arXiv subjects

Waqas Ahmad

Publications and source records attributed to Waqas Ahmad.

7 recordsLinked to original sources

arXiv2026

3D Integrated Embedded Filters for Superconducting Quantum Circuits

Microwave filtering for superconducting qubits is a key element of quantum computing technology, enabling high coherence and fast state detection. This work presents the design and implementation of novel microwave Purcell filters for superconducting quantum circuits, integrated within a multilayer printed circuit board (PCB). The off-chip design removes all filter components from the qubit substrate, reducing device complexity, improving layout footprint and allowing better scalability to large qubit counts. Each embedded filter can couple up to nine readout resonators, enabling efficient multiplexed readout. Electromagnetic simulations of the filter predict a thousand-fold improvement in qubit isolation from the readout port. The design was experimentally validated under cryogenic conditions in conjunction with a 35-qubit device, demonstrating compatibility of the PCB-based filter with high-coherence superconducting qubits. The comparison of the measured qubit median T1 of 84 $μ$s with the expected radiative limit from electromagnetic simulations validated the presence of Purcell filtering in the system.

quant-ph↗

arXiv2026

Design and Operation of Wafer-Scale Packages Containing >500 Superconducting Qubits

Packages capable of supporting large arrays of high-coherence superconducting qubits are vital for the realisation of fault-tolerant quantum computers and the necessary high-throughput metrology required to optimise fabrication and manufacturing processes. We present a wafer-scale packaging architecture supporting over 500 qubits on a single 3-inch die. The package is engineered to suppress parasitic RF modes, and to mitigate material loss through simulation-informed design while managing differential thermal contraction to ensure robust operation at millikelvin temperatures. System-level heat-load calculations from a large wiring payload show this package may be operated in commercial dilution refrigerators. Measurements of the qubits loaded into the package show median $T_1$, $T_{2e} \sim 100~μ$s ($\sim$100 qubits) alongside readout with median fidelity of 97.5% (54 qubits) and a median qubit temperature of 36 mK (54 qubits). These results validate the performance of these packages and demonstrate that large-scale integration can be achieved without compromising device performance. Finally, we highlight the utility of these packages as a tool for high throughput feedback on qubit figures of merit over large sample sizes, allowing identification of performance outliers in the tails of the coherence distribution, a critical capability for informing fabrication and manufacture of high-quality quantum qubits and quantum processors.

quant-ph↗

arXiv2025

Multi Camera Connected Vision System with Multi View Analytics: A Comprehensive Survey

Connected Vision Systems (CVS) are transforming a variety of applications, including autonomous vehicles, smart cities, surveillance, and human-robot interaction. These systems harness multi-view multi-camera (MVMC) data to provide enhanced situational awareness through the integration of MVMC tracking, re-identification (Re-ID), and action understanding (AU). However, deploying CVS in real-world, dynamic environments presents a number of challenges, particularly in addressing occlusions, diverse viewpoints, and environmental variability. Existing surveys have focused primarily on isolated tasks such as tracking, Re-ID, and AU, often neglecting their integration into a cohesive system. These reviews typically emphasize single-view setups, overlooking the complexities and opportunities provided by multi-camera collaboration and multi-view data analysis. To the best of our knowledge, this survey is the first to offer a comprehensive and integrated review of MVMC that unifies MVMC tracking, Re-ID, and AU into a single framework. We propose a unique taxonomy to better understand the critical components of CVS, dividing it into four key parts: MVMC tracking, Re-ID, AU, and combined methods. We systematically arrange and summarize the state-of-the-art datasets, methodologies, results, and evaluation metrics, providing a structured view of the field's progression. Furthermore, we identify and discuss the open research questions and challenges, along with emerging technologies such as lifelong learning, privacy, and federated learning, that need to be addressed for future advancements. The paper concludes by outlining key research directions for enhancing the robustness, efficiency, and adaptability of CVS in complex, real-world applications. We hope this survey will inspire innovative solutions and guide future research toward the next generation of intelligent and adaptive CVS.

cs.CV↗

arXiv2023

Observability of Parameter Space for Charged Higgs Boson in its bosonic decays in Two Higgs Doublet Model Type-1

