Quantum machine learning model for binary classification.
problem Efficiency in high-dimensional binary classification tasks.
method Quantum-classical hybrid algorithm and quantum computer for inference.
result Quantum discriminator achieves 99% accuracy on Iris dataset.
New metrics improve quantum ensemble learning efficiency and power.
problem Quantum ensembles' distances poorly understood due to measurement constraints.
method Introduce MMD-k hierarchy of integral probability metrics for quantum ensembles. result MMD-k requires fewer samples for full discriminative power at higher k. Quantum circuits learn to classify non-orthogonal quantum states.
problem Classifying non-orthogonal quantum states is crucial in quantum information.
method Trained quantum circuits using Adam optimization to discover parameters of unknown POVMs.
result Shallow quantum circuits can learn to discriminate among various quantum states with comparable performance to optimal POVMs.
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.
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.
We prove a reciprocity formula between Gauss sums that is used in the computation of certain quantum invariants of 3-manifolds. Our proof uses the discriminant construction applied to the tensor product of lattices.
Classifies extended Abelian Chern-Simons theories using quadratic modules.
problem Classifying extended Abelian Chern-Simons theories.
method Using quadratic modules to classify theories.
result Finite quadratic modules classify extended Abelian Chern-Simons theories.
A central task in the field of quantum computing is to find applications where quantum computer could provide exponential speedup over any classical computer. Machine learning represents an important field with broad applications where quantum computer may offer significant speedup. Several quantum algorithms for discr…
Quantum-assisted GAN learns MNIST and LSUN datasets.
problem Learning latent variable generative models with adversarial networks.
method Generative adversarial learning with quantum annealing.
result Quantum-assisted GAN successfully learns MNIST and LSUN datasets.
InfoQGAN uses mutual information to improve QGANs, overcoming mode collapse and feature disentanglement issues.
problem Mode collapse and lack of feature control in QGANs.
method Integrates InfoGAN principles with variational quantum circuit, classical discriminator, and MINE for mutual information optimization.
result InfoQGAN effectively mitigates mode collapse and achieves robust feature disentanglement.
Quantum machine learning classification depends on mutual informations between state and parameter spaces.
problem Generalization in quantum machine learning models.
method Link between quantum machine learning and quantum hypothesis testing, using mutual informations.
result Quantum classifier accuracy and generalization depend on mutual informations between state and parameter spaces.
In Deep Learning, a well-known approach for training a Deep Neural Network starts by training a generative Deep Belief Network model, typically using Contrastive Divergence (CD), then fine-tuning the weights using backpropagation or other discriminative techniques. However, the generative training can be time-consuming…
Quantum machine learning has received significant attention in recent years, and promising progress has been made in the development of quantum algorithms to speed up traditional machine learning tasks. In this work, however, we focus on investigating the information-theoretic upper bounds of sample complexity - how ma…
Quantum models generate financial time series with desired properties.
problem Generating synthetic financial data with temporal correlations.
method Quantum generative adversarial networks (QGANs) with quantum and classical components.
result QGANs can generate financial time series with matching distribution and temporal correlations.
Quantum computing improves fault diagnosis in industrial processes.
problem Fault detection and diagnosis in industrial process systems.
method Integrates quantum computing and deep learning to extract features and diagnose faults.
result Quantum-assisted deep learning achieves high fault detection rates (79.2% and 99.39%).
This study connects financial volatility to quantum mechanics on hyperbolic manifolds.
problem Deriving a geometric interpretation of financial volatility.
method Mapping financial pricing to quantum Hamiltonians via transformations.
result Financial volatility is a diffusion process on a hyperbolic manifold.
New framework quantifies uncertainty in data and models using RKHS.
problem Quantifying uncertainty in data and models.
method Projecting data into RKHS, transforming PDF, decomposing gradient flow.
result Decomposes uncertainty moments, providing discriminative resolution.
Kernel density matrices simplify probabilistic deep learning.
problem Representing joint probability distributions of continuous and discrete variables.
method Extending density matrices to a reproducing kernel Hilbert space.
result Versatile representation for marginal and joint probability distributions.
