Recommender systems improve quantum Monte Carlo simulations.
problem Efficiency of quantum Monte Carlo methods without sacrificing accuracy.
method Quantum to classical mapping and molecular simulation techniques.
result Classical molecular gas model reproduces quantum distributions efficiently.
New method compresses Green's function data efficiently.
problem Efficiently representing complex correlation functions.
method Intermediate representation (IR) of analytical continuation.
result IR yields significantly compact form of correlation functions.
New Monte Carlo methods use continuous-time Markov processes for big data.
problem Efficient sampling from posterior distributions in big data.
method Piecewise deterministic Markov processes for continuous-time Monte Carlo.
result Continuous-time Monte Carlo methods can target true posterior distributions efficiently.
New algorithms improve MCMC efficiency for complex distributions.
problem High variance and low effective sample size in MCMC samplers.
method Antithetic Riemannian Manifold and Quantum-Inspired Hamiltonian Monte Carlo.
result Improved effective sample size and variance reduction.
Quantum algorithm speeds up financial option pricing.
problem Optimizing stopping times in stochastic processes for finance.
method Combines quantum computing techniques with LSM for optimal stopping.
result Achieves nearly quadratic speedup in runtime.
Quantum computing techniques applied to Monte Carlo simulations in finance.
problem Efficiently simulating quantum algorithms for financial modeling.
method Introduces quantum computing basics, amplitude estimation, and Grover's algorithm for unstructured search.
result Demonstrates quantum approaches to Monte Carlo integration and counting in finance.
Quantum computing offers a quadratic speedup for estimating non-linear functionals.
problem Estimating non-linear functionals of probability distributions.
method Proposes a quantum-inside-quantum Monte Carlo algorithm for a broad class of non-linear estimation problems.
result Achieves a quadratic speedup for non-linear estimation problems, including nested conditional expectations and stochastic optimization.
New method combines Monte Carlo and tensor networks for solving complex equations.
problem Solving high-dimensional partial differential equations efficiently.
method Uses Monte Carlo simulations and tensor train sketching for updates and re-estimations.
result Demonstrates versatility and efficacy in solving specific equations.
Quantum algorithm reduces qubit usage for Monte Carlo simulations.
problem High qubit requirements for Monte Carlo simulations on quantum computers.
method Use of pseudo-random number generator (PRNG) on a quantum circuit.
result Significant reduction in qubit usage without sacrificing quantum speed.
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 algorithms speed up financial model calculations.
problem Computing financial model expectations efficiently.
method Quantum-accelerated multilevel Monte Carlo methods.
result Improved speed-up for financial model calculations.
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.
Researchers propose a method to simulate quantum annealing with non-stoquastic Hamiltonians.
problem Negative sign problem in quantum Monte Carlo simulation of non-stoquastic Hamiltonians.
method Alternative approach using Suzuki--Trotter decomposition to avoid negative sign problem.
result Demonstrated method's validity through application to a simple problem.
Quantum Monte Carlo speeds up option pricing for complex payoff functions.
problem Efficiently pricing options with complex payoff functions using quantum computing.
method Developed a quantum Monte Carlo algorithm for multidimensional Black-Scholes PDEs.
result Proved polynomial computational complexity and speed-up over classical methods.
New method stabilizes quantum Monte Carlo data analysis.
problem Noise in imaginary-time data impacts real-frequency spectra.
method Sparse modeling and regularization technique.
result Stable analytical continuation achieved with minimal bases.
Quantum circuit optimization speeds up financial derivatives pricing.
problem Efficiently pricing financial derivatives on quantum computers.
method Pretraining conditional parameterized circuits for state-dependent functions.
result Quantum circuit implementation of derivatives' payoff function is more efficient.
Quantum methods improve option pricing accuracy.
problem Pricing financial derivatives using Monte Carlo integration.
method Hybrid classical-quantum methods using Fourier series and QML.
result Quantum methods achieve remarkable accuracy in option 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.
Machine learning classifies phases of spin models using improved correlation configurations.
problem Classifying phases of spin models using machine learning.
method Improved correlation configuration estimator applied to machine learning.
result Classifies Berezinskii-Kosterlitz-Thouless transition in quantum XY model.
Quantum computing promises faster finance algorithms.
problem Solving finance problems faster than classical methods.
method Quantum computing applications to finance, including Monte Carlo, portfolio optimization, and machine learning.
result Quantum speedups for finance problems, especially Monte Carlo and portfolio optimization.
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 computing promises faster insurance contract valuation.
problem Computational intensity of insurance contract valuation.
method Investigation of quantum computing's applicability for insurance contracts using Amplitude Estimation.
result Quantum computing can significantly speed up insurance contract valuation.
New quantum algorithm simplifies complex financial derivatives pricing.
problem Complex financial derivatives pricing with high dimensionality.
method Quantum-inspired variational algorithms combined with neural-network quantum states.
result Simplified pricing of European options with many correlated assets.
Quantum algorithms speed up derivative pricing beyond Black-Scholes models.
problem Quantum speedups for derivative pricing beyond Black-Scholes models.
method Utilizing fast-forwardability and quantum Milstein sampler for non-GBM models, and improved numerical integration for GBM and CIR models.
result Quadratic speedups for derivative pricing in practical models like CIR and Heston's model.
AFT combines AIS, SMC, and NFs for better Monte Carlo estimates.
problem Estimating normalizing constants of complex probability distributions.
method Annealed Flow Transport (AFT) integrates AIS, SMC, and normalizing flows.
result AFT improves Monte Carlo estimates of normalizing constants and expectations.