This study explores the possibility of discovering $H^{\pm}$ through its bosonic decays, i.e. $H^{\pm}\rightarrow W^\pmϕ$ (where $ϕ$ = h or A), within the Type-I Two Higgs Doublet Model (2HDM). The main objective is to demonstrate the available parameter space after applying the recent experimental and theoretical exclusion limits. We suggest that for $m_{H^\pm}$ = 150 GeV is the most probable mass for the $H^\pm\rightarrow W^\pmϕ$ decay channel in $pp$ collisions at $\sqrt{s}$ = 8, 13 and 14 TeV. Therefore we propose that this channel may be used as an alternative to $H^\pm\rightarrow τ^\pmν$.

hep-ph↗

arXiv2022

Data Dimension Reduction makes ML Algorithms efficient

Data dimension reduction (DDR) is all about mapping data from high dimensions to low dimensions, various techniques of DDR are being used for image dimension reduction like Random Projections, Principal Component Analysis (PCA), the Variance approach, LSA-Transform, the Combined and Direct approaches, and the New Random Approach. Auto-encoders (AE) are used to learn end-to-end mapping. In this paper, we demonstrate that pre-processing not only speeds up the algorithms but also improves accuracy in both supervised and unsupervised learning. In pre-processing of DDR, first PCA based DDR is used for supervised learning, then we explore AE based DDR for unsupervised learning. In PCA based DDR, we first compare supervised learning algorithms accuracy and time before and after applying PCA. Similarly, in AE based DDR, we compare unsupervised learning algorithm accuracy and time before and after AE representation learning. Supervised learning algorithms including support-vector machines (SVM), Decision Tree with GINI index, Decision Tree with entropy and Stochastic Gradient Descent classifier (SGDC) and unsupervised learning algorithm including K-means clustering, are used for classification purpose. We used two datasets MNIST and FashionMNIST Our experiment shows that there is massive improvement in accuracy and time reduction after pre-processing in both supervised and unsupervised learning.

cs.CV↗

arXiv2019

Lower Bound on the Sum-rate of Decremental Beam Selection Algorithm for Beamspace MIMO Systems

In general, the zero-forcing (ZF) precoding suffers from a severe receive signal-to-noise ratio (SNR) degradation in the high interference regime. However, recent evidences from realistic measurements demonstrated that millimeter wave (mmWave) systems are mainly noise-limited as high gain antennas behave as spatial filters to the interference signal. This makes ZF precoding equally attractive as that of other linear precoding counterparts. Considering ZF precoding, this paper aims to derive a lower bound on the sum-rate achieved by a decremental beam selection (BS) algorithm in a beamspace MIMO (B-MIMO) system operating at mmWave frequencies. This bound relates Frobenious norms of precoding matrices of full and reduced dimensional (i.e. after BS) B-MIMO systems through a deterministic square-hyperbolic function. Note that, both ZF precoding and decremental BS are not new concepts. However, the derived sum-rate bound provides a new insight to the topic. Given a particular full dimensional B-MIMO channel, the presented bound can be used to understand limits of BS algorithms.

cs.IT↗

arXiv2016

Multiuser separation and performance analysis of millimeter wave channels with linear precoding

In the conventional multiuser MIMO systems, user selection and scheduling has previously been used as an effective way to increase the sum rate performance of the system. However, the recent concepts of the massive MIMO systems (at centimeter wavelength frequencies) have shown that with higher spatial resolution of antenna arrays different users in the dense scenarios can be spatially separated. This in turn significantly reduces the signal processing efforts required for multiuser selection algorithms. On the other hand, recent measurements at millimeter wave frequencies show that multipath components only arrive from few angular directions leading to high spatial correlation between the paths and co-located users. This paper focus at the investigation of spatial separation among the users at the millimeter wave frequencies with fully digital linear zero-forcing transmit precoding considering various channel propagation parameters. Our analysis results convincingly give a proof that multiuser selection algorithms are still important for millimeter wave communication systems. Results also show that increased number of antenna elements does not give a major benefit to sum rate improvements as compared to the selection of correct number of users to be selected/scheduled.

cs.IT↗