Study combines quantum and classical deep learning for better credit risk assessment.
problem Enhancing accuracy and efficiency in credit risk evaluation.
method Hybrid Quantum-Classical Deep Neural Network for Row-Type Dependent Predictive Analysis.
result Proposed framework enhances predictive models for different loan categories.
Quantum SVT reduces credit risk analysis costs.
problem Efficiently estimating credit risk metrics using quantum computing.
method Quantum Singular Value Transformation (QSVT) to reduce state preparation costs.
result Significant reduction in implementation costs for quantum credit risk analysis.
Quantum algorithms improve VaR and CVaR estimation for financial derivatives.
problem Quantum advantage in financial risk analysis of derivatives.
method Two quantum algorithms: QSP and QSP-based approach.
result QSP-based approach requires fewer quantum resources for the same accuracy.
A new portfolio method using quantum mechanics improves risk diversification.
problem Improving risk-based portfolio construction methods for multi-asset portfolios.
method Schrödinger principal component analysis applied to extract common factors from asset fluctuations.
result The proposed method outperforms conventional risk parity and other risk diversification methods.
Modern quantitative risk management relies on an adequate modeling of the tail dependence and a possibly accurate quantification of risk measures, like Value at Risk (VaR), at high confidence levels like 1 in 100 or even 1 in 2000. Quantum computing makes such a quantification quadratically more efficient than the Mont…
Quantum computing offers new solutions for financial optimization, pricing, risk, and security.
problem Core financial bottlenecks in combinatorial search, expectation estimation, and rare-event analysis.
method Identify bottlenecks, specify quantum primitives, compare with classical benchmarks, assess under constraints.
result Strongest near-term case for quantum finance in hybrid workflows, constrained search, and amplitude-estimation.
Quantum Portfolios of quantum algorithms encoded on qbits have recently been reported. In this paper a discussion of the continuous variables version of quantum portfolios is presented. A risk neutral valuation model for options dependent on the measured values of the observables, analogous to the traditional Black-Sch…
Quantum method speeds up risk estimation for insurance tail risks.
problem Sample-sparsity in classical Monte Carlo methods for tail risk pricing.
method Quantum Amplitude Estimation (QAE) with Grover amplification.
result Quantum method achieves convergence approaching order reciprocal N, enabling high-resolution tail estimation within practical budgets.
Quantum algorithm reduces CVA risk-neutral expectation estimation costs.
problem Reducing Monte Carlo sampling cost for CVA on real quantum hardware.
method Noise-aware quantum workflow combining market calibration, discretisation, and oracle construction.
result CABIQAE achieves lower classical post-processing runtime and more effective error exploitation.
Quantum computing improves copula-based risk aggregation models.
problem Improving quantum models for copula-based risk aggregation.
method Applied Quantum Circuit Born Machine (QCBM) to trapped ion quantum computers, introduced annealing-inspired strategy.
result Quantum models yield comparable or better predictions in risk aggregation tasks.
Quantum algorithms speed up financial portfolio valuation.
problem Efficiently pricing and valuing complex financial portfolios.
method Quantum Monte Carlo (QMC) algorithms enhanced with quantum amplitude estimation.
result Quantum algorithms significantly accelerate CVA and portfolio pricing.
Quantum RNG improves financial risk metrics estimation.
problem Estimating financial risk metrics with high precision.
method Quantum-Enhanced Monte Carlo using QRNG.
result Improved accuracy in VaR and CVaR estimation.
VQC-MLPNet combines quantum and classical elements for scalable quantum machine learning.
problem Challenges in expressivity, trainability, and noise resilience of VQCs.
method Hybrid architecture with a VQC generating weights for a classical MLP during training.
result Improved expressivity, trainability, and robustness compared to standalone quantum or hybrid approaches.
Quantum method calculates risk contributions in credit portfolios efficiently.
problem Quantifying risk concentration in subgroups of a credit portfolio.
method Quantum algorithm for simultaneous estimation of multiple expected values.
result Quantum method scales better than classical methods for finely divided subgroups.
Quantum computing improves Monte Carlo option pricing for complex derivatives.
problem Complex financial derivatives require extensive computations in high-dimensional spaces.
method Developed a quantum algorithm for simulating many potential asset paths in parallel.
result Quantum algorithm provides highly accurate option pricing and risk analysis.