A new method called MCLMC avoids dissipation in sampling from canonical distributions.
problem Sampling from canonical distributions without dissipation.
method Microcanonical Langevin Monte Carlo (MCLMC) as a dissipation-free system of SDE.
result MCLMC converges faster than HMC for lattice φ^4 models.
Quantum computing speeds up option pricing for multiple assets.
problem High-dimensional integration bottleneck in option pricing.
method Calibrated marginal distributions, Gaussian copula, QAMC with QAE.
result QAMC reduces integration queries by 10-100 times for similar precision.
Generative neural samplers estimate quantum spin system properties.
problem Estimating observables for quantum spin systems.
method Autoregressive models using Suzuki-Trotter transformation.
result Results for energy, specific heat, and susceptibility are in good agreement with Monte Carlo methods.
QHMC improves HMC for sampling from complex distributions.
problem Inefficiency of HMC in sampling from spiky and multimodal distributions.
method Proposes QHMC, a quantum-inspired version of HMC with a random mass matrix.
result QHMC and QSGNHT achieve more stable and accurate sampling results.
Quantum algorithm speeds up nested expectation estimation by nearly quadratically.
problem Estimating repeatedly nested expectations with quantum computing.
method Proposes a quantum algorithm achieving nearly quadratic speedup over classical methods.
result Achieves nearly quadratic speedup for RNEs, up to logarithmic factors.
Quantum methods model uncertain volatility in financial markets.
problem Modeling financial asset prices with uncertain volatility.
method Quantum stochastic calculus with unitary and non-unitary time evolution.
result Different volatility levels encoded in quantum states, leading to varied market price evolutions.
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.
New method samples from piecewise smooth distributions using Hamiltonian Monte Carlo.
problem Sampling from distributions with discontinuous gradients.
method Generalized Randomized Hamiltonian Monte Carlo (GRHMC) for piecewise smooth targets.
result GRHMC processes sample from piecewise smooth target distributions with the desired distribution as the invariant distribution.
The book covers scalable MCMC methods for Bayesian learning.
problem Scalability issues in Bayesian learning with large datasets.
method Advanced MCMC algorithms, including stochastic gradient, non-reversible, and continuous time methods.
result Substantial advances in practical and theoretical Bayesian computation.
QBC uses quantum computers to speed up Bayesian computation.
problem Exponential speed-up in Bayesian computation.
method Quantum von Neumann measurement for simulating ML algorithms.
result Quantum versions of regression, Gaussian processes, and SGD.
Spin-opstrings from QMC simulations enable ML of quantum phases.
problem Capturing and predicting quantum phase transitions using ML.
method Spin-opstrings derived from QMC simulations used as ML input.
result Spin-opstrings accurately predict quantum phase transitions.
Study evaluates discretized arbitrage strategies in fractional financial markets.
problem Serial correlation in financial markets with fractional Brownian motion.
method Revisit and transfer Shiryaev and Salopek's strategies to a real-world setting, distretizing dynamics and introducing transaction costs.
result Both strategies are promising with respect to terminal portfolio values and loss probabilities.
C-qGAN learns multi-modal distributions efficiently.
problem Learning multi-modal distributions efficiently.
method Conditional Quantum Generative Adversarial Network (C-qGAN) within quantum circuits.
result C-qGAN outperforms current state preparation methods in efficiency.
In most sampling algorithms, including Hamiltonian Monte Carlo, transition rates between states correspond to the probability of making a transition in a single time step, and are constrained to be less than or equal to 1. We derive a Hamiltonian Monte Carlo algorithm using a continuous time Markov jump process, and ar…
Quantum computing speeds up interest rate derivative pricing using LMM.
problem Challenges in pricing interest rate derivatives, especially caps.
method Hybrid classical-quantum approach using quantum amplitude estimation.
result Quantum computing improves convergence in pricing interest rate derivatives.
fintech-kMC simulates financial platforms for AI/ML model validation.
problem Validation of AI/ML models in real-world financial applications.
method Agent-based model with kinetic Monte Carlo engine.
result Generates realistic synthetic data for testing AI/ML models.
Deep learning wave function improves quantum chemistry calculations.
problem Solving the electronic Schrödinger equation for complex molecules is computationally expensive.
method PauliNet, a deep learning wave function ansatz that incorporates physics and is trained with VMC.
result PauliNet achieves nearly exact solutions and outperforms other methods for various molecules.
Efficiently infers coupled hidden Markov models with noisy discrete observations.
problem Intractable inference for coupled continuous-time Markov chains with discrete observations.
method Latent Interacting Particle Systems, look-ahead functions, twisted Sequential Monte Carlo sampling.
result Demonstrated effectiveness on latent SIRS model and wildfire spread dynamics.
CHMC improves HMC efficiency for multimodal distributions.
problem Slow convergence of HMC in multimodal distributions.
method Integrates a counterdiabatic term to optimize Hamiltonian changes.
result CHMC achieves efficient sampling from challenging distributions.
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.
DVAEs speed up calorimeter simulation for LHC data.
problem Slow calorimeter simulation in LHC experiments.
method Discrete Variational Autoencoders (DVAEs).
result Significantly faster calorimeter shower simulation.
Quantum algorithm for pricing European call options.
problem Accurate valuation of financial derivatives, especially for complex models and options.
method Transforms classical FFT into quantum QFT for pricing European call options.
result Quantum algorithm outperforms classical Monte Carlo simulation in NISQ era.
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.