arXiv subjects

Archisman Ghosh

Publications and source records attributed to Archisman Ghosh.

At least 19 recordsLinked to original sources

arXiv2026

Assessing the Impact of Instrumental Requirements on the Scientific Performance of the Einstein Telescope

We investigate the relationship between instrumental requirements and the scientific performance of the Einstein Telescope (ET), a third-generation (3G) gravitational-wave (GW) observatory. Different technical design choices result in distinct noise budgets, ultimately shaping the detector's scientific capabilities. To systematically assess and compare their impact, we define a comprehensive set of performance metrics spanning compact binary coalescence (CBC) detection and parameter estimation, as well as other sources, including stochastic GW backgrounds, isolated spinning neutron stars, and core-collapse supernovae (CCSNe). We build a comparative reference framework that links degradations in specific noise contributions and frequency bands to losses in scientific capabilities. We consider a representative selection of technical parameters, such as coating and suspension temperatures, the filter cavity length in the low-frequency instrument, and the beam size in the high-frequency instrument. We evaluate how sensitivity variations across specific frequency bands affect different scientific objectives. We quantify how the sensitivity below 30 Hz impacts the detectability of massive and/or high-redshift sources and the reconstruction of long-duration CBC signals, affecting early warning and sky localization for binary neutron stars (BNSs). Sensitivity in the 30-450 Hz range governs most CBC parameter-estimation metrics, while high-frequency sensitivity above ~450 Hz predominantly impacts BNS post-merger studies and CCSN detectability, with modest effects on detection rates. Even with the most significant degradations considered, the ET science case remains robust overall. Our results provide a comprehensive benchmark linking scientific objectives to instrumental requirements, particularly important as the final design and infrastructure of 3G observatories are being defined.

astro-ph.IM↗

arXiv2026

Projection-Free Transformers via Gaussian Kernel Attention

Self-attention in Transformers is typically implemented as $\mathrm{softmax}(QK^\top/\sqrt{d})V$, where $Q=XW_Q$, $K=XW_K$, and $V=XW_V$ are learned linear projections of the input $X$. We ask whether these learned projections are necessary, or whether they can be replaced by a simpler similarity-based diffusion operator. We introduce \textbf{Gaussian Kernel Attention} (GKA), a drop-in replacement for dot-product attention that computes token affinities directly using a Gaussian radial basis function (RBF) kernel applied to per-head token features. Each head learns only a bandwidth parameter $\sigma_h$, while a single output projection $W_O$ preserves compatibility with the standard Transformer interface. GKA can be interpreted as normalized kernel regression over tokens, linking modern Transformer architectures to classical non-local filtering and kernel smoothing methods. We evaluate GKA in both vision and language modeling settings. For autoregressive language modeling within the \texttt{nanochat} framework, we implement causal masking and sliding-window constraints by masking and renormalizing the Gaussian kernel. At depth 20, a GKA model with $0.42\times$ the parameters and $0.49\times$ the total training FLOPs of a standard attention baseline trains stably, exhibits a near-zero train-validation gap, and demonstrates competitive behavior on standard benchmarks, albeit with higher bits-per-byte (BPB) at this compute scale. Overall, GKA provides a minimal, interpretable attention mechanism with an explicit locality scale, offering a dimension in the accuracy-efficiency trade-off for Transformer design.

cs.LG↗

arXiv2026

No Tile Left Behind: Multiprogramming for Surface-Code Architectures

Fault-tolerant quantum computing (FTQC) is emerging as the architectural regime in which practical large-scale quantum workloads will execute. In this setting, however, multiprogramming is no longer a matter of partitioning a flat pool of qubits. Quantum error correction exposes a structured floorplan of data tiles, ancilla tiles, and magic-state service resources, so concurrent execution must account for compact placement, connectivity, routing headroom, and shared support infrastructure. This makes FTQC multiprogramming fundamentally harder than its NISQ counterpart: admission decisions can fragment the remaining floorplan, conservative reservations can waste ancilla, and dynamic contention across data, ancilla, and magic-state resources can degrade both throughput and quality of service. In this work, we develop a formal framework for FTQC multiprogramming that captures these structural constraints and their runtime implications. We formulate the baseline static allocation problem, extend it to limited-resource and online settings through hierarchy-aware scheduling policies, and further generalize it to cultivation-enabled architectures with dynamic magic-state generation. Through simulation on synthetic Clifford+T workloads, the proposed scheduler achieves a normalized system speedup of 3.1x, improving over prior FTQC multiprogramming baselines by ~29% while maintaining low mean slowdown.

