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arXiv:2609.02715

Quantum amplitude estimation beyond power-of-two schedules

Farrokh Labib

quant-phSubmitted Sep 2, 2026• Announced Sep 3, 2026• 0 citations
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Abstract

Non-adaptive quantum amplitude estimation (QAE) fixes its Grover depths in advance, so every circuit can run in parallel, but it has so far needed more queries than the best adaptive methods. We show that most of this gap comes from two conventional choices: subspace-based post-processing and power-of-two depth ladders. We replace the first by the exact maximum-likelihood estimate, one matrix multiplication per batch of estimates, and the second by a geometric ladder with ratio $r \approx 1.45$. The result is a fully parallel, deterministic-schedule estimator with total query complexity $2.8$-$3.1/\varepsilon$ at 95% confidence for target errors from $3.5\times 10^{-3}$ to $10^{-6}$. This matches the average-case complexity of chebAE, the best benchmarked adaptive method, within statistical uncertainty (with the lower point estimate at every scale tested), beats its maximum-observed complexity by $1.6\times$, and needs a maximum sequential depth of only $0.21/\varepsilon$ against chebAE's $2.9/\varepsilon$. Relative to csAE, the best non-adaptive benchmark, the constants improve by 30-35% at 95% and $1.5$-$1.7\times$ at 99% confidence. The optimal ratio has a simple origin. Doubling is the fastest depth growth at which the data can still tell neighboring candidate values apart, so power-of-two ladders sit at the edge of confusion and must buy reliability with extra shots; a slightly denser ladder checks every scale redundantly. An error-probability analysis reproduces the measured failure rates and locates the optimum. The likelihood formulation extends directly to noise-aware estimation, and uniformly scaling the capped ladder covers the depth-limited regime, realizing the optimal trade-off $M N_{\mathrm{tot}} \approx (0.4$-$0.6)/\varepsilon^2$ within $\sim 1.1\times$ of the schedule's Cramér-Rao limit.

Comments
14 pages, 4 figures. Code, data, and an interactive demo at https://github.com/unitaryfoundation/csAE