Determining the best method for training a machine learning algorithm is critical to maximizing its ability to classify data. In this paper, we compare the standard "fully supervised" approach (that relies on knowledge of event-by-event truth-level labels) with a recent proposal that instead utilizes class ratios as th…
Researchers successfully implemented quantum autoencoders using quantum adders in a cloud quantum computer.
problem Reducing resource usage in quantum computations.
method Experimental implementation of quantum autoencoders using approximate quantum adders in a cloud quantum computer.
result Experimental fidelities are in good agreement with theoretical predictions, proving the feasibility of quantum autoencoders via quantum adders.
The study quantifies and compares aleatoric and epistemic discrimination in ML models.
problem Sources of discrimination in ML models and their impact on performance.
method Quantifying aleatoric and epistemic discrimination using statistical experiments and model accuracy.
result State-of-the-art fairness interventions are effective at removing epistemic discrimination but not aleatoric discrimination in datasets with missing values.
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.
QGAA learns latent quantum states, reducing errors in quantum data generation.
problem Learning latent representations for quantum data generation.
method Quantum Generative Adversarial Autoencoder (QGAA) combining QAE and QGAN.
result Average errors in energies for H2 and LiH are 0.02 Ha and 0.06 Ha respectively, demonstrating QGAA's potential.
Quantum machine learning uses quantum cross entropy to minimize loss, but measurement loss affects this process.
problem Quantum machine learning's loss minimization through cross entropy is affected by measurement outcomes.
method Defined quantum cross entropy, proved its lower bounds, and investigated its relation to quantum fidelity and likelihood.
result Quantum cross entropy is lower-bounded by negative log-likelihood when derived from quantum data, but measurement outcomes can cause loss.
Quantum Earth Mover's distance improves stability and efficiency in quantum learning.
problem Quantum learning's loss landscapes often lead to poor local minima and gradients.
method Introduced the quantum Earth Mover's (EM) distance and proposed a quantum Wasserstein generative adversarial network (qWGAN).
result The quantum EM distance makes quantum learning more stable and efficient.
Quantum Gaussian processes enable scalable quantum learning.
problem Lack of simple, interpretable, scalable learning frameworks for quantum data.
method Bayesian framework using Gaussian processes with quantum kernels.
result Provable and scalable quantum Gaussian processes for quantum learning.
Discrimination-aware classification is receiving an increasing attention in data science fields. The pre-process methods for constructing a discrimination-free classifier first remove discrimination from the training data, and then learn the classifier from the cleaned data. However, they lack a theoretical guarantee f…
Quantum machine learning models can approximate any continuous function.
problem Theoretical understanding of quantum feature maps in machine learning.
method Proving universal approximation property of quantum machine learning models in quantum-enhanced feature spaces.
result Quantum machine learning models are universal approximators of continuous functions.
Discriminator guidance improves autoregressive diffusion models for generating molecular graphs.
problem Improving the accuracy of autoregressive diffusion models for generating molecular graphs.
method Deriving ways to use a discriminator with a pretrained generative model in the discrete case, including optimal and sub-optimal scenarios.
result Using a discriminator can correct pretrained models and improve exact sampling from the data distribution.
Paper derives quantum Kolmogorov equations using nonlocal quantum mechanics.
problem Quantum finance equations derived from quantum stochastic calculus.
method Nonlocal approach to quantum mechanics for deriving equations.
result Nonlocal diffusions and quantum stochastic processes linked.
This paper proposes a curriculum learning method for GANs using multiple discriminators.
problem Training GANs with sufficient convergence conditions and avoiding mode collapse.
method A framework for training the generator against an ensemble of discriminator networks, formalized in the full-information adversarial bandit framework.
result Our approach improves samples quality and diversity over existing baselines by effectively learning a curriculum.
Introduces Quantum Data Center for quantum era benefits.
problem No specific problem stated; focuses on future potential.
method Combines QRAM and quantum networks.
result QDC offers efficiency, security, and precision.
