Fragment classification is efficiently learnable by quantum neural networks under suitable conditions but resists known classical dequantization techniques.
Goh, Martin Larocca, Lukasz Cincio, M
4 Pith papers cite this work. Polarity classification is still indexing.
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A necessary condition for variational quantum circuits to reach exact ground states requires matching module projection norms between input and solution, enabling classical O(n^5) exact solvers for problems like MaxCut.
PauliEngine delivers a high-performance C++ backend for Pauli string multiplication, commutators, and symbolic tracking that outperforms existing tools in benchmarks.
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Fragmentation is Efficiently Learnable by Quantum Neural Networks
Fragment classification is efficiently learnable by quantum neural networks under suitable conditions but resists known classical dequantization techniques.
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Reachability Constraints in Variational Quantum Circuits: Optimization within Polynomial Group Module
A necessary condition for variational quantum circuits to reach exact ground states requires matching module projection norms between input and solution, enabling classical O(n^5) exact solvers for problems like MaxCut.
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PauliEngine: High-Performant Symbolic Arithmetic for Quantum Operations
PauliEngine delivers a high-performance C++ backend for Pauli string multiplication, commutators, and symbolic tracking that outperforms existing tools in benchmarks.
- Enabling Lie-Algebraic Classical Simulation beyond Free Fermions