Study compares quantum and classical ML in crypto trading, finding hybrid models outperform.
problem Comparing quantum and classical machine learning in crypto trading strategies.
method Backtesting 10 models across multiple crypto assets using classical ML, quantum ML, hybrid models, and transformer models.
result Hybrid quantum models achieve superior performance with 13.99% return and 1.76 Sharpe ratio.
Using a supergeometric interpretation of field functionals, we show that for a class of classical field models used for realistic quantum field theoretic models, an infinite-dimensional supermanifold (smf) of classical solutions in Minkowski space can be constructed. That is, we show that the smf of smooth Cauchy data …
Quantum computers outperform classical methods in density modeling.
problem Density modeling with quantum computers.
method Quantum-classical separation for density modeling.
result Quantum computers offer a super-polynomial advantage over classical algorithms for density modeling.
Improves diffusion model performance and efficiency through classical search.
problem Tackles inference-time control in diffusion models.
method Proposes a framework combining local and global search for efficient navigation.
result Significant gains in performance and efficiency across various domains.
Using a supergeometric interpretation of field functionals developed in previous papers, we show that for quite a large class of systems of nonlinear field equations with anticommuting fields, infinite-dimensional supermanifolds (smf) of classical solutions can be constructed. Such systems arise in classical field mode…
Quantum oracles help identify counterfactuals better than classical ones.
problem Identifying unknown causal parameters in causal models.
method Using quantum oracles to query and identify all causal parameters and counterfactuals.
result Quantum oracles enable identification of all two-way joint counterfactuals and tighter bounds on higher-order counterfactuals.
Classical scaling is shown to be optimal under various noisy conditions.
problem Consistency of classical scaling under general noise conditions.
method Established using finite fourth moments of noise, derived convergence rates, and matching minimax lower bounds.
result Classical scaling achieves minimax optimality in recovering true configuration from noisy dissimilarities.
This work proposes efficient classical training protocols for IQP circuits to train quantum generative models.
problem Training quantum generative models on industrially relevant probability distributions is challenging due to high computational cost.
method Developed protocols for classical training of IQP circuits, which are hard to sample but have efficient gradient computation.
result Classically trained IQP circuits can efficiently sample from target probability distributions, demonstrating practical quantum advantage.
Model financial markets using open quantum systems to understand market imperfections.
problem Understanding market imperfections through imperfect trading mechanisms.
method Using open quantum systems to represent financial markets, characterizing orbits, and analyzing reduced density matrices.
result Non-classical modes of time evolution can incorporate factors like illiquid trades and imperfect trading mechanisms.
We propose a novel quantum model for the restricted Boltzmann machine (RBM), in which the visible units remain classical whereas the hidden units are quantized as noninteracting fermions. The free motion of the fermions is parametrically coupled to the classical signal of the visible units. This model possesses a quant…
Hybrid quantum-classical RL model solves standard benchmark tasks and proves quantum advantage.
problem Challenges in reinforcement learning, especially in solving standard benchmarking tasks.
method Parametrized quantum circuits in a hybrid quantum-classical RL model.
result Demonstrates quantum advantage in solving standard benchmarking tasks and intractable classical problems.
Unified framework for learning quantum models from limited measurements.
problem Sample complexity and measurement shots in classical learning of quantum models.
method Unified learning framework considering probabilistic quantum measurements.
result Asymmetrical effects and interplay of sample size and measurement shots on learning performance.
We discuss of the conceptual difficulties connected with the anticommutativity of classical fermion fields, and we argue that the "space" of all classical configurations of a model with such fields should be described as an infinite-dimensional supermanifold M. We discuss the two main approaches to supermanifolds, and …
Study compares LLMs vs classical models for financial sentiment analysis.
problem Improving sentiment analysis in financial market news.
method Comparative analysis of LLMs and classical models.
result LLMs outperform classical models in sentiment analysis of financial news.
Study benchmarks classical models over quantum in DeFi yield prediction.
problem Accurate yield and performance forecasting for DeFi liquidity allocation.
method Benchmarked six models on Curve Finance pools' historical data.
result Classical models, especially XGBoost, outperform quantum models.
