Selective Precision: Engineered Accuracy

Updated 19 November 2025

Selective Precision is an engineering paradigm that tailors measurement and inference to optimize accuracy by focusing resources where highest fidelity is required.
It integrates targeted pulse shaping, mode-selective measurements, and optimization algorithms across quantum sensing, time-frequency estimation, manufacturing, and neural networks.
These strategies yield practical gains such as achieving quantum limits in sensing, up to 34× estimation enhancements, and sub-micron precision in microfabrication.

Selective Precision denotes strategies and mechanisms by which measurement, inference, or processing systems are engineered to achieve the highest possible accuracy for particular components, directions, features, or operations—while selectively suppressing, abstaining from, or deprioritizing others. Across domains including quantum sensing, manufacturing, control, neural computation, and probabilistic inference, selective precision enables performance beyond conventional uniform approaches, focusing limited resources exactly where highest fidelity is required. This article synthesizes theoretical principles, core technologies, and leading application domains behind selective precision, emphasizing arXiv-grounded methods and experiments.

1. Quantum Metrology and Sensing: Protocol Engineering for Selective Precision

In quantum sensing, selective precision is realized via Hamiltonian and measurement engineering—coherently isolating sensitivity to chosen parameters or signal subspaces. Zhuang et al. (Zhuang et al., 2023) introduce a protocol for quantum vector DC magnetometry achieving selective phase accumulation of only one magnetic field component ( $B_\alpha$ , $\alpha=x,y,z$ ) in a Ramsey interferometer. By embedding rapid, alternating $\pm\pi_\alpha$ pulse sequences during the interrogation interval, only the desired spin component accumulates phase, effectively suppressing cross-sensitivity to orthogonal field directions.

Two estimation protocols are described:

Parallel scheme: Runs three interferometers, each isolating a specific $B_\alpha$ , resulting in strictly decomposed Fisher information matrices and independent optimal bounds for each direction.
Sequential scheme: Employs a single Ramsey sequence partitioned into three blocks (pulse-engineered per direction), producing beat signals with multi-frequency Fourier components. Algebraic inversion retrieves all $B_\alpha$ from one measurement run.

For separable probes, the standard quantum limit (SQL) $\Delta B_\alpha \sim 1/(\sqrt{N}T_\alpha)$ is achieved; for GHZ-type entanglement, the Heisenberg limit $\Delta B_\alpha \sim 1/(N T_\alpha)$ becomes attainable for all directions. Selective phase accumulation thus enables vector parameter estimation at optimal quantum limits with rigorous insensitivity to “off-target” components (Zhuang et al., 2023).

In distributed quantum sensor networks, selective-wave estimation is implemented using spatial control waveforms and preparation of decoherence-free subspaces (DFS) (Hamann et al., 16 Dec 2024). Temporal lock-in via waveform C $_i$ (t) isolates sensitivity to a chosen spectral mode or direction, while entangled GHZ probes prepared in these DFSs cancel multi-mode correlated noise. Quantum Fisher information scaling advantages ( $1/n^2$ rather than $1/n$) and exponential quantum–classical gaps are demonstrated in dense noise regimes.

2. Selective Precision in Time–Frequency Estimation and Quantum Memory

In quantum-limited resolution problems where intrinsic overlap or “Rayleigh curse” constraints appear, mode-selective measurement is critical. In “Quantum-limited time-frequency estimation through mode-selective photon measurement” (Donohue et al., 2018), shaped sum-frequency generation—using Hermite-Gauss mode projectors—is used to resolve temporal or spectral separations directly in quantum-optimal subspaces. When two frequency lines of separation $\Delta\nu \ll \sigma_\nu$ are measured, standard (intensity-only) detection saturates the Cramér–Rao bound as $\Delta\nu\to 0$ ( $\operatorname{Var}(\Delta \nu) \to \infty$ ), but mode-selective HG $_0$ /HG $_1$ projective measurements attain the constant quantum limit $\operatorname{Var}(\Delta\nu) = (4\sigma_\nu^2)/N$ independent of $\Delta\nu$ .

Further, mode-selective atomic memories (e.g., Raman memory in Cs vapor) demonstrate sub-linewidth frequency discrimination with crosstalk $<0.34\%$ and up to $34\pm 4\times$ quantum enhancement in estimation precision compared to intensity-based methods (Zhang et al., 25 Jun 2025). These platforms generalize to super-resolving time–frequency metrology, multi-parameter estimation, and quantum networking architectures.

3. Selective Precision in Sensing, Control, and Estimation

Sensor selection and precision scheduling for state estimation problems are directly addressed through convex optimization and greedy heuristics (Deshpande et al., 2021). For linear time-invariant (LTI) systems, one jointly selects which sensors to deploy and assigns minimal precisions $p_i = 1/\delta_i^2$ per sensor, subject to global constraints on $\mathcal{H}_2$ or $\mathcal{H}_\infty$ estimation error norms:

$\min_{P, R} \quad \mathrm{trace}(W R) \quad \text{s.t. LMIs encoding error bounds and} \; R = \mathrm{diag}(p_i) \succeq 0$

An alternating direction method of multipliers (ADMM) solver efficiently splits the problem into tractable substeps, handling problems at scale. Greedy Sensor Elimination is further introduced to enforce cardinality constraints, systematically pruning sensors with least cost impact. These integrated approaches deliver resource-optimal selective precision: sensors and exacting specifications only where coverage is valuable, with guaranteed global error bounds (Deshpande et al., 2021).

