arXiv research

Paper tackles interpretability issues in deep learning models.

The state-of-the-art machine learning approaches are based on classical von Neumann computing architectures and have been widely used in many industrial and academic domains. With the recent development of quantum computing, researchers and tech-giants have attempted new quantum circuits for machine learning tasks. How…

Quantum ML promises faster data analysis but faces trainability challenges.

In this paper, we have proposed a deep quantum SVM formulation, and further demonstrated a quantum-clustering framework based on the quantum deep SVM formulation, deep convolutional neural networks, and quantum K-Means clustering. We have investigated the run time computational complexity of the proposed quantum deep c…

This work introduces a new quantum kernel, quantum tangent kernel, for improved performance.

Quantum variational circuits improve reinforcement learning efficiency.

Study combines quantum and classical deep learning for better credit risk assessment.

Quantum machine learning improves hedging in finance.

Deep quantum neural networks applied to finance for efficient risk management.

Paper explores Monge-Ampère in deep learning and quantum geometry.

Enhances quantum circuit synthesis using deep learning and geometric methods.

In the past decade, the field of quantum machine learning has drawn significant attention due to the prospect of bringing genuine computational advantages to now widespread algorithmic methods. However, not all domains of machine learning have benefited equally from quantum enhancements. Notably, deep learning and rein…

Hybrid tensor networks improve machine learning by combining quantum and classical methods.

Deep QMC methods use neural networks to solve quantum chemistry problems.

Quantum circuits optimize financial portfolios faster than classical methods.

QBC uses quantum computers to speed up Bayesian computation.

Bayesian methods in machine learning, such as Gaussian processes, have great advantages com-pared to other techniques. In particular, they provide estimates of the uncertainty associated with a prediction. Extending the Bayesian approach to deep architectures has remained a major challenge. Recent results connected dee…

Novel framework explains generalization in deep neural networks.

Quantum circuit Born machines are generative models which represent the probability distribution of classical dataset as quantum pure states. Computational complexity considerations of the quantum sampling problem suggest that the quantum circuits exhibit stronger expressibility compared to classical neural networks. O…

Quantum computing improves fault diagnosis in industrial processes.

Paper introduces a new deep-learning method for quantum mechanics.

A new hybrid framework reduces quantum runtime and noise effects.

This work explores using deep NNs to learn quantum systems from probability distributions.

Quantum computers can enhance spectral methods in machine learning.

We present an algorithm for learning a latent variable generative model via generative adversarial learning where the canonical uniform noise input is replaced by samples from a graphical model. This graphical model is learned by a Boltzmann machine which learns low-dimensional feature representation of data extracted …

Quantum method speeds up VB estimation in machine learning.

Eigen component analysis combines quantum mechanics with machine learning for efficient data analysis.

AI in finance uses quantum logic for better decision-making.

Machine learning advances chemistry and materials science by enabling large-scale exploration of chemical space based on quantum chemical calculations. While these models supply fast and accurate predictions of atomistic chemical properties, they do not explicitly capture the electronic degrees of freedom of a molecule…

Paper proposes quantum methods for optimizing machine learning functions.

Quantum model improves safety in machine learning.

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…

Hybrid quantum-classical RL model solves standard benchmark tasks and proves quantum advantage.

Quantum GAN improves volatility modeling in finance.

Deep neural network predicts molecular wave functions in minimal basis.

A new approach to quantum machine learning circuits reduces training difficulties.

InQMAD detects anomalies in streaming data using quantum measurements and density matrices.

Future advancement of engineering applications is dependent on design of novel materials with desired properties. Enormous size of known chemical space necessitates use of automated high throughput screening to search the desired material. The high throughput screening uses quantum chemistry calculations to predict mat…

Quantum-enhanced method improves stock return prediction accuracy.

We review supervised learning and deep neural network design for learning membership on algebraic varieties. We demonstrate that these trained artificial neural networks can predict the entanglement type for quantum states. We give examples for detecting degenerate states, as well as border rank classification for up t…

New method combines deep learning and quantum mechanics for efficient molecular statistics.

Novel RL approach for molecular design using quantum mechanics.

The resemblance between the methods used in quantum-many body physics and in machine learning has drawn considerable attention. In particular, tensor networks (TNs) and deep learning architectures bear striking similarities to the extent that TNs can be used for machine learning. Previous results used one-dimensional T…

Quantum machine learning solves high-dimensional PDEs with lower variance and improved accuracy.

Deep learning improves OFDFT for molecular systems.

Quantum model discovery uses DQCs to solve equations from data.

Quantum statistical models with singularities are studied for state estimation and model selection.

Study benchmarks classical models over quantum in DeFi yield prediction.