QAOA matches classical tensor power iteration in spiked tensor model recovery.
problem Statistical estimation in spiked tensor model with computational gap.
method Analysis of QAOA performance on spiked tensor model.
result QAOA weak recovery threshold matches tensor power iteration.
Hybrid QAOA approach optimizes portfolios with strict constraints, outperforming classical methods.
problem Combinatorial optimization under strict cardinality constraints in portfolio management.
method Constraint-preserving QAOA with XY-mixers and Trotterized initialization.
result QAOA achieves a Sharpe Ratio of 1.81, significantly outperforming classical methods.
Quantum computing is a computational paradigm with the potential to outperform classical methods for a variety of problems. Proposed recently, the Quantum Approximate Optimization Algorithm (QAOA) is considered as one of the leading candidates for demonstrating quantum advantage in the near term. QAOA is a variational …
This study optimizes currency arbitrage using quantum computing methods.
problem Optimizing profitable trading routes in currency markets.
method Quantum Annealing, QAOA, and Constraint Mapping.
result Quantum computing techniques enhance the identification of optimal arbitrage paths.
Hybrid classical-quantum framework optimizes portfolio rebalancing with reduced transaction costs.
problem Optimizing portfolio rebalancing with reduced transaction costs and lookahead bias.
method Combining Ledoit-Wolf shrinkage covariance estimation, hierarchical correlation clustering, entropy-regularised Genetic Algorithm, minimum-variance and equal-weight benchmarks, QUBO formulation, and QAOA for solving the combinatorial optimisation problem.
result GA + QAOA strategy outperforms classical methods with reduced rebalances and transaction costs.
This tutorial introduces quantum computing for financial portfolio optimization.
problem Combinatorial portfolio optimization in financial markets.
method Application of Quantum Approximate Optimization Algorithm (QAOA) to portfolio optimization.
result Quality of combinatorial portfolio optimization solutions using QAOA on quantum simulator.
Quantum algorithm improves portfolio optimization quality measured by Wasserstein distance.
problem Optimizing financial asset portfolios using quantum computing.
method Used Quantum Approximate Optimization Algorithm (QAOA) and Normalized and Complementary Wasserstein Distance (η) to benchmark solution quality. result Solution quality increases with QAOA circuit depth p and is influenced by the portfolio budget B. Hybrid quantum algorithm tackles binary optimization problems with multiple constraints.
problem Efficiently solving binary optimization problems with multiple constraints using quantum algorithms.
method Combines QAOA with penalty dephasing and Zeno effect for non-Ising constraints.
result Significant improvement in solving practical aircraft loading problems.
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%.
problem Finance-native collateral optimization under ISDA CSAs with legal constraints.
method Hybrid pipeline combining LLM, quantum-inspired exploration, and CP-SAT.
result Improves a strong classical baseline by 9.1-10.7% across different scenarios.
Quantum algorithms for CVaR portfolio optimization face trade-offs between hardware coherence and expressibility.
problem Quantum algorithmic resilience for CVaR portfolio optimization
method WS-QAOA vs. HE-VQNN
result WS-QAOA provides exact theoretical mapping but suffers from hardware decoherence, while HE-VQNN preserves hardware coherence but lacks expressibility.
Proposes PO-QA framework to optimize portfolios using quantum algorithms.
problem Optimizing investment portfolios with reduced risk and increased gains.
method Develops a scalable quantum framework (PO-QA) to investigate quantum algorithm parameters.
result Identifies efficient quantum circuit configurations for portfolio optimization.
Quantum machine learning improves pulsar classification in radio astronomy.
problem Improving classification of pulsars in radio astronomy.
method Used a Born machine (quantum neural network) with a single-qubit architecture.
result Comparable accuracies to classical machine learning methods achieved.
Quantum computing aids in optimizing currency reserves for central banks.
problem Optimizing currency composition in foreign exchange reserves.
method Comparison of quantum and classical algorithms for portfolio optimization.
result Quantum algorithms outperform classical methods in currency optimization.
Develops quantum circuits for faster learning with symmetry considerations.
problem Speeding up learning quantum states with symmetry considerations.
method Utilizes Okounkov-Vershik approach and Young-Jucys-Murphy elements to develop Sn-equivariant convolutional quantum circuits. result Proves Sn-CQA generates any unitary in any given Sn irrep sector, universal for SU(d) symmetry. A quantum framework optimizes collateral allocation for derivatives.
problem Legal constraints and operational rules in collateral allocation for derivatives.
method Certified higher-order quantum framework that normalizes margin requirements and builds a bounded neighborhood of actions.
result Quantum framework improves certified sample quality compared to classical methods.