Quantum-Enhanced Single-Parameter Phase Estimation with Adaptive NOON States
- URL: http://arxiv.org/abs/2604.12323v2
- Date: Sat, 18 Apr 2026 19:52:21 GMT
- Title: Quantum-Enhanced Single-Parameter Phase Estimation with Adaptive NOON States
- Abstract summary: NOON states-maximally path-entangled $N$-photon superpositions $(|N,0rangle + |0,Nrangle)/sqrt2$.<n>We present an end-to-end differentiable quantum-optical framework.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Quantum metrology promises phase sensitivity surpassing the shot-noise limit by exploiting entanglement and photon-number correlations. NOON states-maximally path-entangled $N$-photon superpositions $(|N,0\rangle + |0,N\rangle)/\sqrt{2}$ -achieve the Heisenberg limit $1/N$ for single-parameter estimation, as demonstrated experimentally by Afek et al. (2010) using hybrid coherent-plus-squeezed light up to $N=5$. We present an end-to-end differentiable quantum-optical framework-implemented in Strawberry Fields (Killoran et al., 2019) with a TensorFlow backend -that learns optimal circuit parameters by maximising the classical Fisher information (CFI) across all coincidence channels for $N=2,3,4,5$. Starting from proper numerical reproductions of the Afek et al. coincidence fringes, verified by FFT analysis and parity measurements, we apply gradient descent (Adam) to the eight trainable circuit parameters. Raw CFI improvements grow dramatically with photon number: $+153\%$ ($N=2$), $+834\%$ to $+956\%$ ($N=3$), $+829\%$ to $+1598\%$ ($N=4$), and $+1775\%$ ($N=5$), alongside post-selection rate improvements of $+153\%$ to $+3269\%$, and an $8\times$ to $133\times$ improvement in useful measurement events per pulse across $N=2$-$5$. A fundamental inter-channel trade-off is identified at $N=2$ but weakens at higher $N$ where the Afek initialisation is further from optimal. These results provide numerically rigorous benchmarks for adaptive single-parameter quantum sensing and demonstrate that the Afek working point is significantly suboptimal at $N\geq 3$. QFI calculations confirm that the optimised probe reaches $82\%$ of the Heisenberg limit at $N=2$ and improves from $36\%$ to $58\%$ at $N=5$, while useful measurement events per pulse improve by $8\times$ to $133\times$ across all $N$, making quantum-enhanced sensing at $N\geq 3$ experimentally practical.
Related papers
- Sequential Physics-Constrained Neural Operator Forward Modeling for the $\ extit{Norne}$ Reservoir System [3.175986877079391]
We develop a comprehensive framework for sequential surrogate modeling of black-oil reservoir dynamics using neural operators.<n>The application focus is the Norne benchmark reservoir, defined on a heterogeneous $46times112times22$ grid.<n>A 1000-member ensemble runs in under one minute on a single B200 GPU, giving a $sim104times$ wall-clock speedup over the OPM finite-volume simulator.
arXiv Detail & Related papers (2026-05-27T17:18:39Z) - OAM-Induced Lattice Rotation Reveals a Fractional Optimum in Fault-Tolerant GKP Quantum Sensing [0.0]
We establish that orbital-angular-momentum encoding and Gottesman-Kitaev-Preskill (GKP) lattice geometry are structurally coupled.<n>Using an end-to-end differentiable Strawberry Fields--TensorFlow circuit, we jointly optimise $ell$, the lattice aspect ratio $r$, and the finite-energy envelope $$ to maximise quantum Fisher information.
arXiv Detail & Related papers (2026-05-13T09:49:16Z) - Quantum Fisher Information Revealing Parameter Sensitivity in Long-Baseline Neutrino Experiments [3.060426151281043]
Determination of the leptonic CP-violating phase $_mathrmCP$, the atmospheric mixing angle $_23$, and the mass-squared difference $m_312$ constitutes a primary objective of current and next-generation long-baseline neutrino experiments.<n>We employ QFI (QFI) to establish fundamental precision bounds on single- parameter estimation in three-flavor $_to _e$ oscillations.
arXiv Detail & Related papers (2026-02-05T02:20:12Z) - Lambert W Function Framework for Graphene Nanoribbon Quantum Sensing: Theory, Verification, and Multi-Modal Applications [0.0]
We establish a rigorous mathematical framework connecting graphene nanoribbon quantum sensing to the Lambert W function through the finite square well (FSW) analogy.<n>We demonstrate that operating near the branch point at $z = -1/e$ yields sensitivity enhancement factors scaling as $_textenh propto (z - z_c)-1/2$, achieving 35-fold enhancement at $= 0.001$.
