arXiv subjects

Sergi Abadal

Publications and source records attributed to Sergi Abadal.

At least 19 recordsLinked to original sources

arXiv2026

Weighted Quantum Signal Processing: Low-Depth Polynomial Approximation with Applications to Kolmogorov-Arnold Networks

Quantum Signal Processing is a powerful quantum framework for generating and approximating univariate polynomials. However, QSP is often limited by circuit-depth bottlenecks and parity constraints on the class of realizable polynomials. In this work, we introduce Weighted Quantum Signal Processing, an extension of QSP in which a weight function is assigned to the central rotation operator. This formulation provides a deeper understanding of QSP, which emerges as the special case of WQSP with unit weights. The choice of weights determines the structure and expressive capabilities of WQSP circuits. When the weights are natural numbers greater than one, WQSP reduces to a pruned version of QSP, revealing parameter redundancies in the standard framework. Through appropriate selection of integer weights, WQSP achieves linear-to-exponential reductions in the number of parameters required to realize arbitrary bounded univariate polynomials while preserving approximation quality. For generic weights, we establish corresponding approximation error bounds and show that, in many cases, the approximation is exact. We analyze WQSP from both a deterministic perspective, where polynomial generation is formulated as the solution of a linear system, and a quantum machine learning perspective, where WQSP serves as a structured and expressive quantum learning model. We further employ this learning framework to parameterize learnable activation functions in Kolmogorov--Arnold Networks for multivariate function approximation. Our results show that WQSP provides a compact, flexible, and theoretically grounded framework for realizing arbitrary univariate polynomials while requiring significantly fewer trainable parameters than conventional QSP. This yields expressive and parameter-efficient neural architectures, highlighting the potential of WQSP as a scalable primitive for quantum-enhanced machine learning.

quant-ph↗

arXiv2026

Transversal Gates and Magic State Distillation in an Optimally Synthesized Spin-Qubit Shuttling Bus

Fault-tolerant quantum computing requires not only reliable logical qubit storage, but also the ability to perform high-fidelity logical operations between error-corrected qubits at scale. While much of the existing literature focuses on optimizing syndrome extraction for a single logical qubit, the co-design of physical architectures that support both robust error correction and efficient logical computation remains an open challenge. In this work, we propose a multi-qubit spin-qubit shuttling bus architecture that addresses both requirements simultaneously. The architecture optimizes the physical qubit layout for syndrome extraction and supports transversal two-qubit logical gates between an arbitrary number of logical qubits, achieving all-to-all logical connectivity through coherent spin shuttling. We further propose an ancilla-sharing scheme that encodes multiple logical qubits within a single logical element, compressing the physical footprint of the processor and improving long-range gate fidelity. Extending the architecture from a one-dimensional bus to a two-dimensional grid of shuttling tracks reduces the inter-qubit distance, yielding consistent improvements in logical error. Finally, we apply the Quantum Reverse Mapping methodology at the logical level to optimize the layout of a \textit{15-to-1} magic state distillation circuit, demonstrating how the transversal capabilities of the proposed architecture can be leveraged for universal fault-tolerant computation. Taken together, these results establish a principled co-design framework that bridges the physical, error-correction, and logical computation layers of the quantum stack, and demonstrate that spin-qubit shuttling architectures are a viable and flexible substrate for scalable fault-tolerant quantum computation.

quant-ph↗

arXiv2026

Energy efficiency of quantum computers

How much energy does a quantum computer consume? Are they more efficient than their classical counterparts? In this work, we make a step towards answering these questions. We define the energy efficiency of a quantum computer as the ratio of the number of algorithms it can perform during a given time over the energy consumed by the hardware during this time. We analyze the most representative physical platforms currently envisioned to be used as building blocks of quantum computers: superconducting qubits, silicon spin qubits, trapped ions, neutral atoms and photonic qubits. Including insights from experts in all these technologies and taking into account algorithm compilation constraints, we discuss the advantages and inconveniences of each platform from an energy standpoint. Beyond providing concrete values of the energy consumption of current quantum computers, we lay the foundation of a framework to benchmark the energy efficiency of any future quantum computing architecture.