Quantum MC simulations generate financial risk distributions efficiently.
problem High computational cost in traditional Monte Carlo simulations.
method Integrates quantum amplitude estimation with stochastic models for equity, rate, and credit risk factors.
result Quantum advantage in scenario generation for financial risk analytics.
Quantum algorithms accelerate financial risk computation.
problem Accelerating the computation of financial market risk.
method Quantum gradient estimation algorithms for market sensitivities.
result Significant reduction in resource requirements for financial quantum advantage.
Quantum crypto-economics models price risks in blockchain technology.
problem Quantum technology's potential to undermine blockchain security.
method Building financial models to price quantum risk in blockchain scenarios.
result Quantum crypto-economics models can assess and price quantum risks in blockchain.
Quantum algorithms for financial derivatives and credit risk.
problem Estimating credit risk and option pricing in realistic financial models.
method Developed a regime switching volatility model for financial markets, using a Markov chain to determine volatility parameters.
result Quantum algorithms can be applied to realistic financial models, bringing quantum computing closer to practical applications.
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.
Quantum reservoirs risk bounds are analyzed using Rademacher complexity.
problem Bounding generalization errors of quantum reservoirs.
method Using Rademacher complexity, specific bounds are derived for quantum reservoir classes.
result Risk bounds converge with increasing training samples and qubits.
Quantum computing offers financial industry new optimization and risk management tools.
problem Traditional computing limits financial industry's problem-solving capabilities.
method Structured review of quantum computing platforms, algorithms, and use cases.
result Quantum computing can enhance financial industry applications like optimization and risk management.
Quantum computing speeds up analysis of financial stochastic processes.
problem Challenging simulation and analysis of continuous time stochastic processes.
method Established a quantum framework for efficient state preparation and information extraction.
result Extraction of path-dependent and history-sensitive information from stochastic processes efficiently.
Deep quantum neural networks applied to finance for efficient risk management.
problem Efficiently solving numerical problems in finance, especially risk management.
method Application of deep quantum neural networks to finance, focusing on implied volatilities, option prices, and Greeks.
result Deep quantum neural networks can compute Greeks analytically and efficiently solve financial numerical problems.
The 2008 mortgage crisis is an example of an extreme event. Extreme value theory tries to estimate such tail risks. Modern finance practitioners prefer Expected Shortfall based risk metrics (which capture tail risk) over traditional approaches like volatility or even Value-at-Risk. This paper provides a quantum anneali…
Quantum machine learning boosts financial forecasting accuracy.
problem Churn prediction and credit risk assessment in finance.
method Used quantum and classical Determinantal Point Processes for churn prediction, and quantum neural networks for credit risk assessment.
result Significant improvement in precision for churn prediction (6% increase). Quantum models match classical performance with fewer parameters.
Financial volatility risk and its relation to a business cycle-related intrinsic time is addressed through a multiple round evolutionary quantum game equilibrium leading to turbulence and multifractal signatures in the financial returns and in the risk dynamics. The model is simulated and the results are compared with …
Hybrid QML model improves recovery rate prediction accuracy.
problem Complex nonlinear dependencies, high-dimensional feature spaces, and limited sample sizes in recovery rate forecasting.
method Hybrid Quantum Machine Learning (QML) with Amplitude Encoding, leveraging PQC and qubit data compression.
result Significantly lower RMSE (0.228) compared to classical models.
Quantum computers can optimize foreign exchange reserves management.
problem Optimizing foreign exchange reserves management using quantum computing.
method Demonstrated through quantum Monte Carlo risk measurement and quantum algorithms for portfolio optimization.
result Quantum computers can theoretically optimize FX reserves management in the future.
The key cryptographic protocols used to secure the internet and financial transactions of today are all susceptible to attack by the development of a sufficiently large quantum computer. One particular area at risk are cryptocurrencies, a market currently worth over 150 billion USD. We investigate the risk of Bitcoin, …
Investigates quantum vs classical portfolio optimization of 60 stocks.
problem Optimizing risk vs return portfolios of 60 stocks using quantum and classical methods.
method Classical and quantum annealing approaches applied to historical data.
result Quantum and classical methods yield similar optimal portfolios.
Quantum-inspired tensor network speeds up financial risk assessment.
problem Efficiently pricing multi-asset derivatives in finance.
method Tensor network algorithms for multi-asset options pricing.
result Tensor network approach yields several orders of magnitude speedup.
