arXiv subjects

Ahmed Shokry

Publications and source records attributed to Ahmed Shokry.

At least 19 recordsLinked to original sources

arXiv2026

Structure-Preserving Quantum Simulation of Wave Equations on a Trapped-Ion Processor

Wave equations provide a natural testbed for near-term quantum simulation of partial differential equations, but hardware demonstrations have remained limited in spatial dimension, equation class, system size, and physically meaningful output. We develop and benchmark structure-preserving, Fourier-based quantum circuits for the one- and two dimensional acoustic wave equations and Dirac dynamics with variable mass on the Quantinuum H2-2 trapped-ion processor. The experiments include one-dimensional grids with up to $1024$ points and $32\times32$ two-dimensional grids, corresponding to an encoded state-space dimension of up to $4096$. Rather than reconstructing the full fields, we estimate subdomain kinetic energies directly from measurement samples. Across all tested acoustic and Dirac dynamics problems, the H2-2 results track the classical kinetic-energy dynamics with mean absolute errors between $5.9\times10^{-3}$ and $2.4\times10^{-2}$. At fixed retained bandwidth, the compiled gate counts grow approximately quadratically with the number of grid qubits; the acoustic circuit sizes are essentially independent of evolution time, whereas the cost also grows with the number of product-formula steps. These results provide hardware-level evidence that accurate observable dynamics can remain resolvable for structured wave problems with thousands of encoded degrees of freedom on a present-day trapped-ion processor.

quant-ph↗

arXiv2026

Implicit-Behavior Coordination from Unlabeled Sub-Task Demonstrations for Rearrangement Tasks

Long-horizon robotic rearrangement tasks are often treated as skill sequencing problems, requiring predefined skills, skill labels, or boundaries, and task-specific switching logic. Although effective, such explicit skill abstractions can become difficult to scale as the number of behaviors and the task horizon increase. We instead formulate rearrangement as implicit-behavior coordination from unlabeled sub-task demonstrations, where skill-like behaviors are learned directly from mixed behavior data and coordinated through value-guided action selection. Experiments in Habitat rearrangement tasks support this formulation in three ways. First, our method outperforms task-specific imitation baselines on more complex rearrangement tasks and approaches an oracle-planner baseline with behavior-cloned skills, while using no oracle task plan or skill-labeled full-task demonstrations. Second, ablations show that reliable critic-guided candidate selection is essential for coordinating multi-modal behaviors. Third, scaling experiments show that the method handles larger behavior repertoires and maintains stronger performance than task-specific imitation baselines as chained targets extend the horizon. These results suggest that explicit skill abstraction is not a prerequisite for long-horizon rearrangement, and that implicit-behavior coordination offers a promising data-driven alternative to explicit skill-based pipelines.

cs.RO↗

arXiv2026

Self-Organized Conformal Prediction: Reducing Regional Coverage Gaps with Unsupervised Group Discovery

Conformal prediction guarantees marginal coverage, but a pooled calibration quantile can hide systematic undercoverage across heterogeneous regions of the feature space. We introduce Self-Organized Conformal Prediction (SOCP), a calibration scheme that discovers input-space groups with an unsupervised Self-Organizing Map (SOM) trained without calibration labels. At prediction time, the query's best-matching unit (BMU) draws a calibration buffer from one cell, a fixed grid neighborhood, or a prototype-based enlargement. When fixed neighborhoods are too sparse, Regime 3 adds cells by prototype distance, using a global budget selected from training-cell occupancies and the planned calibration size before any calibration score is observed. The predictor and nonconformity score remain unchanged. Cell-only retrieval has exact cell-conditional validity, and each fixed union of cells has exact retrieved-set validity. Interpreting a neighborhood threshold at its central cell incurs an explicit Kolmogorov-Smirnov (KS) bias term. Across ten regression and classification benchmarks, SOCP reduces the weighted coverage gap relative to pooled split conformal prediction on nine datasets. The mean relative change is $-14.3\%$, at a mean output-size change of $+3.0\%$. Under fixed-neighborhood retrieval, SO composition lowers the ten-seed mean WCovGap in $43$ of the $50$ dataset-score comparisons, while SO-SCP lowers it on average over paired seeds for every dataset at all three tested external partition granularities. These results provide a concise route to group-local calibration without supervised partitions or predictor retraining with a diagnostic toolkit, while keeping the cost and limits of locality explicit.

stat.ML↗

arXiv2026

Efficient and Practical Black-Box Verification of Quantum Metric Learning Algorithms