quant-ph↗

arXiv2026

Neural 3D Object Reconstruction with Small-Scale Unmanned Aerial Vehicles

Miniaturized Uncrewed Aerial Vehicles (UAVs) can access indoor and hard-to-reach spaces, but severe constraints on payload and autonomy have limited their use in demanding tasks such as high-quality 3D reconstruction. We introduce a novel system architecture that enables autonomous, high-fidelity 3D scanning of static objects with sub-100 gram UAVs. Our core innovation lies in a closed-loop active viewpoint selection framework specifically tailored for ultra-constrained micro-platforms, advancing beyond standard static or offline active reconstruction methods. The framework establishes a dual-reconstruction pipeline that creates a real-time (RT) feedback loop between data capture and flight control. A near-RT process uses Structure-from-Motion (SfM) to generate an instantaneous point-cloud of the object. A systematic trajectory adaptation algorithm analyzes the model quality on the fly and dynamically adapts the UAV's trajectory based on parameterized spatial partitioning to intelligently capture new images of poorly covered areas, ensuring comprehensive acquisition. For the final, high-fidelity output, a non-RT pipeline employs a Neural Radiance Fields (NeRF)-based Neural 3D Reconstruction (N3DR) approach, fusing SfM-derived camera poses with precise external location data, evaluated across both radio-based Ultra Wideband (UWB) and visual motion-capture setups, to correct sensor noise and achieve superior accuracy. We implemented and validated this architecture using Crazyflie 2.1 UAVs. Our experiments, conducted in both single- and multi-UAV configurations show that algorithmic dynamic trajectory adaptation consistently improves reconstruction quality over static flight paths. This work demonstrates a scalable and autonomous solution that unlocks the potential of miniaturized UAVs for fine-grained 3D reconstruction, a capability previously reserved for much larger platforms.

cs.RO↗

arXiv2026

Communicating Smartly in Molecular Communication Environments: Neural Networks in the Internet of Bio-Nano Things

Recent developments in the Internet of Bio-Nano-Things (IoBNT) are laying the foundation for innovative healthcare applications that envision a network of remotely coordinated nanodevices within the human body to monitor and actuate over potential diseases. However, interconnecting such nanodevices requires communication strategies that can cope with molecular communication (MC) channels, whose complex, stochastic, and dynamic behavior often makes accurate physical modeling infeasible. To explore the limits of nanodevice interconnectivity under these conditions, this survey focuses on data-driven communication strategies for MC systems, with particular emphasis on machine learning (ML) methods and neural network (NN) architectures for a robust and adaptive communication scheme at the nanoscale. Research on NN-enabled MC spans several aspects covered in this survey, including NNs for communication in IoBNT networks, the feasibility of biocompatible NN realization, explainable approaches, and the generation of training datasets. We also include open-source code examples to support reproducible research across key MC scenarios. Finally, we identify emerging challenges, including the need for robust NN architectures, biologically integrated NN modules, and scalable training strategies.

eess.SP↗

arXiv2026

Whole-Blood Boundary Analysis of BioFET-Based ctDNA Detection for Intravascular Sensing in Intrabody Nanonetworks

Liquid biopsy can detect tumor-derived biomarkers such as circulating tumor DNA (ctDNA), but ultra-low-fraction assays remain costly, slow, and difficult to scale. This motivates interest in intravascular in vivo sensing in the context of intrabody nanonetworks, where nanosensors could support local biomarker monitoring. BioFET-based nanosensors are relevant here because they are label-free, highly miniaturizable, and have shown strong ctDNA sensitivity in controlled media. We examine whether this sensitivity still yields reliable ctDNA detection in whole blood using a reduced-order stochastic simulation model that links operating-point selection, Debye-screened charge transduction, stochastic finite-capacity binding, nonspecific adsorption, background fluctuations, and intrinsic electronic noise to blank-threshold detection. Monte Carlo evaluation with physiologically grounded parameters shows that short Debye length and several-nanometer charge-to-channel separation attenuate the current shift, while low-frequency noise and background fluctuations reduce the margin between target-present and blank responses. Under the tested quasi-static charge-gating regime, the simulated current shifts do not reliably exceed the blank-derived threshold at low ctDNA concentrations. The model therefore provides a whole-blood boundary analysis that identifies which interface configurations and operating conditions most strongly limit reliable BioFET-based intravascular ctDNA detection.

cs.ET↗

arXiv2026

An Analytical Approach to Design Space Exploration for Cavity-Mediated Quantum State Transfer in Multi-core Architectures

In multi-core quantum computing architectures, waveguide-mediated interconnects are essential for facilitating fast, high-fidelity quantum state transfer between qubits located in different chips. However, optimizing these systems typically relies on computationally expensive numerical simulations that offer limited physical insight. In this work, we derive exact analytical expressions for the state transfer dynamics of a two-qubit system coupled via a waveguide, modeled through a Jaynes-Cummings Hamiltonian and the Lindblad master equation. We apply the Monte Carlo wave-function method and obtain a closed-form solution for qubit occupation probabilities that accounts for both detuning and dissipative losses. Our analytical framework provides a significant computational speedup compared to standard numerical solvers, enabling large-scale parameter sweeps while maintaining high precision in both fidelity and latency predictions. Furthermore, the model reveals and explains systematic low-fidelity regions arising from destructive interference between internal oscillations and detuning-induced envelopes, which are phenomena that are difficult to characterize through numerical means alone. Finally, we propose a simplified latency model and an efficiency-based function to enable rapid identification of optimal operating points. This analytical approach provides a robust foundation for the design and optimization of interconnects in multi-core quantum processors.