Quantum assets are priced using a new theorem, extending classical asset pricing.
problem Quantum properties in financial markets and assets.
method Developed a new definition of arbitrage for quantum assets and proved a quantum version of the first fundamental theorem of asset pricing.
result There exists a risk-free density operator under which all quantum assets are martingales if no arbitrage exists.
Quantum method improves CVaR evaluation under correlated fields.
problem Accurately evaluating CVaR in high-dimensional, correlated material uncertainty.
method Quantum-enhanced inference framework using stabilized IQAE.
result Quantum method achieves lower oracle complexity than classical methods.
Quantum model outperforms classical in training but underperforms in real-world metrics.
problem Mismatch between proxy reward signals and true investment objectives in financial domains.
method Hybrid quantum-classical reinforcement learning framework with automated feature engineering.
result Quantum models achieve higher training rewards but underperform in real-world metrics.
Quantum computing speeds up CDO pricing models.
problem Efficiently pricing complex financial products like CDOs.
method Implemented quantum circuits for Gaussian and Normal Inverse Gaussian copula models, using quantum amplitude estimation.
result Quantum computing can significantly speed up CDO pricing compared to Monte Carlo simulations.
Quantum ML promises faster data analysis but faces trainability challenges.
problem Challenges in training quantum machine learning models.
method Review of current methods and applications of quantum neural networks and quantum deep learning.
result Opportunities for quantum advantage in quantum machine learning.
Study uses big data to analyze quantum invariants.
problem Investigate structural properties of Jones polynomial.
method Exploratory and topological data analysis, including coloring, rank increase, categorification.
result Contrasts behavior of Jones polynomial under various enhancements.
Quantum optimization for portfolios with risk and diversification constraints.
problem Implementing complex constraints in portfolio optimization for financial applications.
method Transformed portfolio optimization into a quadratic binary optimization problem suitable for quantum annealers.
result Demonstrated practical implementation of daily constraints in real data using quantum processors.
QNA uses quantum-inspired density operators to diagnose market dependence and structural risk.
problem Lack of unified operator representation for market dependence and structural risk diagnostics.
method Quantum Network of Assets (QNA) framework using density operators.
result QNA entropy remains strongly related to covariance spectral entropy but becomes distinct with multi-feature rolling trajectories.
Eigen component analysis combines quantum mechanics with machine learning for efficient data analysis.
problem Efficiently extracting linearly separable components from complex data.
method Eigen component analysis (ECA) incorporates quantum mechanics principles into linear learning models.
result ECA outperforms classical linear models and can be integrated with deep neural networks.
Quantum computing techniques improve graph analysis and community detection.
problem Analyzing large graphs efficiently and accurately.
method Used quantum annealing and quantum gate computers for community detection and regularity checking.
result Demonstrated the effectiveness of quantum computing in solving complex graph problems.
Hybrid QNN-LSTM predicts financial stock market trends using quantum computing.
problem Complex temporal dependencies and market fluctuations in financial time-series forecasting.
method Custom QNN regressor with hybrid optimization strategies.
result Hybrid models integrate quantum computing into financial forecasting workflows.
Uncertainty in economics still poses some fundamental problems illustrated, e.g., by the Allais and Ellsberg paradoxes. To overcome these difficulties, economists have introduced an interesting distinction between 'risk' and 'ambiguity' depending on the existence of a (classical Kolmogorovian) probabilistic structure m…
Quantum tech speeds up financial risk assessment.
problem Improving credit valuation adjustments using quantum mechanics.
method Developed a quantum algorithm using Bayesian quantum amplitude estimation and engineered likelihood functions.
result Significant speedup in quantum computations for CVA over classical methods.
It is known that quantum computers can speed up Monte Carlo simulation compared to classical counterparts. There are already some proposals of application of the quantum algorithm to practical problems, including quantitative finance. In many problems in finance to which Monte Carlo simulation is applied, many random n…
Quantum speedup for Monte Carlo integration reduces integrand calls.
problem Reducing the number of calls to the integrand subroutine in high-dimensional Monte Carlo integration.
method Combining nested quantum amplitude estimation with pseudorandom numbers for separable integrands.
result Significant reduction in the number of integrand calls for high-dimensional integration.
Quantum computing optimizes ESG portfolios efficiently.
problem Optimizing investment portfolios with risk, return, and ESG considerations.
method Formulated discrete Markowitz portfolio theory (DMPT) for quantum annealers, incorporating ESG ratings.
result Discrete portfolios converge to continuous solutions as budgets increase, outperforming traditional methods.