arXiv subjects

Yuval Cohen

Publications and source records attributed to Yuval Cohen.

8 recordsLinked to original sources

arXiv2026

Quantum-Based Optimization of Gas Throughput in Natural Gas Transmission Networks Under Hydraulic Constraints Using QAOA

Maximizing gas throughput in transmission networks under hydraulic and operational constraints is a combinatorial problem whose complexity grows exponentially with network size, making it computationally intensive to solve exactly. This paper addresses the graph-based optimization problem by optimizing nodal-pressure assignments under the Panhandle-B hydraulic equation. By framing the problem as a search over discretized nodal-pressure assignments coupled with a cost Hamiltonian that encodes both the delivery objective and physical-constraint penalties, we establish a unified formulation suitable for the Quantum Approximate Optimization Algorithm (QAOA). The mathematical model is adapted to a Quadratic Unconstrained Binary Optimization (QUBO) formulation and implemented using the Classiq quantum software platform. In simulator-based experiments, QAOA recovered the maximum-throughput valid operating point, consistent with classical exhaustive evaluation and classical hydraulic simulation reference solutions. A distinctive contribution of this work is the end-to-end execution of a reduced problem instance on the IonQ Forte-1 trapped-ion quantum processor. Remarkably, the hardware implementation used only $p=2$ QAOA layers, substantially fewer than the $p=30$ layers used in the simulator-based study. Despite this significant reduction in circuit depth, the QPU produced physically valid and interpretable candidate solutions that bracketed the continuous classical optimum, with each located within one pressure-discretization step of it. These results demonstrate that meaningful gas-network optimization behavior can be obtained using considerably shallower QAOA circuits than initially expected and provide an end-to-end proof of concept for near-term quantum-assisted gas-network optimization.

quant-ph↗

arXiv2025

Beyond Words: Interjection Classification for Improved Human-Computer Interaction

In the realm of human-computer interaction, fostering a natural dialogue between humans and machines is paramount. A key, often overlooked, component of this dialogue is the use of interjections such as "mmm" and "hmm". Despite their frequent use to express agreement, hesitation, or requests for information, these interjections are typically dismissed as "non-words" by Automatic Speech Recognition (ASR) engines. Addressing this gap, we introduce a novel task dedicated to interjection classification, a pioneer in the field to our knowledge. This task is challenging due to the short duration of interjection signals and significant inter- and intra-speaker variability. In this work, we present and publish a dataset of interjection signals collected specifically for interjection classification. We employ this dataset to train and evaluate a baseline deep learning model. To enhance performance, we augment the training dataset using techniques such as tempo and pitch transformation, which significantly improve classification accuracy, making models more robust. The interjection dataset, a Python library for the augmentation pipeline, baseline model, and evaluation scripts, are available to the research community.

cs.HC↗

arXiv2025

Gradient-free pulse optimization for adiabatic control in open few-body quantum systems

We present a robust pulse optimization method for adiabatic population transfer and adiabatic quantum computation. The approach relies on identifying control pulses that keep the evolving quantum system close to its instantaneous ground state. By combining advanced gradient-free optimization tools with specialized cost functions for adiabatic control, it achieves both efficiency and robustness. To demonstrate its generality, we apply the method to three examples involving both atomic and superconducting qubits. We test different optimization cost functions and discretization bases, showing that the approach outperforms ensemble optimization. Finally, to verify its performance on real quantum hardware, we implement digitized adiabatic qubit control using the optimized pulses on the IBM Quantum cloud.

quant-ph↗

arXiv2025

Quantum-assured magnetic navigation achieves positioning accuracy better than a strategic-grade INS in airborne and ground-based field trials