Quantum metric learning enhances machine learning by mapping classical data to a quantum Hilbert space with maximal separation between classes. However, on current NISQ hardware, this mapping process itself is prone to errors and could be fundamentally incorrect. Verifying that a quantum embedding model successfully achieves its promised separation is essential to ensure the correctness and reliability. In this paper, we propose a practical black-box verification protocol to audit the performance of quantum metric learning models. We define a setting with two parties: a powerful but untrusted prover, who claims to have a parameterized unitary circuit that embeds classical data from different groups with a guaranteed angular separation, and a limited verifier, whose quantum capabilities are restricted to performing only basic measurements. The verifier has no knowledge of the implementation of the prover, including the structure of the model, its parameters, or the details of the prover measurement setup. To verify the separation between different data groups, the proposed algorithm must overcome two key challenges. First, the verifier is ignorant of the prover's implementation details, such as the optimization cost function and measurement setup. Consequently, the verifier lacks any prior information about the expected quantum embedding states for each group. Second, the destructive nature of quantum measurements prevents direct estimation of the separation angles. Our algorithm successfully overcomes these challenges, enabling the verifier to accurately estimate the true separation angles between the different groups. We implemented the proposed protocol and deployed it to verify the QAOAEmbedding models. The results from both theoretical analysis and practical implementation show that our proposal effectively assesses embedding quality and remains robust in adversarial settings.

quant-ph↗

arXiv2026

Quantum Optimization for Access Point Selection Under Budget Constraint

Optimal Access Point (AP) selection is crucial for accurate indoor localization, yet it is constrained by budget, creating a trade-off between localization accuracy and deployment cost. Classical approaches to AP selection are often computationally expensive, hindering their application in large-scale 3D indoor environments. In this paper, we introduce a quantum APs selection algorithm under a budget constraint. The proposed algorithm leverages quantum annealing to identify the most effective subset of APs allowed within a given budget. We formulate the APs selection problem as a quadratic unconstrained binary optimization (QUBO) problem, making it suitable for quantum annealing solvers. The proposed technique can drastically reduce infrastructure requirements with a negligible impact on performance. We implement the proposed quantum algorithm and deploy it in a realistic 3D testbed. Our results show that the proposed approach can reduce the number of required APs by 96.1% while maintaining a comparable 3D localization accuracy. Furthermore, the proposed quantum approach outperforms classical AP selection algorithms in both accuracy and computational speed. Specifically, our technique achieves a time of 0.20 seconds, representing a speedup of 61 times over its classical counterpart, while reducing the mean localization error by 10% compared to the classical counterpart. For floor localization, the quantum approach achieves 73% floor accuracy, outperforming both the classical AP selection (58.6%) and even using the complete set of APs (70.4%). This highlights the promise of the proposed quantum APs selection algorithm for large-scale 3D localization.

cs.ET↗

arXiv2025

Context-Based Meta Reinforcement Learning for Robust and Adaptable Peg-in-Hole Assembly Tasks

Autonomous assembly is an essential capability for industrial and service robots, with Peg-in-Hole (PiH) insertion being one of the core tasks. However, PiH assembly in unknown environments is still challenging due to uncertainty in task parameters, such as the hole position and orientation, resulting from sensor noise. Although context-based meta reinforcement learning (RL) methods have been previously presented to adapt to unknown task parameters in PiH assembly tasks, the performance depends on a sample-inefficient procedure or human demonstrations. Thus, to enhance the applicability of meta RL in real-world PiH assembly tasks, we propose to train the agent to use information from the robot's forward kinematics and an uncalibrated camera. Furthermore, we improve the performance by efficiently adapting the meta-trained agent to use data from force/torque sensor. Finally, we propose an adaptation procedure for out-of-distribution tasks whose parameters are different from the training tasks. Experiments on simulated and real robots prove that our modifications enhance the sample efficiency during meta training, real-world adaptation performance, and generalization of the context-based meta RL agent in PiH assembly tasks compared to previous approaches.

cs.RO↗

arXiv2025

torchsom: The Reference PyTorch Library for Self-Organizing Maps

This paper introduces torchsom, an open-source Python library that provides a reference implementation of the Self-Organizing Map (SOM) in PyTorch. This package offers three main features: (i) dimensionality reduction, (ii) clustering, and (iii) friendly data visualization. It relies on a PyTorch backend, enabling (i) fast and efficient training of SOMs through GPU acceleration, and (ii) easy and scalable integrations with PyTorch ecosystem. Moreover, torchsom follows the scikit-learn API for ease of use and extensibility. The library is released under the Apache 2.0 license with 90% test coverage, and its source code and documentation are available at https://github.com/michelin/TorchSOM.