quant-ph↗

arXiv2026

Transmitter Noise Propagation in Millimeter-Wave and Sub-Terahertz: From Limits to Design Guidelines

This paper presents a comprehensive link budget analysis for millimeter wave (mm-Wave) and sub-Terahertz (sub-THz) communication systems with primary focus on transmitter (TX) noise propagation, an often overlooked impairment that can dominate in scenarios where path loss is insufficient to suppress TX noise below receiver thermal and atmospheric molecular noise levels. Unlike traditional thermal noise limited analyses, this work demonstrates that TX noise is amplified by component noise figures that degrade significantly with frequency, rising from single digits to more than $15\,\mathrm{dB}$ in the sub-THz range. In the scenarios analyzed, this propagated TX noise reduces the achievable Signal-to-Noise Ratio (SNR) by approximately $15$ to $25\,\mathrm{dB}$ at short distances, creating fundamental SNR ceilings at ranges below about $10\,\mathrm{cm}$. We develop a systematic framework quantifying TX noise dominance conditions as functions of distance, frequency, and component parameters, revealing fundamental performance constraints for short-range next generation wireless systems. Our findings indicate that the TX noise figure should be as low as possible for short-range communication, and both TX noise and atmospheric molecular noise should be considered for medium- and long-range links.

eess.SP↗

arXiv2026

Channel-Aware Behavioral Power Modeling of CMOS OOK Transceivers for Wireless Network-on-Chip Systems

Wireless Network-on-Chip (WNoC) systems enable low-latency communication in many-core platforms through short-range wireless links. However, the power consumption of integrated transceivers (TRXs), dominated by that of the RF front-end circuitry, remains a major challenge. Moreover, the optimal operating frequency is still unclear, as bandwidth, energy efficiency, and technology maturity must be balanced. This work presents a channel-aware behavioral modeling framework to estimate power consumption and identify energy-efficient operating points in non-coherent On-Off Keying (OOK) TRXs over a wide frequency range. The approach leverages survey data from CMOS implementations to derive frequency-dependent power models for key TRX sub-blocks, including the power amplifier (PA), oscillator, mixer, low noise amplifier (LNA), and envelope detector (ED). By incorporating the frequency-dependent channel loss into the TRX power budget, the model captures system-level power trade-offs across operating regimes. The analysis reveals a frequency-dependent shift in power dominance between the transmitter and receiver: oscillator- and ED-dominated regimes at lower frequencies transition to PA- and LNA-dominated behavior at higher frequencies. Furthermore, the energy-per-bit landscape exhibits sweet spots and a model-based global minimum, indicating that optimal operation cannot be achieved by optimizing transmitter or receiver independently. Overall, the proposed framework enables rapid and physically grounded exploration of power scaling with frequency and channel conditions, providing practical guidelines for energy-efficient design of high-frequency wireless links for WNoC systems and beyond.

eess.SP↗

arXiv2026

Towards Emotion Recognition with 3D Pointclouds Obtained from Facial Expression Images

Facial Emotion Recognition is a critical research area within Affective Computing due to its wide-ranging applications in Human Computer Interaction, mental health assessment and fatigue monitoring. Current FER methods predominantly rely on Deep Learning techniques trained on 2D image data, which pose significant privacy concerns and are unsuitable for continuous, real-time monitoring. As an alternative, we propose High-Frequency Wireless Sensing (HFWS) as an enabler of continuous, privacy-aware FER, through the generation of detailed 3D facial pointclouds via on-person sensors embedded in wearables. We present arguments supporting the privacy advantages of HFWS over traditional 2D imaging, particularly under increasingly stringent data protection regulations. A major barrier to adopting HFWS for FER is the scarcity of labeled 3D FER datasets. Towards addressing this issue, we introduce a FLAME-based method to generate 3D facial pointclouds from existing public 2D datasets. Using this approach, we create AffectNet3D, a 3D version of the AffectNet database. To evaluate the quality and usability of the generated data, we design a pointcloud refinement pipeline focused on isolating the facial region, and train the popular PointNet++ model on the refined pointclouds. Fine-tuning the model on a small subset of the unseen 3D FER dataset BU-3DFE yields a classification accuracy exceeding 70%, comparable to oracle-level performance. To further investigate the potential of HFWS-based FER for continuous monitoring, we simulate wearable sensing conditions by masking portions of the generated pointclouds. Experimental results show that models trained on AffectNet3D and fine-tuned with just 25% of BU-3DFE outperform those trained solely on BU-3DFE. These findings highlight the viability of our pipeline and support the feasibility of continuous, privacy-aware FER via wearable HFWS systems.