LLMs struggle to optimize hyperparameters efficiently, but hybrid methods can improve performance.
problem Optimizing hyperparameters of small language models using LLMs.
method Comparison of classical HPO algorithms and LLM-based methods, introducing Centaur hybrid approach.
result Hybrid Centaur approach achieves best results, outperforming classical and pure LLM methods.
Study on non-classical generating sets in Fuchsian Schottky groups.
problem Estimating non-classical Schottky structure in discrete subgroups.
method Investigated Fuchsian Schottky groups with non-classical generating sets using Möbius transformations.
result Derived two non-trivial examples of Fuchsian Schottky groups with non-classical generating sets.
Classical time series models forecast Bitcoin prices and volatility accurately.
problem Forecasting Bitcoin prices and volatility using classical models.
method ARIMA, SARIMA, GARCH, and EGARCH models were trained and tested on Bitcoin price data.
result ARIMA models performed best for short-term price dynamics, while EGARCH models were best for volatility.
Enhances financial risk quantification in classical models.
problem Risk quantification in classical finance models.
method Nested risk measures, limiting behavior analysis.
result Uniqueness of risk-averse limit in classical models.
Given a classical channel---a stochastic map from inputs to outputs---the input can often be transformed to an intermediate variable that is informationally smaller than the input. The new channel accurately simulates the original but at a smaller transmission rate. Here, we examine this procedure when the intermediate…
Quantum models avoiding barren plateaus can also be efficiently simulated classically.
problem Understanding the limitations of barren plateaus in quantum computing.
method Analyzing commonly used models and their ability to be simulated classically.
result Many quantum models with barren plateau-free landscapes can also be efficiently simulated classically.
We study a sigma-model with target space the flag manifold U(3)/U(1)^3. A peculiarity of the model is that the complex structure on the target space enters explicitly in the action. We describe the classical solutions of the model for the case when the worldsheet is a sphere CP^1.
Quantum CNNs can be efficiently simulated classically on simple datasets.
problem Quantum CNNs' success on simple datasets is due to low-bodyness measurements.
method Classical simulation using Pauli shadows on low-bodyness subspace.
result Quantum CNNs' action on low-bodyness subspace can be efficiently simulated classically.
Classical clients can verify quantum learning tasks efficiently.
problem Making quantum learning accessible to classical clients.
method Developed a framework for classical verification of quantum learning.
result Quantum learning tasks can be efficiently verified by classical verifiers.
A large collection of time series poses significant challenges for classical and neural forecasting approaches. Classical time series models fail to fit data well and to scale to large problems, but succeed at providing uncertainty estimates. The converse is true for deep neural networks. In this paper, we propose a hy…
These notes grew out of a lecture course on mathematical methods of classical physics for students of mathematics and mathematical physics at the master's level. Also, physicists with a strong interest in mathematics may find this text useful as a resource complementary to existing textbooks on classical physics. Topic…
Novel neural models have been proposed in recent years for learning under domain shift. Most models, however, only evaluate on a single task, on proprietary datasets, or compare to weak baselines, which makes comparison of models difficult. In this paper, we re-evaluate classic general-purpose bootstrapping approaches …
Quantum neural network and tensor network models outperform classical models in Japanese stock market predictions.
problem Improving stock return predictions using quantum and quantum-inspired machine learning.
method Evaluation of quantum neural network and tensor network models against classical models like linear and neural networks.
result Tensor network model outperforms classical models in Japanese stock market, including linear and neural network models.
Paper benchmarks quantum neural networks against classical ones for binary classification tasks.
problem Comparing quantum neural networks with classical ones for binary classification.
method Evaluated with two toy examples, focusing on model complexity and training data size.
result EQNN and QNN outperform ENN and DNN for smaller parameter sets and training data samples.
Quantum circuits reveal pathways to dequantization in machine learning models.
problem Navigating the complex landscape of quantum machine learning models and algorithms.
method Introducing a framework connecting quantum circuit structure to function representability.
result Fundamental properties of quantum circuits determine classical simulability of models.
In this paper we construct the quantum group, at roots of unity, of abelian Chern-Simons theory. We then use it to model classical theta functions and the actions of the Heisenberg and modular groups on them.