stat.ML↗

arXiv2025

Clustering by Attention: Leveraging Prior Fitted Transformers for Data Partitioning

Clustering is a core task in machine learning with wide-ranging applications in data mining and pattern recognition. However, its unsupervised nature makes it inherently challenging. Many existing clustering algorithms suffer from critical limitations: they often require careful parameter tuning, exhibit high computational complexity, lack interpretability, or yield suboptimal accuracy, especially when applied to large-scale datasets. In this paper, we introduce a novel clustering approach based on meta-learning. Our approach eliminates the need for parameter optimization while achieving accuracy that outperforms state-of-the-art clustering techniques. The proposed technique leverages a few pre-clustered samples to guide the clustering process for the entire dataset in a single forward pass. Specifically, we employ a pre-trained Prior-Data Fitted Transformer Network (PFN) to perform clustering. The algorithm computes attention between the pre-clustered samples and the unclustered samples, allowing it to infer cluster assignments for the entire dataset based on the learned relation. We theoretically and empirically demonstrate that, given just a few pre-clustered examples, the model can generalize to accurately cluster the rest of the dataset. Experiments on challenging benchmark datasets show that our approach can successfully cluster well-separated data without any pre-clustered samples, and significantly improves performance when a few clustered samples are provided. We show that our approach is superior to the state-of-the-art techniques. These results highlight the effectiveness and scalability of our approach, positioning it as a promising alternative to existing clustering techniques.

cs.LG↗

arXiv2025

A Dynamic Safety Shield for Safe and Efficient Reinforcement Learning of Navigation Tasks

Reinforcement learning (RL) has been successfully applied to a variety of robotics applications, where it outperforms classical methods. However, the safety aspect of RL and the transfer to the real world remain an open challenge. A prominent field for tackling this challenge and ensuring the safety of the agents during training and execution is safe reinforcement learning. Safe RL can be achieved through constrained RL and safe exploration approaches. The former learns the safety constraints over the course of training to achieve a safe behavior by the end of training, at the cost of high number of collisions at earlier stages of the training. The latter offers robust safety by enforcing the safety constraints as hard constraints, which prevents collisions but hinders the exploration of the RL agent, resulting in lower rewards and poor performance. To overcome those drawbacks, we propose a novel safety shield, that combines the robustness of the optimization-based controllers with the long prediction capabilities of the RL agents, allowing the RL agent to adaptively tune the parameters of the controller. Our approach is able to improve the exploration of the RL agents for navigation tasks, while minimizing the number of collisions. Experiments in simulation show that our approach outperforms state-of-the-art baselines in the reached goals-to-collisions ratio in different challenging environments. The goals-to-collisions ratio metrics emphasizes the importance of minimizing the number of collisions, while learning to accomplish the task. Our approach achieves a higher number of reached goals compared to the classic safety shields and fewer collisions compared to constrained RL approaches. Finally, we demonstrate the performance of the proposed method in a real-world experiment.

cs.RO↗

arXiv2024

Verifying Shortest Paths in Linear Time

In this paper we propose a linear-time certifying algorithm for the single-source shortest-path problem capable of verifying graphs with positive, negative, and zero arc weights. Previously proposed linear-time approaches only work for graphs with positive arc weights.

cs.DS↗

arXiv2024

An Efficient Quantum Binary-Neuron Algorithm for Accurate Multi-Story Floor Localization

Accurate floor localization in a multi-story environment is an important but challenging task. Among the current floor localization techniques, fingerprinting is the mainstream technology due to its accuracy in noisy environments. To achieve accurate floor localization in a building with many floors, we have to collect sufficient data on each floor, which needs significant storage and running time; preventing fingerprinting techniques from scaling to support large multi-story buildings, especially on a worldwide scale. In this paper, we propose a quantum algorithm for accurate multi-story localization. The proposed algorithm leverages quantum computing concepts to provide an exponential enhancement in both space and running time compared to the classical counterparts. In addition, it builds on an efficient binary-neuron implementation that can be implemented using fewer qubits compared to the typical non-binary neurons, allowing for easier deployment with near-term quantum devices. We implement the proposed algorithm on a real IBM quantum machine and evaluate it on three real indoor testbeds. Results confirm the exponential saving in both time and space for the proposed quantum algorithm, while keeping the same localization accuracy compared to the traditional classical techniques, and using half the number of qubits required for other quantum localization algorithms.

