A quantum algorithm discovers and samples rare events with optimal quantum scaling without prior knowledge of the events, yielding quadratic speedup for heavy-tailed systems and polynomial speedup for stationary processes.
V.; Sosnin, S
8 Pith papers cite this work. Polarity classification is still indexing.
8
Pith papers citing it
citation-role summary
background 3
citation-polarity summary
roles
background 3polarities
background 3representative citing papers
Quantum circuits for coherent multilayer neural network inference achieve quadratic to polylogarithmic speedups over classical methods depending on quantum data access models for inputs and weights.
QKAN is a quantum algorithmic framework using block-encodings and QSVT to implement wide-and-shallow networks for quantum learning and compositional state preparation.
NEB-adapted ravine ensembles for QNNs classifying concentratable entanglement outperform naive methods when local-prediction variability is high and reduce costs, with ravines persisting under depth and qubit scaling.
A compact parameterized quantum circuit with geometric phase embedding achieves exact, zero-variance generalization on Z11 arithmetic and S4 group multiplication, a regime the authors call crystallization.
Unitaria is a new open-source Python library that provides a high-level, composable interface for block encodings in quantum computing, enabling automatic circuit generation and classical simulation-based verification.
Precomputes rotation angles classically and adds a magnitude-then-phase procedure to enable complex-valued state preparation on BBQRAM at unchanged O(log²(MN)) query cost with no reversible arithmetic on the QPU.
Review of quantum neural networks on gate-based quantum computers for molecular property prediction and generation in drug discovery.
citing papers explorer
-
Quantum enhanced rare event discovery and sampling
A quantum algorithm discovers and samples rare events with optimal quantum scaling without prior knowledge of the events, yielding quadratic speedup for heavy-tailed systems and polynomial speedup for stationary processes.
-
Accelerating Inference for Multilayer Neural Networks with Quantum Computers
Quantum circuits for coherent multilayer neural network inference achieve quadratic to polylogarithmic speedups over classical methods depending on quantum data access models for inputs and weights.
-
QKAN: quantum Kolmogorov-Arnold networks with applications in machine learning and multivariate state preparation
QKAN is a quantum algorithmic framework using block-encodings and QSVT to implement wide-and-shallow networks for quantum learning and compositional state preparation.
-
Ravines in quantum cost landscapes: opportunities for improved VQA predictions
NEB-adapted ravine ensembles for QNNs classifying concentratable entanglement outperform naive methods when local-prediction variability is high and reduce costs, with ravines persisting under depth and qubit scaling.
-
Universal Quantum Transformer
A compact parameterized quantum circuit with geometric phase embedding achieves exact, zero-variance generalization on Z11 arithmetic and S4 group multiplication, a regime the authors call crystallization.
-
Unitaria: Quantum Linear Algebra via Block Encodings
Unitaria is a new open-source Python library that provides a high-level, composable interface for block encodings in quantum computing, enabling automatic circuit generation and classical simulation-based verification.
-
Efficient Complex-Valued State Preparation on Bucket Brigade QRAM
Precomputes rotation angles classically and adds a magnitude-then-phase procedure to enable complex-valued state preparation on BBQRAM at unchanged O(log²(MN)) query cost with no reversible arithmetic on the QPU.
-
Quantum Machine Learning in Drug Discovery: Applications in Academia and Pharmaceutical Industries
Review of quantum neural networks on gate-based quantum computers for molecular property prediction and generation in drug discovery.