quant-ph↗

arXiv2026

Toward designing workload-aware Surface Code Architectures

Practical quantum advantage is expected to depend on fault-tolerant quantum computing, although the architectural overhead needed to support fault tolerance is still extremely high. Prior FTQC designs generally emphasize either fast logical-qubit accessibility at the cost of significant qubit overhead, or high logical-qubit density at the cost of added workload latency. We propose an architecture that balances these competing objectives by placing surface-code patches around an ancilla-centric region, which yields nearly uniform ancilla access for all data qubits. Building on this design, we introduce a new workload-driven placement method that uses the $T$-gate profile of an application to determine an effective floorplan. We further provide a reconfigurable optimization for reducing the latency of $Y$-gate measurements on a per-workload basis. To improve flexibility, we also study concurrent execution of multiple programs on the same architecture. Numerical evaluation indicates that our approach keeps cycles per instruction near the optimal regime while reducing the number of required data tiles by up to $\sim21\%$, and achieves up to $\sim90\%$ efficiency when running 10 programs concurrently.

quant-ph↗

arXiv2026

Probing Cosmic Expansion and Early Universe with Einstein Telescope

Over the next two decades, gravitational-wave (GW) observations are expected to evolve from a discovery-driven endeavour into a precision tool for astrophysics, cosmology, and fundamental physics. Current second-generation ground-based detectors have established the existence of compact-binary mergers and enabled GW multi-messenger astronomy, but they remain limited in sensitivity, redshift reach, frequency coverage, and duty cycle. These limitations prevent them from addressing many fundamental open questions in cosmology. By the 2040s, wide-field electromagnetic surveys will have mapped the luminous Universe with unprecedented depth and accuracy. Nevertheless, key problems including the nature of dark matter, the physical origin of cosmic acceleration, the properties of gravity on cosmological scales, and the physical conditions of the earliest moments after the Big Bang will remain only partially constrained by electromagnetic observations alone. Progress on these fronts requires access to physical processes and epochs that do not emit light. Gravitational waves provide a unique and complementary observational channel: they propagate over cosmological distances largely unaffected by intervening matter, probe extreme astrophysical environments, and respond directly to the geometry of spacetime. In this context, next-generation GW observatories such as the Einstein Telescope (ET) will be transformative for European astronomy. Operating at sensitivities and frequencies beyond existing detectors, ET will observe binary black holes and neutron stars out to previously inaccessible redshifts, enable continuous high signal-to-noise monitoring of compact sources, and detect gravitational-wave backgrounds of astrophysical and cosmological origin. Together with space-based detectors, ET will play a central role in advancing our understanding of cosmic evolution and fundamental physics.

astro-ph.CO↗

arXiv2025

ESO Expanding Horizon White Paper: Revealing the properties of matter at supranuclear densities with gravitational waves

Understanding dense matter under extreme conditions is one of the most fundamental puzzles in modern physics. Complex interactions give rise to emergent, collective phenomena. While nuclear experiments and Earth - based colliders provide valuable insights, much of the quantum chromodynamics phase diagram at high density and low temperature remains accessible only through astrophysical observations of neutron stars, neutron star mergers, and stellar collapse. Astronomical observations thus offer a direct window to the physics on subatomic scales with gravitational waves presenting an especially clean channel. Next-generation gravitational - wave observatories, such as the Einstein Telescope, would serve as unparalleled instruments to transform our understanding of neutron star matter. They will enable the detection of up to tens of thousands of binary neutron star and neutron star - black hole mergers per year, a dramatic increase over the few events accessible with current detectors. They will provide an unprecedented precision in probing cold, dense matter during the binary inspiral, exceeding by at least an order of magnitude what current facilities can achieve. Moreover, these observatories will allow us to explore uncharted regimes of dense matter at finite temperatures produced in a subset of neutron star mergers, areas that remain entirely inaccessible to current instruments. Together with multimessenger observations, these measurements will significantly deepen our knowledge of dense nuclear matter.

astro-ph.IM↗

arXiv2025

A Graph-Based Forensic Framework for Inferring Hardware Noise of Cloud Quantum Backend

