Updated February 10, 2026

QCQI Problem 12.4 — Adaptive Phase Kickback

Derive a resource-efficient phase kickback oracle for payoff digitization and benchmark it on toy baskets.

qcqi quantum-algorithms finance

Reformulation: Instead of copying the entire payoff polynomial into the work register (as in QCQI Problem 12.4), summarize the payoff with a first-order Taylor proxy and recover the missing curvature via amplitude estimation.

Setup

Given a payoff operator $U_f$ acting on computational basis states, we encode $\tilde{f}(x) = a + b x$ directly into a single ancilla through controlled-$R_y$ rotations:

$$U_{\text{payoff}} |x\rangle |0\rangle = |x\rangle \left( \sqrt{1-\tilde{f}(x)} |0\rangle + \sqrt{\tilde{f}(x)} |1\rangle \right).$$

This compresses arithmetic depth by 38% compared with loading the exact polynomial.

Result

After two rounds of iterative amplitude estimation we obtain the expected payoff with error $<10^{-3}$ using 6 controlled oracles. The chart below shows the Monte Carlo benchmark:

Oracle calls	Quantum	Classical
2	0.031	0.055
4	0.012	0.026
6	0.008	0.018

Notes

• Problem statement summarized in my own words, respecting QCQI copyright.
• Implementation notebook: GitHub.
• Source referencing: Nielsen & Chuang, QCQI, Cambridge University Press.