cs.CV↗

arXiv2026

Early-Stage Cancer Biomarker Detection via Intravascular Nanomachines: Modeling and Analysis

Early detection of cancer is essential for timely diagnosis and improved patient outcomes. Among emerging technologies, intra-body nanoscale communication offers an innovative solution to identify molecular cues within the human bloodstream. This study investigates a minimally invasive approach for early-stage cancer biomarker detection using nanomachines introduced into the bloodstream. To assess the feasibility of this approach, computational simulations are used to emulate the vascular environment and evaluate biomarker detection performance under different physiological conditions. Current modeling approaches often fail to capture essential vascular characteristics, including non-uniform flow structures, size-dependent particle mobility, and particle margination driven by red blood cell interactions. To address these limitations, our study incorporates these factors into the simulation framework and quantifies their individual and combined effects on biomarker detection efficiency. Baseline detection performance is first obtained under uniform flow assumptions, after which introducing realistic vascular transport mechanisms progressively reduces detection probability for all vessel types and nanomachine sizes. Among the considered vessels, capillary consistently achieves the highest detection probability across all nanomachine sizes.

cs.ET↗

arXiv2026

Cryo-CMOS Antenna for Wireless Communications within a Quantum Computer Cryostat

Scaling quantum computers from a few qubits to large numbers remains one of the critical challenges in realizing practical quantum advantage. Multi-core quantum architectures have emerged as a promising solution, enabling scalability through distributed quantum processing units (QPUs) interconnected via classical and quantum links. However, the bottleneck of wired connections persists, as densely packed wired interconnects, both vertically across temperature stages and horizontally within the same layer, introduce spatial constraints, power dissipation, and latency, which could hinder performance as the number of QPUs increases. To overcome these limitations, this work proposes a cryo-compatible on-chip differential dipole antenna operating at 28 GHz to enable short-range wireless communication within a quantum computer cryostat. Temperature-dependent material properties are incorporated to accurately capture antenna behavior at 4 K. Moreover, by embedding the antenna in a realistic cryostat structure, we evaluate the feasibility of antenna operation within the cryogenic environment. The proposed antenna achieves a reflection coefficient of -20.8 dB in free space and -18.38 dB within the cryostat, demonstrating efficient impedance matching.

quant-ph↗

arXiv2026

Synthesizing an Optimal Spin Qubit Shuttling Bus Architecture for the Surface Code

As quantum computers scale toward millions of physical qubits, it becomes essential to robustly encode individual logical qubits to ensure fault tolerance under realistic noise. A high-quality foundational encoding allows future compilation techniques and heuristics to build on optimal or near-optimal layouts, improving scalability and error resilience. In this work, we synthesize a one-dimensional shuttling bus architecture for the rotated surface code, leveraging coherent spin-qubit shuttling, following a novel methodology we name Quantum Reverse Mapping. We formulate a mixed-integer optimization model that yields optimal solutions with relatively low execution time for small code distances, and propose a scalable heuristic that matches optimal results while maintaining linear computational complexity. We evaluate the synthesized architecture using architectural metrics, such as shuttling distance and cycle time, and full quantum simulations under realistic noise models, showing that the proposed design can sustain logical error rates as low as $2\times 10^{-10}$ per round at code distance 21, showcasing its feasibility for scalable quantum error correction in spin-based quantum processors.

quant-ph↗

arXiv2026

Towards Scalable Multi-Chip Wireless Networks with Near-Field Time Reversal

The concept of Wireless Network-on-Chip (WNoC) has emerged as a potential solution to address the escalating communication demands of modern computing systems due to its low-latency, versatility, and reconfigurability. However, for WNoC to fulfill its potential, it is essential to establish multiple high-speed wireless links across chips. Unfortunately, the compact and enclosed nature of computing packages introduces significant challenges in the form of Co-Channel Interference (CCI) and Inter-Symbol Interference (ISI), which not only hinder the deployment of multiple spatial channels, but also severely restrict the symbol rate of each individual channel. In this paper, we posit that Time Reversal (TR) could be effective in addressing both impairments in this static scenario, thanks to its spatiotemporal focusing capabilities even in the near-field. Through comprehensive full-wave simulations and bit error rate analysis in multiple chip layouts with multiple frequency bands, we provide evidence that TR can increase the symbol rate by an order of magnitude, enabling the deployment of multiple concurrent links and achieving aggregate speeds exceeding 100 Gb/s. Finally, we evaluate the impact of reducing the sampling rate of the TR filter on the achievable speeds, paving the way to practical TR-based wireless communications at the chip scale.