quant-ph↗

arXiv2024

Handling Device Heterogeneity for Deep Learning-based Localization

Deep learning-based fingerprinting is one of the current promising technologies for outdoor localization in cellular networks. However, deploying such localization systems for heterogeneous phones affects their accuracy as the cellular received signal strength (RSS) readings vary for different types of phones. In this paper, we introduce a number of techniques for addressing the phones heterogeneity problem in the deep-learning based localization systems. The basic idea is either to approximate a function that maps the cellular RSS measurements between different devices or to transfer the knowledge across them. Evaluation of the proposed techniques using different Android phones on four independent testbeds shows that our techniques can improve the localization accuracy by more than 220% for the four testbeds as compared to the state-of-the-art systems. This highlights the promise of the proposed device heterogeneity handling techniques for enabling a wide deployment of deep learning-based localization systems over different devices.

cs.CY↗

arXiv2024

DeepCell: A Ubiquitous Accurate Provider-side Cellular-based Localization

Although outdoor localization is already available to the general public and businesses through the wide spread use of the GPS, it is not supported by low-end phones, requires a direct line of sight to satellites and can drain phone battery quickly. The current fingerprinting solutions can provide high-accuracy localization but are based on the client side. This limits their ubiquitous deployment and accuracy. In this paper, we introduce DeepCell: a provider-side fingerprinting localization system that can provide high accuracy localization for any cell phone. To build its fingerprint, DeepCell leverages the unlabeled cellular measurements recorded by the cellular provider while opportunistically synchronizing with selected client devices to get location labels. The fingerprint is then used to train a deep neural network model that is harnessed for localization. To achieve this goal, DeepCell need to address a number of challenges including using unlabeled data from the provider side, handling noise and sparsity, scaling the data to large areas, and finally providing enough data that is required for training deep models without overhead. Evaluation of DeepCell in a typical realistic environment shows that it can achieve a consistent median accuracy of 29m. This accuracy outperforms the state-of-the-art client-based cellular-based systems by more than 75.4%. In addition, the same accuracy is extended to low-end phones.

cs.CY↗

arXiv2024

An Efficient Quantum Euclidean Similarity Algorithm for Worldwide Localization

Fingerprinting techniques are widely used for localization because of their accuracy, especially in the presence of wireless channel noise. However, the fingerprinting techniques require significant storage and running time, which is a concern when implementing such systems on a global worldwide scale. In this paper, we propose an efficient quantum Euclidean similarity algorithm for wireless localization systems. The proposed quantum algorithm offers exponentially improved complexity compared to its classical counterpart and even the state-of-the-art quantum localization systems, in terms of both storage space and running time. The basic idea is to entangle the test received signal strength (RSS) vector with the fingerprint vectors at different locations and perform the similarity calculation in parallel to all fingerprint locations. We give the details of how to construct the quantum fingerprint, how to encode the RSS measurements in quantum particles, and finally; present the quantum algorithm for calculating the Euclidean similarity between the online RSS measurements and the fingerprint ones. Implementation and evaluation of our algorithm in a real testbed using a real IBM quantum machine as well as a simulation for a larger testbed confirm its ability to correctly obtain the estimated location with an exponential enhancement in both time and space compared to the traditional classical fingerprinting techniques and the state-of-the-art quantum localization techniques.

quant-ph↗

arXiv2024

A Deployable Quantum Access Points Selection Algorithm for Large-Scale Localization

Effective access points (APs) selection is a crucial step in localization systems. It directly affects both localization accuracy and computational efficiency. Classical APs selection algorithms are usually computationally expensive, hindering the deployment of localization systems in a large worldwide scale. In this paper, we introduce a quantum APs selection algorithm for large-scale localization systems. The proposed algorithm leverages quantum annealing to eliminate redundant and noisy APs. We explain how to formulate the APs selection problem as a quadratic unconstrained binary optimization (QUBO) problem, suitable for quantum annealing, and how to select the minimum number of APs that maintain the same overall localization system accuracy as the complete APs set. Based on this, we further propose a logarithmic-complexity algorithm to select the optimal number of APs. We implement our quantum algorithm on a real D-Wave Systems quantum machine and assess its performance in a real test environment for a floor localization problem. Our findings reveal that by selecting fewer than 14% of the available APs in the environment, our quantum algorithm achieves the same floor localization accuracy as utilizing the entire set of APs and a superior accuracy over utilizing the reduced dataset by classical APs selection counterparts. Moreover, the proposed quantum algorithm achieves more than an order of magnitude speedup over the corresponding classical APs selection algorithms, emphasizing the efficiency of the proposed quantum algorithm for large-scale localization systems.

