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.
Adversarial quantum-classical model learns and infers data faster.
problem Training quantum circuits is harder than classical neural networks.
method Coupling quantum generator with classical discriminator for training.
result Quantum circuit can infer missing data with quadratic speed up.
Paper analyzes classical multidimensional scaling for cluster recovery.
problem Cluster recovery from noisy data.
method Classical multidimensional scaling followed by distance-based clustering.
result Scaling conditions for high probability cluster recovery.
Survey of various non-classical knot theories from geometric and algebraic perspectives.
problem Various modifications to classical knot theory.
method Comparative geometric and algebraic analysis of non-classical knot theories.
result Distinct topological and combinatorial features in generalized knot theories.
In this paper we investigate the life-span of classical solutions to the hyperbolic geometric flow in two space variables with slow decay initial data. By establishing some new estimates on the solutions of linear wave equations in two space variables, we give a lower bound of the life-span of classical solutions to th…
Paper presents a faster classical algorithm for principal component regression.
problem Efficiently solving principal component regression problems.
method Uses quantum-inspired linear algebra techniques.
result Achieves polylogarithmic runtime, significantly faster than state-of-the-art.
New method uses quantum computing to process classical data efficiently.
problem Inefficient quantum machine learning due to data loading and trainability issues.
method Linear Hamiltonian-based machine learning with ground state problems for k-local Hamiltonians.
result Demonstrated the effectiveness and scalability of the method on up to 50 qubits.
Quantum machine learning offers advantages for broader learning tasks.
problem Demonstrate QML advantage over classical methods for general learning tasks.
method Construct a new family of supervised learning tasks and prove their hardness.
result Prove provable advantage of QML based on general quantum computational advantages.
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.
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 …
Recently, increased computational power and data availability, as well as algorithmic advances, have led machine learning techniques to impressive results in regression, classification, data-generation and reinforcement learning tasks. Despite these successes, the proximity to the physical limits of chip fabrication al…
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.
A multisymplectic setting for classical field theories subjected to non-holonomic constraints is presented. The infinite dimensional setting in the space of Cauchy data is also given.
Quantum learning complexity reviewed using information theory.
problem Learning properties of quantum systems or processing data via quantum computing.
method Information-theoretic techniques focusing on data, copy, and model complexity.
result Copy complexity due to irreversible quantum measurements limits information extraction.
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.
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…
New classical algorithm outperforms quantum in neural network subnetwork selection.
problem Selecting sparse subnetworks from large neural networks efficiently.
method Quantum-inspired classical algorithm using ridgelet transform sampling.
result Runs in polynomial time, outperforming naive classical methods.
Hybrid model combines deep learning and classical methods for forecasting time series.
problem Forecasting large collections of similar time series is challenging and complex.
method Proposes a hybrid model integrating deep neural networks and classical time series models.
result Demonstrates improved data efficiency, accuracy, and computational complexity.
Neuroimaging research has predominantly drawn conclusions based on classical statistics, including null-hypothesis testing, t-tests, and ANOVA. Throughout recent years, statistical learning methods enjoy increasing popularity, including cross-validation, pattern classification, and sparsity-inducing regression. These t…
This study improves quantum classifiers by optimizing data preprocessing.
problem Quantum Machine Learning advantages are not yet clearly demonstrated.
method Used Linear Discriminant Analysis (LDA) for data preprocessing.
result Variational Quantum Algorithm (VQA) outperforms classical classifiers.
Quantum computing improves feature selection in machine learning.
problem Optimizing feature selection in machine learning problems.
method Formulated feature selection as a QUBO problem and compared quantum and classical methods.
result Quantum computing can outperform classical methods in feature selection, depending on data set.
The paper calculates bridge numbers for knots using machine learning.
problem Determining the bridge number for virtual knots with multiple definitions.
method Employed computational techniques and machine learning models to classify knots based on their bridge numbers.
result Demonstrated that the bridge number for virtual knots can differ significantly.
Study evaluates quantum and classical conditional Boltzmann machines for time-series forecasting.
problem Time-series forecasting using quantum and classical conditional Boltzmann machines.
method Developed and compared four conditional energy-based forecasting architectures: Gaussian-Bernoulli CRBM, QCRBM, QQRBM, and QFeatureQRBM. Evaluated using symmetric hyperparameter optimisation.
result No systematic evidence of a quantum advantage in time-series forecasting at the available sample size.
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.
FUSE neural centrality framework improves data point measurement in high dimensions.
problem Measuring centrality in high-dimensional data is expensive and unstable.
method Combines global and local heads trained on arbitrary representations.
result Reveals meaningful classical ordering and competitive performance.
Tensor networks preserve data interpretability for supervised learning.
problem Efficiently classifying data using tensor networks.
method Number-state preserving tensor networks for supervised learning.
result Number-state preserving tensor networks can be trained to maximize their scalar product against data sets.