Cloud quantum platforms give users access to many backends with different qubit technologies, coupling layouts, and noise levels. The execution of a circuit, however, depends on internal allocation and routing policies that are not observable to the user. A provider may redirect jobs to more error-prone regions to conserve resources, balance load or for other opaque reasons, causing degradation in fidelity while still presenting stale or averaged calibration data. This lack of transparency creates a security gap: users cannot verify whether their circuits were executed on the hardware for which they were charged. Forensic methods that infer backend behavior from user-visible artifacts are therefore becoming essential. In this work, we introduce a Graph Neural Network (GNN)-based forensic framework that predicts per-qubit and per-qubit link error rates of an unseen backend using only topology information and aggregated features extracted from transpiled circuits. We construct a dataset from several IBM 27-qubit devices, merge static calibration features with dynamic transpilation features and train separate GNN regressors for one- and two-qubit errors. At inference time, the model operates without access to calibration data from the target backend and reconstructs a complete error map from the features available to the user. Our results on the target backend show accurate recovery of backend error rate, with an average mismatch of approximately 22% for single-qubit errors and 18% for qubit-link errors. The model also exhibits strong ranking agreement, with the ordering induced by predicted error values closely matching that of the actual calibration errors, as reflected by high Spearman correlation. The framework consistently identifies weak links and high-noise qubits and remains robust under realistic temporal noise drift.

quant-ph↗

arXiv2025

Multi-messenger and time-domain astronomy in the 2040s

Multi-messenger astronomy will be transformed in the 2040s by an unprecedented volume of detections from next-generation gravitational wave, high-energy, and ultra-high energy neutrino, cosmic ray, and time domain observatories. This white paper, prepared for the European Southern Observatory (ESO) Expanding Horizons call, outlines the key science questions enabled by this emerging multi-messenger ecosystem, ranging from nucleosynthesis and dense matter physics to cosmology, fundamental physics, and the growth of black holes across cosmic time. We demonstrate that fully exploiting these discoveries requires a step change in optical to near infrared spectroscopic capability, including low latency response, high throughput, and flexible time domain operations across both hemispheres. We argue that without a dedicated large-aperture time domain facility, the scientific return of multi-messenger astronomy in the 2040s will be considerably limited.

astro-ph.IM↗

arXiv2025

Extended Abstract: Synthesizable Low-overhead Circuit-level Countermeasures and Pro-Active Detection Techniques for Power and EM SCA

The gamut of todays internet-connected embedded devices has led to increased concerns regarding the security and confidentiality of data. Most internet-connected embedded devices employ mathematically secure cryptographic algorithms to address security vulnerabilities. Despite such mathematical guarantees, as these algorithms are often implemented in silicon, they leak critical information in terms of power consumption, electromagnetic (EM) radiation, timing, cache hits and misses, photonic emission and so on, leading to side-channel analysis (SCA) attacks. This thesis focuses on low overhead generic circuit-level yet synthesizable countermeasures against power and EM SCA. Existing countermeasures (including proposed) still have relatively high overhead which bars them from being used in energy-constraint IoT devices. We propose a zero-overhead integrated inductive sensor which is able to detect i)EM SCA ii) Clock glitch-based Fault Injection Attack (FIA), and iii) Voltage-glitch based Fault Injection Attack by using a simple ML algorithm. Advent of quantum computer research will open new possibilities for theoretical attacks against existing cryptographic protocols. National Institute of Standard & Technology (NIST) has standardized post-quantum cryptographic algorithms to secure crypto-systems against quantum adversary. I contribute to the standardization procedure by introducing the first silicon-verified Saber (a NIST finalist modulo Learning with Rounding scheme) which consumes lowest energy and area till date amongst all the candidates.

cs.CR↗

arXiv2025

Design-Space Exploration of Distributed Neural Networks in Low-Power Wearable Nodes

Wearable devices are revolutionizing personal technology, but their usability is often hindered by frequent charging due to high power consumption. This paper introduces Distributed Neural Networks (DistNN), a framework that distributes neural network computations between resource-constrained wearable nodes and resource-rich hubs to reduce energy at the node without sacrificing performance. We define a Figure of Merit (FoM) to select the optimal split point that minimizes node-side energy. A custom hardware design using low-precision fixed-point arithmetic achieves ultra-low power while maintaining accuracy. The proposed system is ~1000x more energy efficient than a GPU and averages 11x lower power than recent machine learning (ML) ASICs at 30 fps. Evaluated with CNNs and autoencoders, DistNN attains an SSIM of 0.90 for image reconstruction and 0.89 for denoising, enabling scalable, energy-efficient, real-time wearable applications.

