Symbol C
C is part of the quantity the equation computes from the expression on the right.
Read this term in its guide →Published equation contexts
Why this formula appears here
For a machine-learning task, the near-term architecture almost universally used is the parameterized (or variational) quantum circuit, and its mechanism has four concrete steps. First, classical input data x is encoded into a quantum state by applying a data-dependent unitary V(x) to a fixed initial state — commonly done by rotating each qubit by an angle set from one feature of x, so the input vector is literally written into rotation angles. Second, an “ansatz” unitary U(θ) — a fixed sequence of parameterized single-qubit rotation gates and two-qubit entangling gates, repeated over several layers — is applied, where θ is now a set of continuously adjustable classical numbers rather than…
Read the representative guide
Symbol θ
θ is an argument of the function-like quantity on the left; its role is set by that function’s stated inputs.
Read this term in its guide →Symbol psi_0
ps is an input to the expression that computes the quantity on the left.
Read this term in its guide →Symbol V^dagger
agger is an input to the expression that computes the quantity on the left.
Read this term in its guide →Symbol x
x is an input to the expression that computes the quantity on the left.
Read this term in its guide →Symbol U^dagger
agger is an input to the expression that computes the quantity on the left.
Read this term in its guide →Symbol U
U is an input to the expression that computes the quantity on the left.
Read this term in its guide →Symbol V
V is an input to the expression that computes the quantity on the left.
Read this term in its guide →How to interpret it
Read it with the definitions, units, and assumptions supplied by the article.
Research cited beside this formula
Published contexts (1)
A symbol can carry a different meaning in another article. Each occurrence keeps its own guide and term definitions.
Equation 4 · Future Hardware
How Post-CMOS, Neuromorphic, Photonic, and Quantum AI Compute Actually Works
This equation states an equality: the expressions on both sides have the same value under the article’s assumptions.
For a machine-learning task, the near-term architecture almost universally used is the parameterized (or variational) quantum circuit, and its mechanism has four concrete steps. First, classical input data x is encoded into a quantum state by applying a data-dependent unitary V(x) to a fixed initial state — commonly done by rotating each qubit by an angle set from one feature of x, so the input vector is literally written into rotation angles. Second, an “ansatz” unitary U(θ) — a fixed sequence of parameterized single-qubit rotation gates and two-qubit entangling gates, repeated over several layers — is applied, where θ is now a set of continuously adjustable classical numbers rather than…