Solves GAN mode collapse by assigning minibatches to multiple discriminators.
problem Mode collapse in GANs where models generate similar samples.
method Multiple discriminators, microbatching, and changing tasks.
result Promotes sample diversity in generated sets.
We define quantum exterior product wedge_h and quantum exterior differential d_h on Poisson manifolds, of which symplectic manifolds are an important class of examples. Quantum de Rham cohomology is defined as the cohomology of d_h. We also define quantum Dolbeault cohomology. Quantum hard Lefschetz theorem is proved. …
Paper proposes a quantum deep clustering framework with improved performance.
problem Improving clustering performance in quantum machine learning.
method Quantum deep SVM, deep convolutional neural networks, and quantum K-Means clustering.
result The proposed quantum deep clustering framework shows significant performance gains over classical methods.
Kernel discriminant analysis uses nonlinear embeddings to improve classification.
problem Limited effectiveness of linear discriminant analysis in capturing nonlinear features.
method Study of nonlinear embeddings in kernel discriminant analysis using polynomial and Gaussian kernels, solving generalized eigenvalue problems.
result Polynomial and Gaussian discriminants capture class differences through population moments and randomized projections.
VQAs use classical optimization to train quantum circuits, promising quantum advantage.
problem High computational cost of quantum simulations and solving large-scale problems.
method Variational Quantum Algorithms (VQAs) use classical optimizers to train parametrized quantum circuits.
result VQAs are a promising strategy for obtaining quantum advantage.
Quantum Natural Gradient uses quantum geometry for optimization.
problem Optimizing variational quantum circuits efficiently.
method Quantum generalization of Natural Gradient Descent using Quantum Information Geometry.
result Efficient algorithm for computing metric tensor approximations.
Quantum machine learning tackles large datasets with randomized measurements.
problem Efficiently process large, high-dimensional datasets on quantum computers.
method Randomized measurements to scale linearly with dataset size and quadratic for post-processing.
result Substantial speed-up for noisy quantum computers, enabling image classification.
Quantum optimization aids in financial crash prediction and portfolio management.
problem Hard financial optimization problems.
method Quantum algorithms for financial crashes and portfolio optimization.
result Quantum strategies improve financial prediction and portfolio management.
Quantum model investigates financial derivative price dynamics with quantum interference effects.
problem Investigate quantum drift in financial derivatives using Heisenberg Equation of Motion.
method Apply geometric techniques to integrate Heisenberg Equation of Motion, model financial market as quantum observable.
result Quantum interference effects can act as drag or boost on financial returns.
Quantum states can be learned efficiently using gentle measurements.
problem Efficiently learning quantum states with minimal measurements.
method Introducing α-LGM measurements and proving strong quantum DPI.
result The number of states needed for accurate learning is of order 1/(ε^2 α^2).
Lower-dimensional video discriminators improve GAN performance.
problem High curvature in unconstrained video discriminator loss surfaces.
method Proposed Lower-Dimensional Video Discriminators (LDVD GANs).
result LDVD GANs double Temporal-GAN performance and achieve state-of-the-art performance.
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.
Post-quantum cryptography needed for blockchain security.
problem Quantum computers threaten traditional blockchain cryptography.
method Review of theoretical cryptography and quantum information theory.
result Post-quantum cryptography is essential for blockchain security.
Quantum trace map connects Teichmüller theory and quantum groups.
problem Connecting quantum groups to Teichmüller theory for knots.
method Quantum snakes technology to relate Fock-Goncharov monodromy matrices to quantum SL_n.
result Quantized Fock-Goncharov matrices satisfy quantum SL_n relations.
Q-CurL optimizes quantum learning with a curriculum design.
problem Efficiently training quantum models with limited resources.
method Quantum curriculum learning framework.
result Q-CurL enhances training convergence and generalization.
Quantum LS-SVM simplifies matrix inversion for faster machine learning.
problem Speeding up machine learning algorithms for large datasets.
method Introduces a novel quantum algorithm using continuous variables to simplify matrix inversion in LS-SVM, and proposes a hybrid quantum-classical approach for sparse solutions.
result Quantum LS-SVM achieves exponential speed-up and can solve classically difficult tasks.