Parameterized Quantum Circuit
A parameterized quantum circuit (PQC) is a quantum circuit governed by trainable parameters, commonly used in quantum machine learning as a model architecture that learns data patterns by adjusting gate parameters.
Timeline (last 90 days)
Parameterized quantum circuits (PQCs) have attracted much attention in quantum machine learning, yet how their architectures provide task-aligned inductive biases remains poorly understood to date.
Data re-uploading is a circuit design pattern in which the same set of data is encoded into the circuit multiple times, with trainable parameterized gates inserted between each encoding.
For parameterized quantum circuits, the inductive bias is jointly determined by the encoding scheme, the gate set, the entanglement structure and the measurement scheme.
A parameterized quantum circuit consists of a series of tunable rotation gates and entanglement gates, and its output is the expectation value of the quantum state after measurement.
All Facts (4)
Parameterized quantum circuits (PQCs) have attracted much attention in quantum machine learning, yet how their architectures provide task-aligned inductive biases remains poorly understood to date.
50%UnverifiedData re-uploading is a circuit design pattern in which the same set of data is encoded into the circuit multiple times, with trainable parameterized gates inserted between each encoding.
50%UnverifiedFor parameterized quantum circuits, the inductive bias is jointly determined by the encoding scheme, the gate set, the entanglement structure and the measurement scheme.
50%UnverifiedA parameterized quantum circuit consists of a series of tunable rotation gates and entanglement gates, and its output is the expectation value of the quantum state after measurement.
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