In selective position estimation for 5G V2X networks, dynamic switching between GNSS and cellular TDOA positioning (via SPNTV) enhances availability at strict error targets (≤3 m with 76% availability), and fusing with context-aware strategies (e.g., roadside units) recovers performance in adverse conditions (Fouda et al., 2021).

4. Selective Manufacturing and Microfabrication

Manufacturing applications leverage selective energy or material addition/removal to realize complex structures with localized, controlled high-precision features that are unattainable with uniform methods.

Selective Laser Melting (SLM) and Etching (SLE): Additive manufacturing with SLM enables topologically complex, lightweight mirror bodies (mass reduction up to 63.5%) while maintaining optical-grade precision after post-processing (form error < 150 nm PV, roughness < 1 nm RMS) (Hilpert et al., 2018). Selective laser etching in ULE glass exploits ultrafast pulse-induced nanograting formation for micron/submicron 3D feature creation (etch selectivity S=100–200; dimensional tolerance ±0.27 µm over cm-scale) (Casamenti et al., 28 Jun 2024).
Ultrafast Selective Ablation: In thin film patterning, sub-picosecond laser ablation with high-electron-phonon-coupling interlayers (e.g., Ti) achieves selective, damage-free metal removal at high spatial resolution (<5 µm) and minimal substrate heating, realized via ultrafast two-temperature dynamics (Kim et al., 2020).

5. Selective Precision in Neural and Probabilistic Computation

Selective Quantization: In deep neural networks, selective precision consists of assigning variable bit-widths per layer to optimally balance memory, accuracy, and latency. By ranking layers by activation-sensitivity and iteratively producing models exempting most-sensitive layers, one constructs Pareto-optimal trade-offs. Empirical results indicate up to 54% reduction in accuracy loss compared to full quantization, and model size reductions of up to 72.9% (Louloudakis et al., 16 Jul 2025).
Selective Classification and Abstention: Selective classifiers abstain on inputs with high model uncertainty, thereby selectively achieving high precision at the accepted inputs. The coverage–risk curve is bounded by the “selective classification gap,” decomposable into irreducible Bayes noise, approximation error, ranking error, statistical noise, and shift-induced slack (Rabanser et al., 23 Oct 2025). Empirical and theoretical analyses show monotone calibration alone cannot tighten the coverage–risk gap; instead, only feature-aware confidence scoring, deep ensembles, and robust learning meaningfully close the selective precision deficit.

6. Selective Precision via Statistical Post-Processing and Context-aware Inference

Selective error control mechanisms—prominently in statistical learning and object detection—enable provable precision guarantees for selected outputs. In ovarian follicle counting, Learn-Then-Test (LTT) postprocessing selects confidence and context thresholds to ensure, with high probability ( $1-\delta$ ), that realized prediction precision exceeds a target $P_0$ on any new data (Blot et al., 23 Jan 2025). Multiple-testing frameworks, enriched with domain-specific criteria (e.g., biological position) or auxiliary classifiers, allow maximal recall while provably enforcing high precision, and are model-agnostic—requiring no retraining.

7. Spatial and Temporal Selectivity in Experimental Physics

Spatially-selective probe shaping via DMD/SLM devices enables in-situ, high-precision, arbitrary-region magnetometry in ultracold atomic clouds (Elíasson et al., 2018). Region-of-interest (ROI)-tailored dispersive probing and pixel-resolved detection permit precision measurements down to 27 nT Hz $^{-1/2}$ sensitivity over micrometer-scale patches, scalable to arbitrary geometries and simultaneously supporting experimental feedback in high-resolution quantum simulation platforms.

Summary Table: Representative Domains of Selective Precision

Field/Technology	Selective Precision Mechanism	Performance/Advantage
Quantum Magnetometry	Pulse-engineered Ramsey interferometry	SQL/HL bounds for chosen vector component (Zhuang et al., 2023)
Microfabrication	Ultrafast pulse SLE in ULE glass; SLM in metals	Micron-scale 3D, sub-micron accuracy (Casamenti et al., 28 Jun 2024, Hilpert et al., 2018)
Signal Estimation	Mode-selective quantum memory/photon measurement	>30× precision enhancement; sub-bandwidth resolution (Zhang et al., 25 Jun 2025, Donohue et al., 2018)
Control & Estimation	Convex sensor selection & scheduling	Minimal total precision for H₂/H∞ bounds (Deshpande et al., 2021)
Deep Learning	Per-layer/region quantization; abstention/postprocessing	Pareto-optimal accuracy/size; provable error rates (Louloudakis et al., 16 Jul 2025, Rabanser et al., 23 Oct 2025, Blot et al., 23 Jan 2025)

Selective precision is thus a unifying paradigm exploiting physical control, architectural tailoring, statistical rigor, and context-awareness to maximize measurement or inference fidelity where most needed, while actively suppressing unwanted or low-priority components. This selects sensitivity or accuracy in directions, regions, parameters, or output subsets critical for a given application, with broad implications for quantum sensing, modern AI, experimental physics, and manufacturing.