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This research introduces a novel quantum amplitude amplification protocol that integrates weak measurements into Grover search loops to achieve oracle savings and adaptive handling of unknown target probabilities. By deriving closed-form expressions for expected iteration costs, the study demonstrates how tunable measurement strengths can improve leading constants while preserving the fundamental $\Theta(1/\sqrt{p})$ search scaling. These findings offer a rigorous framework for optimizing quantum search efficiency in scenarios where prior knowledge of solution density is limited or absent.
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Weakly Measured Loops for Quantum Amplitude Amplification: Oracle Savings and Adaptive Search with Unknown Target Probability

arXiv2 min read

arXiv:2609.39621v1 Announce Type: new Abstract: We present a family of quantum amplitude amplification algorithms that interleave ordinary Grover rotations with tunable weak measurements in loops. A successful measurement yields the target state and stops the algorithm, while after a failure, the loop resumes from the post-measurement state without restarting. We express the leading costs in expected Grover iterations as $p \to 0$. For a known target probability $p$, an exact weak measurement-conditioned loop for states near the angle $\pi/4$ uses $(\pi/8+1/4+o(1))/\sqrt{p}$ iterations, improving on the standard Grover constant $\pi/4$ and on optimised restart Grover, and matching the corresponding infimum in our continuous-angle analysis. When only a lower bound $0 2$, we rigorously derive a closed-form Gamma-function expression $C(b)$ giving $(C(b)/2+o(1))/\sqrt{p}$ expected iterations. Numerical minimisation of this explicit expression gives $b \approx 5.2$ and $C(b)/2 \approx 1.01$. The results show that weak measurements preserve the $\Theta(1/\sqrt{p})$ search scale while adding an explicit fixed-point control mechanism and provable oracle savings.

Abstract:We present a family of quantum amplitude amplification algorithms that interleave ordinary Grover rotations with tunable weak measurements in loops. A successful measurement yields the target state and stops the algorithm, while after a failure, the loop resumes from the post-measurement state without restarting. We express the leading costs in expected Grover iterations as $p \to 0$. For a known target probability $p$, an exact weak measurement-conditioned loop for states near the angle $\pi/4$ uses $(\pi/8+1/4+o(1))/\sqrt{p}$ iterations, improving on the standard Grover constant $\pi/4$ and on optimised restart Grover, and matching the corresponding infimum in our continuous-angle analysis. When only a lower bound $0 < p_0 \leq p < 1/2$ is available, a discrete Lyapunov equation gives the exact expected oracle cost for every fixed measurement strength and a closed-form optimal strength. Using the strength selected from $p_0$, the expected number of Grover iterations is at most $\frac{1}{2\sqrt{2}}(\frac{1}{\sqrt{p}}+\frac{1}{\sqrt{p_0}})$, where the expected cost decreases as the actual $p$ increases, and gives the leading constant $1/\sqrt{2}$ at the promise boundary $p = p_0$. For completely unknown $p$, we identify measurement strength $\kappa(t)=\Theta(1/t)$ as the critical scale within the regular schedules considered here and analyse $\kappa_b(t)=\min\{1/2,b/t\}$. For each fixed $b > 2$, we rigorously derive a closed-form Gamma-function expression $C(b)$ giving $(C(b)/2+o(1))/\sqrt{p}$ expected iterations. Numerical minimisation of this explicit expression gives $b \approx 5.2$ and $C(b)/2 \approx 1.01$. The results show that weak measurements preserve the $\Theta(1/\sqrt{p})$ search scale while adding an explicit fixed-point control mechanism and provable oracle savings.

arXiv:2609.39621 [quant-ph]

(or arXiv:2609.39621v1 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2609.39621 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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