cs.ET↗

arXiv2025

Design Automation in Quantum Error Correction

Quantum error correction (QEC) underpins practical fault-tolerant quantum computing (FTQC) by addressing the fragility of quantum states and mitigating decoherence-induced errors. As quantum devices scale, integrating robust QEC protocols is imperative to suppress logical error rates below threshold and ensure reliable operation, though current frameworks suffer from substantial qubit overheads and hardware inefficiencies. Design automation in the QEC flow is thus critical, enabling automated synthesis, transpilation, layout, and verification of error-corrected circuits to reduce qubit footprints and push fault-tolerance margins. This chapter presents a comprehensive treatment of design automation in QEC, structured into four main sections. The first section delves into the theoretical aspects of QEC, covering logical versus physical qubit representations, stabilizer code construction, and error syndrome extraction mechanisms. In the second section, we outline the QEC design flow, detailing the areas highlighting the need for design automation. The third section surveys recent advancements in design automation techniques, including algorithmic $T$-gate optimization, modified surface code architecture to incorporate lesser qubit overhead, and machine-learning-based decoder automation. The final section examines near-term FTQC architectures, integrating automated QEC pipelines into scalable hardware platforms and discussing end-to-end verification methodologies. Each section is complemented by case studies of recent research works, illustrating practical implementations and performance trade-offs. Collectively, this chapter aims to equip readers with a holistic understanding of design automation in QEC system design in the fault-tolerant landscape of quantum computing.

quant-ph↗

arXiv2025

Adversarial Threats in Quantum Machine Learning: A Survey of Attacks and Defenses

Quantum Machine Learning (QML) integrates quantum computing with classical machine learning, primarily to solve classification, regression and generative tasks. However, its rapid development raises critical security challenges in the Noisy Intermediate-Scale Quantum (NISQ) era. This chapter examines adversarial threats unique to QML systems, focusing on vulnerabilities in cloud-based deployments, hybrid architectures, and quantum generative models. Key attack vectors include model stealing via transpilation or output extraction, data poisoning through quantum-specific perturbations, reverse engineering of proprietary variational quantum circuits, and backdoor attacks. Adversaries exploit noise-prone quantum hardware and insufficiently secured QML-as-a-Service (QMLaaS) workflows to compromise model integrity, ownership, and functionality. Defense mechanisms leverage quantum properties to counter these threats. Noise signatures from training hardware act as non-invasive watermarks, while hardware-aware obfuscation techniques and ensemble strategies disrupt cloning attempts. Emerging solutions also adapt classical adversarial training and differential privacy to quantum settings, addressing vulnerabilities in quantum neural networks and generative architectures. However, securing QML requires addressing open challenges such as balancing noise levels for reliability and security, mitigating cross-platform attacks, and developing quantum-classical trust frameworks. This chapter summarizes recent advances in attacks and defenses, offering a roadmap for researchers and practitioners to build robust, trustworthy QML systems resilient to evolving adversarial landscapes.

quant-ph↗

arXiv2025

A Hubble constant estimation with dark standard sirens and galaxy cluster catalogues

In this paper, we explore the possibility of using galaxy cluster catalogues to provide redshift support for a gravitational-wave dark standard siren measurement of the Hubble constant $H_0$. We adapt the cosmology inference pipeline gwcosmo to handle galaxy cluster catalogues. Together with binary black holes from the GWTC-3, we use galaxy cluster data from the PSZ2 and the eRASS catalogues. With these catalogues, we obtain $H_0 = 77^{+10}_{-10}$ and $81^{+8}_{-8}\, \text{km}\, \text{s}^{-1}\, \text{Mpc}^{-1}$ respectively, which demonstrates improvements on precision by factors of 10% and 38% respectively over the traditional galaxy catalogue result. This exploratory work paves the way towards precise and accurate cosmography making use of distant compact binary mergers from upcoming observing runs of the LIGO-Virgo-KAGRA detector network and future gravitational-wave observatories.