quant-ph↗

arXiv2023

A Quantum Fingerprinting Algorithm for Next Generation Cellular Positioning

The recent release of the third generation partnership project, Release 17, calls for sub-meter cellular positioning accuracy with reduced latency in calculation. To provide such high accuracy on a worldwide scale, leveraging the received signal strength (RSS) for positioning promises ubiquitous availability in the current and future equipment. RSS Fingerprint-based techniques have shown a great potential for providing high accuracy in both indoor and outdoor environments. However, fingerprint-based positioning faces the challenge of providing a fast matching algorithm that can scale worldwide. In this paper, we propose a cosine similarity-based quantum algorithm for enabling fingerprint-based high accuracy and worldwide positioning that can be integrated with the next generation of 5G and 6G networks and beyond. By entangling the test RSS vector with the fingerprint RSS vectors, the proposed quantum algorithm has a complexity that is exponentially better than its classical version as well as the state-of-the-art quantum fingerprint positioning systems, both in the storage space and the running time. We implement the proposed quantum algorithm and evaluate it in a cellular testbed on a real IBM quantum machine. Results show the exponential saving in both time and space for the proposed quantum algorithm while keeping the same positioning accuracy compared to the traditional classical fingerprinting techniques and the state-of-the-art quantum algorithms.

quant-ph↗

arXiv2022

Spectroscopic and Photometric Study of the new $δ$ Scuti Star ASAS J063309+1810.8

We present BVR observations and low-resolution spectra collected by the Kottamia Astronomical Observatory 1.88 m telescope (KAO) for the new pulsating star ASAS J063309+1810.8 (hereafter it will be called ASAS06+18). The photometric analysis revealed that the star is a $δ$ Scuti star with low amplitude (a=0.054-0.099 in V mag.) and a short period (102.604 min). Fourier analysis of the light curves reveals the fundamental mode with two harmonics. The photometric analysis yielded a new value of the updated frequency of 13.0035232 cd-1 with an amplitude of 49.93 mmag at phases 0.326 and S/N 21.75 and to two frequencies (20.2099237cd-1, 5.9130945cd-1). Given the available data, 37 new times of maximum light are presented, and an updated ephemeris for the star and its O-C data. Assuming its period decreases and changes smoothly, a new value of (1/P)dP/dt is determined. We calculated the effective temperature and surface gravity as Teff=7125+- 250 K and log g=4.0+-0.2 dex from model atmosphere analysis of the star's spectra at different phases. The bolometric magnitude Mbol=2.798 (0.016), radius R=1.577(0.077) R_sun, luminosity L=5.714(1.066) L_sun, the mass is M=1.595 M_sun and pulsation constant Q=0{m}.0338(0.0003). The star's locations in the evolutionary mass-luminosity and mass-radius relationships are discussed.

astro-ph.SR↗

arXiv2022

Device-independent Quantum Fingerprinting for Large Scale Localization

Although RF fingerprinting is one of the most commonly used techniques for localization, deploying it in a ubiquitous manner requires addressing the challenge of supporting a large number of heterogeneous devices and their variations. We present QHFP, a device-independent quantum fingerprint matching algorithm that addresses two of the issues for realizing worldwide ubiquitous large-scale location tracking systems: storage space and running time as well as devices heterogeneity. In particular, we present a quantum algorithm with a complexity that is exponentially better than the classical techniques, both in space and running time. QHFP also has provisions for handling the inherent localization error due to building the large-scale fingerprint using heterogeneous devices. We give the details of the entire system starting from extracting device-independent features from the raw RSS, mapping the classical feature vectors to their quantum counterparts, and showing a quantum cosine similarity algorithm for fingerprint matching. We have implemented our quantum algorithm and deployed it in a real testbed using the IBM Quantum machine simulator. Results confirm the ability of QHFP to obtain the correct estimated location with an exponential improvement in space and running time compared to the traditional classical counterparts. In addition, the proposed device-independent features lead to more than 20% better accuracy in median error. This highlights the promise of our algorithm for future ubiquitous large-scale worldwide device-independent fingerprinting localization systems.

quant-ph↗