A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
QAOA matches classical tensor power iteration in spiked tensor model recovery.
Paper proposes machine learning to optimize QAOA for combinatorial problems.
Hybrid QAOA approach optimizes portfolios with strict constraints, outperforming classical methods.
This study optimizes currency arbitrage using quantum computing methods.
Hybrid classical-quantum framework optimizes portfolio rebalancing with reduced transaction costs.
This tutorial introduces quantum computing for financial portfolio optimization.
Quantum algorithm improves portfolio optimization quality measured by Wasserstein distance.
Hybrid quantum algorithm tackles binary optimization problems with multiple constraints.
Quantum computing exploits basic quantum phenomena such as state superposition and entanglement to perform computations. The Quantum Approximate Optimization Algorithm (QAOA) is arguably one of the leading quantum algorithms that can outperform classical state-of-the-art methods in the near term. QAOA is a hybrid quant…
Hybrid LLM and quantum optimization improve CSA collateral management by 9-10%.
Quantum algorithms for CVaR portfolio optimization face trade-offs between hardware coherence and expressibility.
Proposes PO-QA framework to optimize portfolios using quantum algorithms.
Quantum machine learning improves pulsar classification in radio astronomy.
Quantum computing aids in optimizing currency reserves for central banks.
Develops quantum circuits for faster learning with symmetry considerations.
A quantum framework optimizes collateral allocation for derivatives.