A variational quantum model is a small parameterized circuit inside an ordinary training loop. Classical code prepares features, updates parameters, and scores a loss. The quantum step only turns a state into samples or expectation values.
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Lesson close · Quantum Machine Learning
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Hypotheses. The device (or simulator) returns estimates of from finitely many shots, not the vector . is stored and updated classically. A holdout split is applied to classical pairs before any claim about generalization.
The loop is the same shape as a neural net with a peculiar layer. Features are computed on a CPU. Encoding writes them into gate angles or into amplitudes. The ansatz is a product of gates whose angles are . Measuring — often a Pauli on one qubit — yields a number in if is that Pauli. A batch loss might be
The optimizer proposes from and from estimates of . Those estimates are classical post-processing of shot averages (parameter shifts or finite differences).
Why the optimizer stays classical: the device returns shot estimates of , not a stored gradient you can iterate, and the data set does not fit in the register. Two evaluations at the same differ by shot noise. Different shot budgets are not the same experiment.
A research check “does this feature set beat logistic regression on a later month?” is this pipeline with a time-ordered holdout. The quantum piece is only and . The comparison is classical, on the same metric and the same dates.
Interactive · classical↔quantum split
quantum=0.50 · classical=0.50
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