Variational Quantum Classifier Benchmark
Benchmark of variational quantum classifiers that use parameterized quantum circuits for supervised learning. Circuit-centric quantum classifiers encode classical data into quantum states via feature maps, then apply trainable unitaries and measure a readout qubit for binary classification. Benchmarks evaluate different ansatz architectures, encoding strategies, and circuit depths on standard datasets including Iris, MNIST subsets, and domain-specific applications.[1]
- Algorithm: Variational Quantum Classifier (VQC)
- Category: machine-learning
- Qubits: 8
- Framework: Qiskit Machine Learning, PennyLane
- Reproducible: Yes
- Published:
- VQC
- classification
- variational
- machine-learning
- hybrid
- data-encoding
What algorithm does Variational Quantum Classifier Benchmark use?
Variational Quantum Classifier Benchmark uses the Variational Quantum Classifier (VQC) algorithm, categorized under machine-learning.
Frequently Asked Questions
What is the Variational Quantum Classifier Benchmark benchmark?
Benchmark of variational quantum classifiers that use parameterized quantum circuits for supervised learning. Circuit-centric quantum classifiers encode classical data into quantum states via feature maps, then apply trainable unitaries and measure a readout qubit for binary classification. Benchmarks evaluate different ansatz architectures, encoding strategies, and circuit depths on standard datasets including Iris, MNIST subsets, and domain-specific applications.
Is Variational Quantum Classifier Benchmark reproducible?
Yes, this benchmark is reproducible.