Hybrid model combines deep learning and Gaussian processes for forecasting.
problem Challenges in classical and neural forecasting for large time series data.
method Data-driven hybrid model with a deep latent component and a local Gaussian Process.
result Obtains higher accuracy than state-of-the-art methods.
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…
SQS uses quantum kernels to improve credit scoring with fewer data points.
problem Credit scoring models struggle with scarce and skewed data.
method Systemic Quantum Score (SQS) leverages quantum kernels for better pattern extraction.
result SQS shows improved performance and pattern extraction with fewer data points.
A quantum-inspired classical algorithm speeds up LS-SVM classification.
problem Big data challenge in SVM classification.
method Improved indirect sampling technique for LS-SVM.
result Algorithm achieves logarithmic runtime for low rank data matrices.
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.
This review explores entropy applications in data analysis and machine learning.
problem Characterizing probability mass distributions in data analysis and machine learning.
method Review of various entropy types and their applications.
result Entropy's versatility in data analysis and machine learning.
A new ML method speeds up PDE simulations without needing classical training.
problem Accelerating transient PDE simulations using machine learning.
method Online-learned preconditioners using a bandit algorithm.
result One-shot acceleration of PDE simulations.
Quantum dynamics algorithm learns manifold from data.
problem Learning manifolds from high-dimensional datasets.
method Simulation of quantum dynamics on a graph embedding of data.
result Algorithm reveals connections between data sampling and quantization.
Method preserves order in hierarchical clustering of ordered data.
problem Order preserving hierarchical clustering of directed acyclic graphs.
method Combination of classical hierarchical clustering and ultrametric fitting.
result Optimal clustering preserves both cluster quality and order.
Estimates classical potential from stock price data using quantum mechanics.
problem Estimating classical potential from empirical stock price data.
method Quantum mechanical model of stock price distribution, estimating potential from wave function.
result Suggests methods to evaluate classical potential for Schrodinger equation.
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.
Introduces CSLC models to bridge deep generative models and classical algorithms.
problem Mode collapse and memorization issues in deep generative models and restrictive assumptions in classical algorithms.
method Introduces conditionally strongly log-concave (CSLC) models, factorizing data distribution into strongly log-concave conditional distributions.
result Efficient parameter estimation and sampling algorithms with theoretical guarantees for non-log-concave data distributions.
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.
New algorithm for maximizing submodular functions in real-time data changes.
problem Maximizing submodular functions under dynamic constraints.
method Randomized algorithm with O(k2) amortized update time. result 4-approximate solution to submodular maximization problem.
Improved stock index analysis using fuzzy parameters and machine learning.
problem Analyzing the S&P 500 stock index with long-term dependence.
method Combining fuzzy theory and machine learning to modify the Barndorff-Nielsen and Shephard model.
result The new model effectively captures the stochastic dynamics of the stock index time series.
Improved PCA accuracy using maximum entropy method.
problem Accuracy issues in classical PCA.
method Model uncertainty with random variables, apply Maximum Entropy Method.
result Improved estimates of distances between data items.
Improved neural network regression uncertainty estimation.
problem Neural networks lack classical uncertainty due to finite data.
method Bootstrapped Deep Ensembles, incorporating parametric bootstrap.
result Significantly improved uncertainty estimation compared to standard Deep Ensembles.
Quantum Boltzmann Machines trained on quantum annealers produce noisy synthetic data.
problem Training quantum Boltzmann machines on quantum annealers for financial data generation.
method Used D-Wave Advantage 4.1 quantum annealer to train QBMs and compare with classical RBMs.
result Quantum Boltzmann Machines trained on quantum annealers are noisier and less effective than classical RBMs.
KAN-PCA improves asset return analysis by capturing more variance than classical PCA during market crises.
problem Inefficient classical PCA during market crises when correlations between assets change dramatically.
method KAN-PCA uses KAN (Kolmogorov-Arnold Networks) with B-spline functions to learn nonlinear projections.
result KAN-PCA achieves a higher reconstruction R^2 (66.57%) compared to classical PCA (62.99%) on 20 S&P 500 stocks.
This review discusses challenges and solutions for AI in chemical engineering.
problem Challenges in applying classical machine learning to chemical engineering data.
method Identifying four data characteristics and discussing their applications and solutions.
result Current research extends data science and machine learning to handle chemical engineering data challenges.
In genome-wide interaction studies, to detect gene-gene interactions, most methods are divided into two folds: single nucleotide polymorphisms (SNP) based and gene-based methods. Basically, the methods based on the gene are more effective than the methods based on a single SNP. Recent years, while the kernel canonical …
AR-Net models time-series with interpretable coefficients and scalability.
problem Modeling time-series with long-range dependencies and interpretability.
method Feed-forward neural network approach to AR-process dynamics.
result AR-Net learns identical AR-coefficients as Classic-AR and scales to long-range dependencies.