.png){kind=icon}
Return
Quantum machine learning : Developing hybrid quantum-classical algorithms for enhanced computational power
.png){kind=icon}
PRE
AI
DOI：10.47974/JDMSC-2543.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The increasing need for machine learning to run with less computer resources is i ) P tic l ly f j b th mpl lip tt r th fi di causing quantum computing to be considered as a viable substitute for conventional i di at th t th i ed pp chat m c th l i l computing. This paper presents a hybrid quantum-classical system that improves learning b eli s R l qu ntu h dare l o wok ll h h y em; i h o by means of a classical optimisation loop combined with parameterised quantum circuits. impa it p i ion Thi work d m rats ho bind q While conventional procedures handle parameter updates and convergence, the proposed approach stores and processes high-dimensional data using quantum variational circuits. a l g m gh ci m n h w n s a pr d Qiskit is used by usto construct thisarchitecture and runit on conventional datasets like genuin q an b s r app ai n q r gAag pros i g Iris, MNIST(binary), CIFAR-100 (subset), HIGGS, and GTEx (gene expression). Particularly Subject Classification 68M25 for jobs with complex, nonlinear patterns, the findings indicate that the mixed approach is Keywords: Quantum Machine Learning, Hybrid Algorithms, Variational Quantum Circuit, Quantum-Classical Optimization, Qiskit, NISQDevices as accurate as or more accurate than classical baselines. Real quantum hardware also works well with the system; noise has no impact on its precision. This work demonstrates how combined quantum-classical learning models might circumvent hardware constraints and provide genuine quantum benefits for applications requiring large data processing.
Keywords:
Quantum machine learning
Hybrid algorithms
Variational quantum circuit
Quantum-classical optimization
Qiskit
NISQ devices
Journal
J
IF:
1.1
Papers:
185
Citations:
0
Organization
Cited Papers
No cited papers available