Phase-Drift Limits and Adaptive Quadrature Readout in Programmable Photonic Processors

• URL: http://arxiv.org/abs/2608.02249v2
• Date: Thu, 06 Aug 2026 10:16:21 GMT
• Title: Phase-Drift Limits and Adaptive Quadrature Readout in Programmable Photonic Processors
• Abstract summary: We study the phase-drift penalty that arises when sine and cosine quadratures are measured sequentially rather than simultaneously.<n>We derive an increment-aware estimator from a local state-space model.<n>For ideal balanced Poisson detection, the Fisher information of each quadrature equals its detected signal-photon number.
• Score: 2.4050736024350994
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Phase fluctuations between optical inputs limit programmable photonic processors because their output powers depend on coherent interference. We study the phase-drift penalty that arises when sine and cosine quadratures are measured sequentially rather than simultaneously. The analysis is motivated by measurements from an eight-mode programmable photonic processor, including 35 free-running recordings of 300 s acquired at approximately 125 samples per second per channel. These recordings provide an empirical route for estimating the phase-increment variance at a selected reconfiguration interval. The estimate is defined at the time of the second measurement. For fixed quadrature order, perturbation of the atan2 reconstruction gives $e_{C\to S}=-δ_τ\sin^2φ_0+O(δ_τ^2)$ and $e_{S\to C}=-δ_τ\cos^2φ_0+O(δ_τ^2)$. Writing $Q_τ=\operatorname{Var}(δ_τ)$, uniform phase averaging gives the first-order drift mean-square error $3Q_τ/8$. A phase-predicted ordering rule measures the locally less informative quadrature first and the more informative quadrature second. Its uniform first-order penalty is $(3/8-1/π)Q_τ$, which is 84.9 percent below the fixed-order value. We also derive an increment-aware estimator from a local state-space model. Marginalizing the unknown phase increment increases the variance of a stale phase observation by $Q_τ$, reducing its Fisher information from $I$ to $I/(1+IQ_τ)$. For ideal balanced Poisson detection, the Fisher information of each quadrature equals its detected signal-photon number. This yields dimensionless architecture boundaries in spatial information and phase-increment variance. Nonlinear Monte Carlo simulations validate the perturbative laws, quantify robustness to prediction error, and compare simultaneous, fixed-order, increment-aware, and adaptive receivers under a common noise model.