Modern navigation systems rely critically on GNSS, which in many cases is unavailable or unreliable (e.g. due to jamming or spoofing). For this reason there is great interest in augmenting backup navigation systems such as inertial navigation systems (INS) with additional modalities that reduce positioning error in the absence of reliable GNSS. Magnetic-anomaly navigation is one such approach, providing passive, non-jammable navigation through periodic position fixes obtained by comparing local measurements of Earth's crustal field against known anomaly maps. Despite its potential, existing MagNav efforts have been limited by magnetometer performance and platform noise; solutions addressing these problems have proven either too brittle or impractical for realistic deployment. Here we demonstrate a quantum-assured MagNav solution based on proprietary quantum magnetometers with by a novel denoising and map-matching algorithms. The system fits on fixed-wing drones or in the avionics bay of a commercial airliner. We present trials at altitudes up to 19000 feet, testing onboard and outboard quantum magnetometers comparing against a strategic-grade INS. Our MagNav solution achieves superior performance, delivering up to 46x better positioning error than the velocity-aided INS; the best final positioning accuracy we achieve is 22m or 0.006% of the flight distance. Airborne trials consistently achieve at least 11x advantage over the INS across varying conditions, altitudes, and flight patterns. The system learns model parameters online without special vehicle maneuvers providing robustness to various configuration changes (e.g. changing payload or latitude). Our trials also include the first successful MagNav performed in a ground vehicle using publicly-available anomaly maps, delivering bounded positioning error 7x lower than the INS, with both systems in strapdown configuration.

quant-ph↗

arXiv2024

Detection-Driven Object Count Optimization for Text-to-Image Diffusion Models

Accurately controlling object count in text-to-image generation remains a key challenge. Supervised methods often fail, as training data rarely covers all count variations. Methods that manipulate the denoising process to add or remove objects can help; however, they still require labeled data, limit robustness and image quality, and rely on a slow, iterative process. Pre-trained differentiable counting models that rely on soft object density summation exist and could steer generation, but employing them presents three main challenges: (i) they are pre-trained on clean images, making them less effective during denoising steps that operate on noisy inputs; (ii) they are not robust to viewpoint changes; and (iii) optimization is computationally expensive, requiring repeated model evaluations per image. We propose a new framework that uses pre-trained object counting techniques and object detectors to guide generation. First, we optimize a counting token using an outer-loop loss computed on fully generated images. Second, we introduce a detection-driven scaling term that corrects errors caused by viewpoint and proportion shifts, among other factors, without requiring backpropagation through the detection model. Third, we show that the optimized parameters can be reused for new prompts, removing the need for repeated optimization. Our method provides efficiency through token reuse, flexibility via compatibility with various detectors, and accuracy with improved counting across diverse object categories.

cs.CV↗

arXiv2021

Electromagnetic induction imaging with a scanning radio-frequency atomic magnetometer

We demonstrate electromagnetic induction imaging with an unshielded, portable radio-frequency atomic magnetometer scanning over the target object. This configuration satisfies standard requirements in typical applications, from security screening to medical imaging. The ability to scan the magnetometer over the object relies on the miniaturization of the sensor head and on the active compensation of the ambient magnetic field. Additionally, a procedure is implemented to extract high-quality images from the recorded spatial dependent magnetic resonance. The procedure is shown to be effective in suppressing the detrimental effects of the spatial variation of the magnetic environment.

physics.atom-ph↗

arXiv2019

A Cold Atom Radio-Frequency Magnetometer

We propose and demonstrate a radio-frequency atomic magnetometer with sub-Doppler laser cooled rubidium-87. With a simple and compact design, our system demonstrates a sensitivity of $330~pT/\sqrt{Hz}$ in an unshielded environment, thus matching or surpassing previously reported cold atoms designs. By merging the multiple uses and robustness of radio-frequency atomic magnetometers with the detailed control of laser cooling, our cold atom radio-frequency magnetometer has the potential to move applications of atomic magnetometry to high spatial resolution. Direct impact in metrology for applied sciences, materials characterization, and nanotechnology can be anticipated.

physics.atom-ph↗

arXiv2010

Mining The Successful Binary Combinations: Methodology and A Simple Case Study

The importance of finding the characteristics leading to either a success or a failure is one of the driving forces of data mining. The various application areas of finding success/failure factors cover vast variety of areas such as credit risk evaluation and granting loans, micro array analysis, health factors and health risk factors, and parameter combination leading to a product success. This paper presents a new approach for making inferences about dichotomous data. The objective is to determine rules that lead to a certain result. The method consists of four phases: in the first phase, the data is processed into a binary format of a truth table, in the second phase; rules are found by utilizing an algorithm that minimizes Boolean functions. In the third phase the rules are checked and filtered. In the fourth phase, simple rules that involve one to two features are revealed.

cs.DB↗