arXiv Detail & Related papers (2026-01-15T08:32:47Z) - CarBoN: Calibrated Best-of-N Sampling Improves Test-time Reasoning [62.56541355300587]
We introduce a general test-time calibration framework that adaptively modifies the model toward high-reward reasoning paths.<n>Within this framework, we propose CarBoN, a two-phase method that first explores the solution space and then learns a calibration of the logits.<n>Experiments on MATH-500 and AIME-2024 show that CarBoN improves efficiency, with up to $4times$ fewer rollouts to reach the same accuracy.
arXiv Detail & Related papers (2025-10-17T14:04:37Z) - Beyond likelihood ratio bias: Nested multi-time-scale stochastic approximation for likelihood-free parameter estimation [49.78792404811239]
We study inference in simulation-based models where the analytical form of the likelihood is unknown.<n>We use a ratio-free nested multi-time-scale approximation (SA) method that simultaneously tracks the score and drives the parameter update.<n>We show that our algorithm can eliminate the original bias $Obig(sqrtfrac1Nbig)$ and accelerate the convergence rate from $Obig(beta_k+sqrtfracalpha_kNbig)$.
arXiv Detail & Related papers (2024-11-20T02:46:15Z) - Convergence Rate Analysis of LION [54.28350823319057]
LION converges iterations of $cal(sqrtdK-)$ measured by gradient Karush-Kuhn-T (sqrtdK-)$.
We show that LION can achieve lower loss and higher performance compared to standard SGD.
arXiv Detail & Related papers (2024-11-12T11:30:53Z) - Towards large-scale quantum optimization solvers with few qubits [59.63282173947468]
We introduce a variational quantum solver for optimizations over $m=mathcalO(nk)$ binary variables using only $n$ qubits, with tunable $k>1$.
We analytically prove that the specific qubit-efficient encoding brings in a super-polynomial mitigation of barren plateaus as a built-in feature.
arXiv Detail & Related papers (2024-01-17T18:59:38Z) - Global Variational Quantum Circuits for Arbitrary Symmetric State Preparation [0.0]
We use a variational circuit composed of global one-axis twisting and global rotations to efficiently prepare arbitrary symmetric states.<n>The circuit does not require local addressability or ancilla qubits.<n>We estimate that the protocol achieves fidelities $gtrsim 95%$ in the presence of typical experimental noise levels.
arXiv Detail & Related papers (2023-12-08T14:28:34Z) - Bounding the Width of Neural Networks via Coupled Initialization -- A
Worst Case Analysis [121.9821494461427]
We show how to significantly reduce the number of neurons required for two-layer ReLU networks.
We also prove new lower bounds that improve upon prior work, and that under certain assumptions, are best possible.
arXiv Detail & Related papers (2022-06-26T06:51:31Z) - Quantum Multi-Parameter Adaptive Bayesian Estimation and Application to
Super-Resolution Imaging [1.4222887950206657]
In quantum sensing tasks, the user gets $rho_theta$, the quantum state that encodes $theta$.
Personick found the optimum POVM $Pi_l$ that minimizes the MMSE over all possible measurements.
This result from 1971 is less-widely known than the quantum Fisher information (QFI), which lower bounds the variance of an unbiased estimator.
arXiv Detail & Related papers (2022-02-21T04:12:55Z) - Multiparameter simultaneous optimal estimation with an SU(2) coding
unitary evolution [5.789743084845758]
In a ubiquitous $SU(2)$ dynamics, achieving the simultaneous optimal estimation of multiple parameters is difficult.
We propose a method, characterized by the nested cross-products of the coefficient vector $mathbfX$ of $SU(2)$ generators.
Our work reveals that quantum control is not always functional in improving the estimation precision.
arXiv Detail & Related papers (2022-02-08T06:05:20Z) - A Simple Convergence Proof of Adam and Adagrad [74.24716715922759]
We show a proof of convergence between the Adam Adagrad and $O(d(N)/st)$ algorithms.
Adam converges with the same convergence $O(d(N)/st)$ when used with the default parameters.
arXiv Detail & Related papers (2020-03-05T01:56:17Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.