Related papers

• Phase information beyond entanglement sudden death in coherence-to-entanglement conversion under post-gate noise [1.028961895672321]
  An ideal CNOT maps the phase of a coherent qubit onto the coherence between $ket00$ and $ket11$ of a two-qubit state.<n>We ask how post-gate noise degrades these two quantities, and find that they are not lost together.
  arXiv  Detail & Related papers  (2026-08-15T11:08:57Z)
• What Does a Discrete Diffusion Model Learn? [71.03603607324338]
  discrete diffusion model learns: a denoiser, a score ratio, or a bridge plug-in predictor.<n>We start with a rigorous derivation of the continuous-time Markov chain (CTMC) ELBO for any noising process.<n>Every identity is verified numerically, without approximation, on an exactly solvable model.
  arXiv  Detail & Related papers  (2026-07-06T17:56:11Z)
• Asymptotic-Preserving A Posteriori Analysis of Diffusion and Flow-Matching Samplers [0.6768558752130311]
  Diffusion and flow-matching samplers integrate a learned probability-flow ODE from a large noise scale down to a small terminal floor $_min$, at which the score is stiff and the flow develops a boundary layer.<n>We treat $_min$ as a singular-perturbation parameter and determine which fixed-step samplers are solvable.
  arXiv  Detail & Related papers  (2026-07-05T04:48:33Z)
• Quantum Phaselift [0.0]
  We introduce a lifting-based framework that estimates the rank-one matrix $Z = f fdagger$ rather than estimating $f$ directly.<n>We prove that a $O(1)$ bandwidth suffices for generic signals, leading to substantial savings in controlled operations.<n>We numerically demonstrate that high-quality recovery is possible for the 2D Fermi-Hubbard and 2D transverse-field Ising model signals of size exceeding 100 time points.
  arXiv  Detail & Related papers  (2026-02-09T19:09:57Z)
• DifGa: Differentiable Error Mitigation for Multi-Mode Gaussian and Non-Gaussian Noise in Quantum Photonic Circuits [4.11280059407553]
  DifGa is a framework for continuous-variable quantum photonic circuits operating under Gaussian loss and weak non-Gaussian noise.<n>Noise-aware training using Monte Carlo averaging yields robust generalization, reducing error by more than an order of magnitude compared to Gaussian-trained recovery.
  arXiv  Detail & Related papers  (2025-12-29T13:18:21Z)
• Measurement-induced phase transition for free fermions above one dimension [46.176861415532095]
  Theory of the measurement-induced entanglement phase transition for free-fermion models in $d>1$ dimensions is developed. Critical point separates a gapless phase with $elld-1 ln ell$ scaling of the second cumulant of the particle number and of the entanglement entropy.
  arXiv  Detail & Related papers  (2023-09-21T18:11:04Z)
• Sharper Convergence Guarantees for Asynchronous SGD for Distributed and Federated Learning [77.22019100456595]
  We show a training algorithm for distributed computation workers with varying communication frequency. In this work, we obtain a tighter convergence rate of $mathcalO!!!(sigma2-2_avg!! . We also show that the heterogeneity term in rate is affected by the average delay within each worker.
  arXiv  Detail & Related papers  (2022-06-16T17:10:57Z)
• Some Error Analysis for the Quantum Phase Estimation Algorithms [0.0]
  We characterize the probability of computing the phase values in terms of the consistency error. In the first two cases, we show that in order to obtain the phase value with error less or equal to $2-n$ and probability at least $1-epsilon$, the required number of qubits is $ t geq n + log big. For the third case, we found a similar estimate, but the number of random steps has to be sufficiently large.
  arXiv  Detail & Related papers  (2021-11-19T20:36:16Z)
• Sample Complexity of Asynchronous Q-Learning: Sharper Analysis and Variance Reduction [63.41789556777387]
  Asynchronous Q-learning aims to learn the optimal action-value function (or Q-function) of a Markov decision process (MDP) We show that the number of samples needed to yield an entrywise $varepsilon$-accurate estimate of the Q-function is at most on the order of $frac1mu_min (1-gamma)5varepsilon2+ fract_mixmu_min (1-gamma)$ up to some logarithmic factor.
  arXiv  Detail & Related papers  (2020-06-04T17:51:00Z)
• Analytic Signal Phase in $N-D$ by Linear Symmetry Tensor--fingerprint modeling [69.35569554213679]
  We show that the Analytic Signal phase, and its gradient have a hitherto unstudied discontinuity in $2-D $ and higher dimensions. This shortcoming can result in severe artifacts whereas the problem does not exist in $1-D $ signals. We suggest the use of Linear Symmetry phase, relying on more than one set of Gabor filters, but with a negligible computational add-on.
  arXiv  Detail & Related papers  (2020-05-16T21:17:26Z)
• Quantum Algorithms for Simulating the Lattice Schwinger Model [63.18141027763459]
  We give scalable, explicit digital quantum algorithms to simulate the lattice Schwinger model in both NISQ and fault-tolerant settings. In lattice units, we find a Schwinger model on $N/2$ physical sites with coupling constant $x-1/2$ and electric field cutoff $x-1/2Lambda$. We estimate observables which we cost in both the NISQ and fault-tolerant settings by assuming a simple target observable---the mean pair density.
  arXiv  Detail & Related papers  (2020-02-25T19:18:36Z)
• Differentially Quantized Gradient Methods [53.3186247068836]
  We show that Differentially Quantized Gradient Descent (DQ-GD) attains a linear contraction factor of $maxsigma_mathrmGD, rhon 2-R$. No algorithm within a certain class can converge faster than $maxsigma_mathrmGD, 2-R$.
  arXiv  Detail & Related papers  (2020-02-06T20:40:53Z)

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.