Variational Quantum Classifier Benchmark
Variational Quantum Classifier (VQC) · Machine-learning · 8 qubits · Qiskit Machine Learning, PennyLane
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.
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.
Foundational benchmark for quantum machine learning, establishing the circuit-centric classification paradigm used by most subsequent QML research.
Simulator / hardware-agnostic
Qiskit Machine Learning, PennyLane