteria for QRAM Error Suppression via Efficient Large-Scale QRAM Simulator
New scalable QRAM simulator reveals post-selection constraints on error filtration and produces refined near-deterministic performance criteria.
-
Edge-Based Anisotropic Decoding for Generalized Bicycle Codes
Edge-coloring eliminates automorphisms in low-weight stabilizer subgraphs of generalized bicycle codes, enabling improved anisotropic min-sum decoding.