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Quantum Amplitude Estimation (QAE) -- a technique by which the amplitude of a given quantum state can be estimated with quadratically fewer queries than by standard sampling -- is a key sub-routine in several important quantum algorithms, including Grover search and Quantum Monte-Carlo methods. An obstacle to implementing QAE in near-term noisy intermediate-scale quantum (NISQ) devices has been the need to perform Quantum Phase Estimation (QPE) -- a costly procedure -- as a sub-routine. This impediment was lifted with various QPE-free methods of QAE, wherein Grover queries of varying depths / powers (often according to a "schedule") are followed immediately by measurements and classical post-processing techniques like maximum likelihood estimation (MLE). Existing analyses as to the optimality of various query schedules in these QPE-free QAE schemes have hitherto assumed noise-free systems. In this work, we analyse QPE-free QAE under common noise models that may afflict NISQ devices and report on the optimality of various query schedules in the noisy regime. We demonstrate that, given an accurate noise characterization of one's system, one must choose a schedule that balances the trade-off between the greater ideal performance achieved by higher-depth circuits, and the correspondingly greater accumulation of noise-induced error.
Quantum Amplitude Estimation promises to accelerate Monte Carlo integration by replacing the classical ε∝N−1/2\varepsilon \propto N^{-1/2}ε∝N−1/2 sampling law with the Heisenberg-limited ε∝N−1\varepsilon \propto N^{-1}ε∝N−1 scaling, but whether any part of that advantage survives on present-day hardware is an empirical question. We report a systematic experimental benchmark of a Maximum-Likelihood Amplitude Estimation QMC pipeline on IonQ trapped-ion processors, comprising 60 hardware trials on IonQ Forte-1 across five settings(5 & 9 qubits, 100-200 shots per depth, linear & exponential amplification schedules, ~113 device-hours), together with 100 trials on IonQ Aria-1 & Forte-1 noise models at 5, 9 & 19 qubits. The pipeline combines controlled-RyR_yRy​ rotational state preparation, Grover-based amplitude amplification and maximum-likelihood inference and is benchmarked against bmax⁡−1 ⁣∫0bmax⁡ ⁣sin⁡2 ⁣x dxb_{\max}^{-1}\!\int_0^{b_{\max}}\!\sin^2\!x\,\mathrm{d}xbmax−1​∫0bmax​​sin2xdx with bmax⁡=π/5b_{\max}=π/5bmax​=π/5 ,given results emerge. First, the noiseless limit it attains ε∝N−0.88\varepsilon \propto N^{-0.88}ε∝N−0.88, close to the Heisenberg-scaling reference, reducing the error 54-74x over a 133x increase in oracle calls. Second, scaling does not survive on hardware. Every noisy backend saturates at an error floor of (2-5)x 10^{-2}, with fitted exponents of 0.04-0.15 on Forte-1 linear schedules and α≤ ⁣0α\le\!0α≤0 on the vendor noise models: beyond the optimal shallow depth, however, deeper amplification provides no sustained accuracy gain and instead returns to the noise floor. Forte-1 hardware also outperformed IonQ's own Aria-1 noise model at every depth m≥2m \ge 2m≥2, indicating that vendor noise models are bad predictors of MLAE accuracy. The practical implication for UQ is that on current devices the useful operating point is a shallow, schedule-tuned amplification depth chosen so that the deepest circuit lands near p~1/2 not the deepest schedule the coherence budget allows.
We demonstrate that the problem of amplitude estimation, a core subroutine used in many quantum algorithms, can be mapped directly to a problem in signal processing called direction of arrival (DOA) estimation. The DOA task is to determine the direction of arrival of an incoming wave with the fewest possible measurements. The connection between amplitude estimation and DOA allows us to make use of the vast amount of signal processing algorithms to post-process the measurements of the Grover iterator at predefined depths. Using an off-the-shelf DOA algorithm called ESPRIT together with a compressed-sensing based sampling approach, we create a phase-estimation free, parallel quantum amplitude estimation (QAE) algorithm with a worst-case sequential query complexity of ∼4.3/ε\sim 4.3/\varepsilon∼4.3/ε and a parallel query complexity of ∼0.26/ε\sim 0.26/\varepsilon∼0.26/ε at 95% confidence. This performance is statistically equivalent and a 16×16\times16× improvement over Rall and Fuller [Quantum 7, 937 (2023)], for sequential and parallel query complexity respectively, which to our knowledge is the best published result for amplitude estimation. The approach presented here provides a simple, robust, parallel method to performing QAE, with many possible avenues for improvement borrowing ideas from the wealth of literature in classical signal processing.
Amplitude estimation, in its original form, is formulated as phase estimation upon the Grover iterate. Subsequent improvements to the algorithm have eliminated the need for phase estimation and introduced low-depth variants that trade speedups for lower circuit depth. We make the key observation that amplitude estimation is equivalent to estimating the energy gap of an effective Hamiltonian, whereby discrete-time evolution is generated by amplitude amplification. This enables us to develop an amplitude estimation algorithm for both Heisenberg-limited and low-depth circuit regimes, inspired by statistical phase estimation techniques developed for early fault-tolerant ground-state energy estimation. In the Heisenberg-limited regime, our approach achieves performance comparable to state-of-the-art methods while using simplified classical post-processing. In the low-depth regime, it obtains optimal query--depth tradeoffs up to polylogarithmic factors, with provable guarantees and improved empirical performance over prior approaches. The resulting protocol is ancilla-free and requires only standard Grover reflections. Due to its flexibility, generality, and robustness, we expect our approach to be a key enabler for a broad range of early fault-tolerant applications.
Quantum Amplitude Estimation (QAE) is a key primitive in quantum computing, but its standard implementation using Quantum Phase Estimation is resource-intensive, requiring a large number of coherent qubits in a single circuit block to achieve high precision. This presents a significant challenge for near-term Noisy Intermediate-Scale Quantum (NISQ) devices. To address this, we introduce the Adaptive Windowed Quantum Amplitude Estimation (AWQAE) framework, a modular, scalable and adaptive approach that decouples estimation precision from the number of physical qubits required in a single circuit. AWQAE operates by iteratively estimating the phase bits in small, fixed-size chunks, using a number of smaller, independent quantum circuits, which are amenable to parallel processing. A key technical contribution of this work is introduction of a phase resolution circuit and an ancilla-guided mechanism that enables accurate chunk assignment and eigenphase reconstruction in the presence of multiple eigenstates. This design is inherently NISQ-friendly, by lowering circuit depth and qubit count per block to reduce decoherence and noise effects. A key component of our approach is a robust classical post-processing algorithm that resolves measurement ambiguities that arise during the iterative process. This post-processing routine uses a least-significant-bit (LSB)-to-most-significant-bit (MSB) correction to reconstruct the full, high-precision phase estimate, ensuring accuracy. By combining a modular quantum-classical loop with an ambiguity-aware reconstruction method, AWQAE offers a powerful and flexible solution for performing high-precision QAE on resource-constrained quantum hardware. Our approach demonstrates enhanced scalability, and adaptability, making it a promising candidate for practical applications of QAE in the NISQ era.
We develop a class of amplitude estimation algorithms with tunable circuit depth MMM and circuit repetitions NNN, requiring neither ancilla qubits nor controlled Grover operations. For additive error εεε in the Grover angle, they attain the nearly optimal query-depth tradeoff M2N∈O~(ε−2)M^2N\in\widetilde{O}(ε^{-2})M2N∈O(ε−2) uniformly over λ∈[0,π/2]λ\in[0,π/2]λ∈[0,π/2], spanning the full range from classical sampling at M=1M=1M=1 to the Heisenberg limit at M=Θ(ε−1)M=Θ(ε^{-1})M=Θ(ε−1). While prior depth-tunable work establishes comparable angle-accuracy guarantees only away from the boundaries λ→0λ\to0λ→0 or π/2π/2π/2 or at discrete depth tradeoffs, our guarantee extends to both boundaries, so the quantum speedup persists there rather than degrading to classical sampling. Numerical experiments confirm the predicted uniform angle accuracy and show low overhead in practice, making them strong candidates for practical amplitude estimation in the early fault-tolerant regime, with applications such as overlap certification, trial-state verification, and Monte Carlo methods.