Any low-success-probability LPN solver can be transformed into a high-success-probability solver on scaled parameters LPN with noise and dimension divided by k = Θ(1/δ log 1/ε).
Proceedings of the Thirty-Seventh Annual ACM Symposium on Theory of Computing , pages =
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Representing binary variables as complex phases on the unit circle yields an implicit regularization that improves ground-state recovery in QUBO, sparse coding, and planted Ising models.
Multi-agent DRL framework shows dynamic incentives and pricing can cut commuter costs ~20%, emissions ~10%, and double public transport profit in simulated morning peak scenarios.
A review describing the Decoded Quantum Interferometry algorithm for quantum speedups in max-LINSAT optimization, with claimed superpolynomial advantage in the OPI problem.
citing papers explorer
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Hardness Amplification for (Sparse) LPN
Any low-success-probability LPN solver can be transformed into a high-success-probability solver on scaled parameters LPN with noise and dimension divided by k = Θ(1/δ log 1/ε).
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Implicit Binarization via Complex Phase Dynamics in Combinatorial Optimization
Representing binary variables as complex phases on the unit circle yields an implicit regularization that improves ground-state recovery in QUBO, sparse coding, and planted Ising models.
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Dynamic multi-agent deep reinforcement learning-based pricing and incentivization approach in multimodal transportation networks
Multi-agent DRL framework shows dynamic incentives and pricing can cut commuter costs ~20%, emissions ~10%, and double public transport profit in simulated morning peak scenarios.
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Quantum Decoding Algorithms: Quantum Speedups in Optimization
A review describing the Decoded Quantum Interferometry algorithm for quantum speedups in max-LINSAT optimization, with claimed superpolynomial advantage in the OPI problem.