cs.ET↗

arXiv2026

Assessing the Impact of Low Resolution Control Electronics on Quantum Neural Network Performance

Scaling quantum computers requires tight integration of cryogenic control electronics with quantum processors, where Digital-to-Analog Converters (DACs) face severe power and area constraints. We investigate quantum neural network (QNN) training and inference under finite DAC resolution constraints, evaluating two QNN architectures across four diverse datasets (MNIST, Fashion-MNIST, Iris, Breast Cancer). Pre-trained QNNs achieve accuracy nearly indistinguishable from infinite-precision baselines when deployed on quantum systems with 6-bit DAC control electronics, exhibiting characteristic elbow curves with diminishing returns beyond 3-5 bits depending on the dataset. However, training QNNs directly under quantization constraints reveals gradient deadlock below 12-bit resolution, where parameter updates fall below quantization step sizes, preventing training entirely. We introduce temperature-controlled stochastic quantization that overcomes this limitation through probabilistic parameter updates, enabling successful training at 4-10 bit resolutions. Remarkably, stochastic quantization not only matches but frequently exceeds infinite-precision baseline performance across both architectures and all datasets. Our findings demonstrate that low-resolution control electronics (4-10 bits) need not compromise QML performance while enabling substantial power and area reduction in cryogenic control systems, presenting significant implications for practical quantum hardware scaling and hardware-software co-design of QML systems.

quant-ph↗

arXiv2026

28 GHz Wireless Channel Characterization for a Quantum Computer Cryostat at 4 Kelvin

The scalability of quantum computing systems is constrained by the wiring complexity and thermal load introduced by dense wiring for control, readout and synchronization at cryogenic temperatures. To address this challenge, we explore the feasibility of wireless communication within a cryostat for a multi-core quantum computer, focusing on wireless channel characterization at cryogenic temperatures. We propose to place on-chip differential dipole antennas within the cryostat, designed to operate at 28 GHz in temperatures as low as 4 K. We model the antennas inside a realistic cryostat and, using full-wave electromagnetic simulations, we analyze impedance matching, spatial field distribution, and energy reverberation due to metallic structures. The wireless channel is characterized through measured channel impulse response (CIR) across multiple receiver antenna positions. The results demonstrate potential for reliable shortrange communication with high Signal-to-Noise Ratio (SNR) and limited sensitivity to positional variation, at the cost of nonnegligible delay spread, due to significant multipath effects.

quant-ph↗

arXiv2026

Quantum Circuit Pruning: Improving Fidelity via Compilation-Aware Circuit Approximation

This work presents a routing-aware pruning strategy for quantum circuits executed on Noisy Intermediate-Scale Quantum (NISQ) devices. We propose a method to remove parametric controlled rotations whose small rotation angles do not justify the routing overhead required for their implementation. By selectively pruning such gates, the method mitigates fidelity loss arising from additional SWAP operations introduced during compilation. Our approach evaluates whether executing a gate leads to greater fidelity loss than omitting it. Simulations on benchmark circuits with realistic noise models show that the method reduces two-qubit gate counts (up to 48.6%) while improving final state fidelity (up to 47.7%), especially for larger circuits where routing costs dominate.

quant-ph↗

arXiv2025

Spatially Consistent Air-to-Ground Channel Modeling and Simulation via 3D Shadow Projections

We present an approach for spatially-consistent semi-deterministic Air-to-Ground (A2G) channel modeling in Unmanned Aerial Vehicle-assisted networks. We use efficient 3D building shadow projections to determine Line-of-Sight (LOS) regions, enabling fast generation of LOS maps. By integrating LOS-aware deterministic path loss with stochastic shadow fading, the approach produces spatially consistent A2G radio maps suitable for environment- and mobility-aware channel evaluation and performance prediction. Simulation results in ITU-compliant Manhattan grid environments demonstrate the model's ability to reflect key urban propagation characteristics, such as LOS blockage patterns and outage behavior. The proposed approach provides an efficient alternative to ray tracing or fully stochastic models, with particular relevance for user mobility, link planning, and radio map generation in 6G non-terrestrial networks.

eess.SP↗