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.
hub
Pseudorandom Generators with- out the XOR Lemma
14 Pith papers cite this work, alongside 2,651 external citations. Polarity classification is still indexing.
14
Pith papers citing it
2,651
external citations · Crossref
hub tools
citation-role summary
background 4
citation-polarity summary
roles
background 4polarities
background 4representative citing papers
A dual Fourier-PSF and contour-PSF framework resolves the smoothness-sparsity trade-off for efficient quantum simulation of singular and holomorphic matrix functions.
New scalable QRAM simulator reveals post-selection constraints on error filtration and produces refined near-deterministic performance criteria.
Lattice-surgery scheduling is mapped to 3D path embedding and solved with look-ahead Dijkstra projection, yielding 3.8x lower execution time on quantum phase estimation benchmarks versus greedy scheduling.
The 2D HLF problem lies in QNC^0 but not in AC^0, with an exponential average-case correlation lower bound against AC^0 circuits.
Quantum circuits show high average condition (97.56%) and decision (97.63%) coverage but lower path coverage (71.84%), with probabilistic versions adding confidence levels (averages 88.87%, 88.65%, 37.18%); mutation testing reveals weak or no correlation between structural coverage and fault finding
VarQEC uses a distinguishability loss as a machine-learning objective to variationally discover resource-efficient encoding circuits optimized for given noise models.
QARMA applies transformer-augmented reinforcement learning to qubit allocation and reuse in modular quantum systems, reporting up to 86% average reduction in inter-core communications versus optimized Qiskit baselines.
QADR decomposes n-qubit VQCs into local sub-circuits to reduce memory from O(2^n) to O(n * 2^{2d+1}) and mitigate barren plateaus, scaling to 2000 features on MNIST and wind turbine diagnostics while matching classical models.
Large qLDPC blocks in distributed quantum computing enable Pauli-based computation to run up to 10x faster than surface codes for optimization algorithms by using spare nodes to bypass serialization bottlenecks.
Noise in lattice-based cryptography fails to erase information permanently, so quantum error correction and learning can extract secrets, making unconditional post-quantum security claims premature.
A topical review unifying statistical mechanics, tensor network, and AI approaches to approximate maximum likelihood decoding for quantum error correction codes.
citing papers explorer
-
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.
-
A Unified Poisson Summation Framework for Generalized Quantum Matrix Transformations
A dual Fourier-PSF and contour-PSF framework resolves the smoothness-sparsity trade-off for efficient quantum simulation of singular and holomorphic matrix functions.
-
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.
-
Efficient and high-performance routing of lattice-surgery paths on three-dimensional lattice
Lattice-surgery scheduling is mapped to 3D path embedding and solved with look-ahead Dijkstra projection, yielding 3.8x lower execution time on quantum phase estimation benchmarks versus greedy scheduling.
-
Probabilistic Condition, Decision and Path Coverage of Circuit-based Quantum Programs
Quantum circuits show high average condition (97.56%) and decision (97.63%) coverage but lower path coverage (71.84%), with probabilistic versions adding confidence levels (averages 88.87%, 88.65%, 37.18%); mutation testing reveals weak or no correlation between structural coverage and fault finding
-
Learning Encodings by Maximizing State Distinguishability: Variational Quantum Error Correction
VarQEC uses a distinguishability loss as a machine-learning objective to variationally discover resource-efficient encoding circuits optimized for given noise models.
-
Learning-Optimized Qubit Mapping and Reuse to Minimize Inter-Core Communication in Modular Quantum Architectures
QARMA applies transformer-augmented reinforcement learning to qubit allocation and reuse in modular quantum systems, reporting up to 86% average reduction in inter-core communications versus optimized Qiskit baselines.
-
Quantum Algorithm for Distributed Reduction of Entanglements (QADR): A Trainable and Simulation-Efficient QML Framework
QADR decomposes n-qubit VQCs into local sub-circuits to reduce memory from O(2^n) to O(n * 2^{2d+1}) and mitigate barren plateaus, scaling to 2000 features on MNIST and wind turbine diagnostics while matching classical models.
-
Space-Time Tradeoffs of Pauli-Based Computation in Distributed qLDPC Architectures
Large qLDPC blocks in distributed quantum computing enable Pauli-based computation to run up to 10x faster than surface codes for optimization algorithms by using spare nodes to bypass serialization bottlenecks.
-
Fundamental Limitations of Post-Quantum Cryptographic Architectures
Noise in lattice-based cryptography fails to erase information permanently, so quantum error correction and learning can extract secrets, making unconditional post-quantum security claims premature.
-
Maximum Likelihood Decoding of Quantum Error Correction Codes
A topical review unifying statistical mechanics, tensor network, and AI approaches to approximate maximum likelihood decoding for quantum error correction codes.