arXiv research

Quantum states can be learned efficiently using gentle measurements.

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…

Quantum ML predicts data with improved speed and accuracy.

Study noncommutative Sobolev inequalities using quantum state metrics.

Quantum theory challenges traditional cause-effect relations, showing causal influences even without Bell inequality violations.

Quantum Gaussian processes enable scalable quantum learning.

Unified geometric approach to quantum indeterminacy.

Quantum channels' contraction under privacy constraints studied.

Quantum-assisted Gaussian process speeds up data regression.

Bell's theorem shows quantum correlations can't be explained by classical causal models, even with some measurement dependence.

New method uses quantum computing to process classical data efficiently.

Quantum neural networks approximate periodic functions more efficiently.

Researchers determine quantum filtration structure of torus links.

A new GP method enforces physical constraints in probabilistic terms.

Quantum learning complexity reviewed using information theory.

Study online learning of quantum processes, showing feasibility for certain types.

Quantum CNNs improve on multi-channel data processing.

The paper explores quantum statistical manifolds and their autoparallelity, providing estimation-theoretical characterizations.

Unified framework for robust causal directionality in quantum systems under MNAR observation.

Distributed Quantum Gaussian Processes improve modeling in multi-agent systems.

The paper proves a Hawking-type singularity theorem using worldvolume quantum strong energy inequalities.

Intertwining curvature bounds for graphs and quantum Markov semigroups verified.

In this paper, we present a new framework to obtain tail inequalities for sums of random matrices. Compared with existing works, our tail inequalities have the following characteristics: 1) high feasibility--they can be used to study the tail behavior of various matrix functions, e.g., arbitrary matrix norms, the absol…

Quantum model generates complex time series data with preserved temporal dynamics.

Quantum systems with scrambling improve temporal information processing, but scaling requires exponential overhead.

New method uses single quantum state for machine learning tasks, improving accuracy.

Quantum computing improves fault diagnosis in industrial processes.

Quantum reservoir computing tackles noisy quantum computers for temporal tasks.

Enhances quantum machine learning models using Fock states.

Quantum Signal Processing reduces derivative pricing quantum resource requirements.

Quantum machine learning generalizes well from limited data.

In 1981 Edward Witten proved a remarkable result where he derived the classical Morse Inequalities using ideas from Supersymmetric (SUSY) Quantum Mechanics. In this regard, one has an example where a Physical Theory has something to say about the underlying Mathematical Structure. The objective of this essay is to unde…

Quantum Process Tomography (QPT) methods aim at identifying, i.e. estimating, a given quantum process. QPT is a major quantum information processing tool, since it especially allows one to characterize the actual behavior of quantum gates, which are the building blocks of quantum computers. However, usual QPT procedure…

Quantum machine learning tackles large datasets with randomized measurements.

Quantum isometry groups extend to all countable metric spaces, and loose embeddings help understand metric space relationships.

Quantum mechanics fundamentally forbids deterministic discrimination of quantum states and processes. However, the ability to optimally distinguish various classes of quantum data is an important primitive in quantum information science. In this work, we train near-term quantum circuits to classify data represented by …

Study on learning quantum dynamics without direct interaction.

This paper proposes a brain-inspired approach to quantum machine learning with the goal of circumventing many of the complications of other approaches. The fact that quantum processes are unitary presents both opportunities and challenges. A principal opportunity is that a large number of computations can be carried ou…

QBC uses quantum computers to speed up Bayesian computation.

Classical clients can verify quantum learning tasks efficiently.

Quantum RNG improves financial risk metrics estimation.

Quantum machine learning uses quantum cross entropy to minimize loss, but measurement loss affects this process.

Data processing inequalities link Fisher information to local differential privacy constraints.

We demonstrate how quantum computation can provide non-trivial improvements in the computational and statistical complexity of the perceptron model. We develop two quantum algorithms for perceptron learning. The first algorithm exploits quantum information processing to determine a separating hyperplane using a number …

Quantum algorithm speeds up financial option pricing.

Improved MMWU algorithm achieves instance-optimal regret bound for matrix LEA.

The paper improves count data regression models for overdispersed data.

For a 2-periodic link $\tilde L$ in the thickened annulus and its quotient link $L$, we exhibit a spectral sequence with $E^1 \cong AKh(\tilde L) \otimes_{\mathbb{F}_2} \mathbb{F}_2[θ, θ^{-1}] \rightrightarrows E^\infty \cong AKh(L) \otimes_{\mathbb{F}_2} \mathbb{F}_2[θ, θ^{-1}].$ This spectral sequence splits along qu…