Quantum mechanics is inherently probabilistic in light of Born's rule. Using quantum circuits as probabilistic generative models for classical data exploits their superior expressibility and efficient direct sampling ability. However, training of quantum circuits can be more challenging compared to classical neural net…
Enhances quantum computing for symmetrical systems, proving a new class of problems.
problem Proving the efficiency of a new quantum computing model for symmetrical systems.
method Introducing equivariant convolutional quantum algorithms tailored for SU(d) symmetries.
result Demonstrates a problem that can be solved efficiently on a new quantum model, suggesting it's not classically simulatable.
Paper compares neural networks and classical statistics for dementia prediction, highlighting interpretability of classical methods.
problem Tackles the challenge of interpreting risk factors for dementia prediction.
method Compares neural networks and classical statistics for dementia prediction.
result Classical statistics provide clearer interpretation of risk factors compared to neural networks.
Quantum walks model financial returns with flexibility and asymmetry.
problem Accurate modeling of financial asset price dynamics.
method Discrete-time quantum walks to model asset price evolution.
result Quantum walk models can generate asymmetric return distributions and higher probabilities for extreme events.
We consider the classical "Serrin symmetry result" for the overdetermined boundary value problem related to the equation Δu=−1 in a model manifold of non-negative Ricci curvature. Using an extension of the Weinberger classical argument we prove a Euclidean symmetry result under a suitable "compatibility" assumption b…
We analyze complexity of financial (and general economic) processes by comparing classical and quantum-like models for randomness. Our analysis implies that it might be that a quantum-like probabilistic description is more natural for financial market than the classical one. A part of our analysis is devoted to study t…
A new hybrid framework reduces quantum runtime and noise effects.
problem Challenges in deploying deep QFMs on real quantum hardware.
method Iterative Quantum Feature Maps (IQFMs) combining shallow QFMs and classical augmentation weights.
result Numerical experiments show IQFMs outperforming quantum convolutional neural networks.
Quantum effects improve stock option pricing model.
problem Persistent discrepancies between classical Black-Scholes model and actual stock prices.
method Introduced an additional pseudo-Wiener process to represent non-classical information.
result The norm of a complex quantity compensates for price discrepancies, providing market evidence for non-classical processes.
Quantum models improve data generation from noisy quantum processors.
problem Creating complex probability distributions from limited data.
method Quantum-noise-driven generative diffusion models.
result Quantum noise can be harnessed to generate more complex distributions efficiently.
This paper asks, "Do classics exist in megaproject management?" We identify three types of classic texts: conventional, Kuhnian, and citation classics. We find that the answer to our question depends on the definition of "classic" employed. First, "citation classics" do exist in megaproject management, and they perform…
In this paper we present a new framework for time-series modeling that combines the best of traditional statistical models and neural networks. We focus on time-series with long-range dependencies, needed for monitoring fine granularity data (e.g. minutes, seconds, milliseconds), prevalent in operational use-cases. Tra…
Quantum computing speeds up linear regression training.
problem Reducing training time for machine learning models.
method Formulated regression problem as QUBO, used D-Wave 2000Q for adiabatic optimization.
result Quantum approach achieves up to 2.8x speedup on larger datasets.
In this paper we compare two classical one-factor diffusion models which are used to model the term structure of interest rates. One of them is based on the Wiener-Bachelier process while the second one is based on the Ornstein-Uhlenbeck process. We show essential differences between the prices of European call options…
Quantum machine learning: Adiabatic quantum SVM outperforms classical methods.
problem Training support vector machines efficiently on large datasets.
method Adiabatic quantum computing for SVM training.
result Quantum approach outperforms classical methods in accuracy and scalability.
The issue of constructing a risk minimizing hedge under an additional almost-surely type constraint on the shortfall profile is examined. Several classical risk minimizing problems are adapted to the new setting and solved. In particular, the bankruptcy threat of optimal strategies appearing in the classical risk minim…
New algorithms learn MDPs with better regret bounds using generative sampling.
problem Learning MDPs with optimal policies under uncertainty.
method Hybrid exploration-generative RL model, classical and quantum algorithms.
result Quantum algorithms achieve polylogT regret for infinite-horizon MDPs. 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.