Quantum Kernel Methods Classification Benchmark
Benchmark evaluating quantum kernel estimation (QKE) and quantum kernel training (QKT) for classification tasks using quantum support vector machines. A large-scale study examined fidelity-based and projected quantum kernels across 64 datasets and five dataset families. While quantum kernels outperform classical methods on specifically constructed ad-hoc datasets, results on standard classical benchmarks are mixed.[1]
- Algorithm: Quantum Support Vector Machine (QSVM) with quantum kernels
- Category: machine-learning
- Qubits: 10
- Framework: Qiskit Machine Learning, PennyLane
- Reproducible: Yes
- Published:
- quantum-kernel
- QSVM
- classification
- feature-map
- machine-learning
What algorithm does Quantum Kernel Methods Classification Benchmark use?
Quantum Kernel Methods Classification Benchmark uses the Quantum Support Vector Machine (QSVM) with quantum kernels algorithm, categorized under machine-learning.
Frequently Asked Questions
What is the Quantum Kernel Methods Classification Benchmark benchmark?
Benchmark evaluating quantum kernel estimation (QKE) and quantum kernel training (QKT) for classification tasks using quantum support vector machines. A large-scale study examined fidelity-based and projected quantum kernels across 64 datasets and five dataset families. While quantum kernels outperform classical methods on specifically constructed ad-hoc datasets, results on standard classical benchmarks are mixed.
Is Quantum Kernel Methods Classification Benchmark reproducible?
Yes, this benchmark is reproducible.