Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Hybridization of Atomic Orbitals II03:35

Hybridization of Atomic Orbitals II

sp3d and sp3d 2 Hybridization
Hybridization of Atomic Orbitals I03:24

Hybridization of Atomic Orbitals I

The mathematical expression known as the wave function, ψ, contains information about each orbital and the wavelike properties of electrons in an isolated atom. When atoms are bound together in a molecule, the wave functions combine to produce new mathematical descriptions that have different shapes. This process of combining the wave functions for atomic orbitals is called hybridization and is mathematically accomplished by the linear combination of atomic orbitals. The new orbitals that...
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
Aggregates Classification01:29

Aggregates Classification

Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
Classification of Signals01:30

Classification of Signals

In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
Classification of Systems-II01:31

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

FinTextSim: a domain-specific sentence-transformer for extracting predictive latent topics from financial disclosures.

Frontiers in artificial intelligence·2026
Same author

Standardized quantum transistor block enables differentiable learning on gait dynamics.

Scientific reports·2026
Same author

Relativistic triangle-curvature computing for federated HIV-1 protein-sequence monitoring.

Scientific reports·2026
Same author

Quantum-classical deep learning hybrid architecture with graphene-printed low-cost capacitive sensor for essential tremor detection.

Scientific reports·2025
Same author

Sufficiency for PSS tracking gait disorders in multiple sclerosis: A managerial perspective.

Heliyon·2024
Same author

Socio-Technical Analysis of the Benefits and Barriers to Using a Digital Representation of the Global Horse Population in Equine Veterinary Medicine.

Animals : an open access journal from MDPI·2023

Related Experiment Videos

Exploiting quantum chaos diagnostics in QAOA for enhanced hybrid quantum classical deep learning classification.

Javier Villalba-Díez1,2, Juan Carlos Losada-González3

  • 1Fakultät Wirtschaft, Hochschule Heilbronn, Max-Planck-Str.39, Heilbronn, 74081, Baden-Württemberg, Germany. javier.villalba-diez@hs-heilbronn.de.

Scientific Reports
|May 19, 2026
PubMed
Summary

A new chaos diagnostic enhances quantum classifiers by improving accuracy in limited quantum systems. This method balances circuit expressivity and sensitivity for better performance.

Keywords:
Chaos diagnosticsHybrid quantum-classicalImage classificationOTOCQAOAQuantum chaosQuantum machine learning

Related Experiment Videos

Area of Science:

  • Quantum Computing
  • Machine Learning
  • Complex Systems

Background:

  • Hybrid quantum-classical classifiers offer a promising approach for machine learning tasks.
  • The Quantum Approximate Optimization Algorithm (QAOA) is a key variational algorithm for near-term quantum devices.
  • Understanding and mitigating finite-size effects and sensitivity are crucial for practical quantum machine learning.

Purpose of the Study:

  • To repurpose QAOA as a feature map in a hybrid quantum-classical classifier.
  • To introduce a chaos-informed diagnostic to enhance classifier performance.
  • To investigate the impact of a chaos diagnostic on classifier accuracy across varying qubit numbers.

Main Methods:

  • Extracted a scalar chaos feature using Out-Of-Time-Ordered correlators (OTOC) and lognormal modeling.
  • Trained two models on MNIST: StandardHybrid (Pauli expectations) and ChaosAwareHybrid (adds OTOC feature).
  • Conducted 5-fold cross-validation across different numbers of qubits ([Formula: see text]) and fixed depth ([Formula: see text]).

Main Results:

  • The ChaosAwareHybrid model significantly improved test accuracy at [Formula: see text] qubits ([Formula: see text]-[Formula: see text]), with high win-rates (86-100%).
  • At [Formula: see text] qubits, the chaos-aware approach showed diminishing returns or negative effects, indicating over-sensitivity.
  • The optimal performance was observed at [Formula: see text] qubits ([Formula: see text]), balancing expressivity and sensitivity.

Conclusions:

  • A calibrated chaos diagnostic can enhance hybrid quantum-classical classifiers, especially in resource-limited scenarios.
  • The chaos diagnostic provides a tunable parameter to optimize the trade-off between circuit expressivity and sensitivity.
  • This approach offers a principled method for improving quantum machine learning model performance.