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

Associative Learning01:27

Associative Learning

1.1K
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
1.1K
Woodward–Hoffmann Selection Rules and Microscopic Reversibility01:34

Woodward–Hoffmann Selection Rules and Microscopic Reversibility

3.7K
Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...
3.7K
Reinforcement Schedules01:24

Reinforcement Schedules

383
Positive reinforcement is a powerful method for teaching new behaviors to both animals and humans. B.F. Skinner demonstrated this with his experiments using rats in a Skinner box. When a rat pressed a lever, it received a food pellet. This immediate reward encouraged the rat to repeat the behavior. This method, where a reward follows every instance of the behavior, is known as continuous reinforcement. It is highly effective for establishing new behaviors quickly.
Once a behavior is learned,...
383
Reinforcement01:23

Reinforcement

723
Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
Positive reinforcement occurs when a behavior is followed by the presentation of a rewarding stimulus, increasing the frequency of that behavior. For example:
723
Hybridization of Atomic Orbitals II03:35

Hybridization of Atomic Orbitals II

47.0K
sp3d and sp3d 2 Hybridization
47.0K
Hybridization of Atomic Orbitals I03:24

Hybridization of Atomic Orbitals I

64.5K
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...
64.5K

You might also read

Related Articles

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

Sort by
Same author

Multi-qubit correction for quantum annealers.

Scientific reports·2021
Same author

Understanding Cybersecurity Threat Trends Through Dynamic Topic Modeling.

Frontiers in big data·2021
Same author

Canonical correlation analysis of brain prefrontal activity measured by functional near infra-red spectroscopy (fNIRS) during a moral judgment task.

Behavioural brain research·2018
Same author

The role of prefrontal cortex in a moral judgment task using functional near-infrared spectroscopy.

Brain and behavior·2018
Same author

A Hybrid Task Graph Scheduler for High Performance Image Processing Workflows.

Journal of signal processing systems·2017
Same author

Security policies and trust in ubiquitous computing.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences·2008

Related Experiment Video

Updated: Dec 21, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

996

Reinforcement Quantum Annealing: A Hybrid Quantum Learning Automata.

Ramin Ayanzadeh1, Milton Halem2, Tim Finin2

  • 1Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD, 21250, United States. ayanzadeh@umbc.edu.

Scientific Reports
|May 16, 2020
PubMed
Summary

We developed reinforcement quantum annealing (RQA), a new method where an intelligent agent optimizes problems for quantum annealers. RQA improves finding optimal solutions for complex problems like Boolean satisfiability using fewer samples.

More Related Videos

Gradient Echo Quantum Memory in Warm Atomic Vapor
10:00

Gradient Echo Quantum Memory in Warm Atomic Vapor

Published on: November 11, 2013

13.1K

Related Experiment Videos

Last Updated: Dec 21, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

996
Gradient Echo Quantum Memory in Warm Atomic Vapor
10:00

Gradient Echo Quantum Memory in Warm Atomic Vapor

Published on: November 11, 2013

13.1K

Area of Science:

  • Quantum Computing
  • Artificial Intelligence
  • Computational Complexity

Background:

  • Quantum annealing is a metaheuristic optimization technique that leverages quantum mechanics to find the global optimum of a given objective function.
  • Solving complex problems like Boolean satisfiability (SAT) remains a significant challenge in computer science, often requiring substantial computational resources.
  • Current quantum annealing approaches may require extensive sampling to achieve optimal solutions.

Purpose of the Study:

  • To introduce a novel Reinforcement Quantum Annealing (RQA) scheme that enhances problem-solving capabilities of quantum annealers.
  • To develop a new method for casting Boolean satisfiability (SAT) problems into Ising Hamiltonians suitable for quantum annealing.
  • To demonstrate the efficacy of RQA in improving the probability of finding global optima with fewer samples.

Main Methods:

  • An intelligent agent interacts with a quantum annealer, adjusting problem Hamiltonians based on previous results.
  • The RQA scheme involves an agent iteratively refining the penalty for unsatisfied constraints and reformulating the problem as an Ising Hamiltonian.
  • A proof-of-concept approach for mapping SAT problems to Ising Hamiltonians was developed and tested.

Main Results:

  • Experimental results on benchmark SAT problems using a D-Wave 2000Q quantum processor showed RQA's effectiveness.
  • RQA consistently found better solutions compared to existing quantum annealing techniques.
  • The RQA scheme required significantly fewer samples to achieve high-quality solutions.

Conclusions:

  • Reinforcement Quantum Annealing (RQA) offers a promising advancement for optimizing complex problems on quantum hardware.
  • The proposed SAT-to-Ising Hamiltonian casting method combined with RQA enhances solution quality and efficiency.
  • RQA represents a significant step towards more effective utilization of quantum annealers for computational challenges.