astro-ph.CO↗

arXiv2025

The Luminosity of the Darkness: Schechter function in dark sirens

Gravitational waves (GWs) offer a novel avenue for probing the Universe. One of their exciting applications is the independent measurement of the Hubble constant, $H_0$, using dark standard sirens, which combine GW signals with galaxy catalogues considering that GW events are hosted by galaxies. However, due to the limited reach of telescopes, galaxy catalogues are incomplete at high redshifts. The commonly used GLADE+ is complete only up to redshift $z=0.1$, necessitating a model accounting for the galaxy luminosity distribution accounting for the selection function of galaxies, typically described by the Schechter function. In this paper, we examine the influence of the Schechter function model on dark sirens, focusing on its redshift evolution and its impact on $H_0$ and rate parameters measurements. We find that neglecting the evolution of the Schechter function can influence the prior in redshift on GWs, which has particularly high impact for distant GW events with limited galaxy catalogue support. Moreover, conducting a joint estimation of $H_0$ and the rate parameters, we find that allowing them to vary fixes the bias in $H_0$ but the rate parameter $\gamma$ depends on the evolving Schechter function. Our results underscore the importance of incorporating an evolving Schechter function to account for changes in galaxy populations over cosmic time, as this impacts rate parameters to which $H_0$ is sensitive.

astro-ph.CO↗

arXiv2025

Dark standard siren cosmology with bright galaxy subsets

In this short paper, we investigate the impact of selecting only a subset of bright galaxies to provide redshift information for a dark standard siren measurement of the Hubble constant $H_0$. Employing gravitational-wave observations from the Third Gravitational-Wave Transient Catalogue (GWTC-3) in conjunction with the GLADE+ galaxy catalogue, we show that restricting to bright galaxy subsets can enhance the precision of the $H_0$ estimate by up to $80\%$ in the most favorable scenario. A comprehensive assessment of systematic uncertainties is still required. This work lays the foundation for employing alternative tracers -- such as brightest cluster galaxies (BCGs) and luminous red galaxies (LRGs) -- in gravitational-wave cosmology, particularly at redshifts where conventional galaxy catalogues offer limited coverage.

astro-ph.CO↗

arXiv2025

Capturing Quantum Snapshots from a Single Copy via Mid-Circuit Measurement and Dynamic Circuit

We propose Quantum Snapshot with Dynamic Circuit (QSDC), a hardware-agnostic, learning-driven framework for capturing quantum snapshots: non-destructive estimates of quantum states at arbitrary points within a quantum circuit, which can then be classically stored and later reconstructed. This functionality is vital for introspection, debugging, and memory in quantum systems, yet remains fundamentally constrained by the no-cloning theorem and the destructive nature of measurement. QSDC introduces a guess-and-check methodology in which a classical model, powered by either gradient-based neural networks or gradient-free evolutionary strategie, is trained to reconstruct an unknown quantum state using fidelity from the SWAP test as the sole feedback signal. Our approach supports single-copy, mid-circuit state reconstruction, assuming hardware with dynamic circuit support and sufficient coherence time. We validate core components of QSDC both in simulation and on IBM quantum hardware. In noiseless settings, our models achieve average fidelity up to 0.999 across 100 random quantum states; on real devices, we accurately reconstruct known single-qubit states (e.g., Hadamard) within three optimization steps.

quant-ph↗

arXiv2025

Survival of the Optimized: An Evolutionary Approach to T-depth Reduction

Quantum Error Correction (QEC) is the cornerstone of practical Fault-Tolerant Quantum Computing (FTQC), but incurs enormous resource overheads. Circuits must decompose into Clifford+T gates, and the non-transversal T gates demand costly magic-state distillation. As circuit complexity grows, sequential T-gate layers ("T-depth") increase, amplifying the spatiotemporal overhead of QEC. Optimizing T-depth is NP-hard, and existing greedy or brute-force strategies are either inefficient or computationally prohibitive. We frame T-depth reduction as a search optimization problem and present a Genetic Algorithm (GA) framework that approximates optimal layer-merge patterns across the non-convex search space. We introduce a mathematical formulation of the circuit expansion for systematic layer reordering and a greedy initial merge-pair selection, accelerating the convergence and enhancing the solution quality. In our benchmark with ~90-100 qubits, our method reduces T-depth by 79.23% and overall T-count by 41.86%. Compared to the reversible circuit benchmarks, we achieve a 2.58x improvement in T-depth over the state-of-the-art methods, demonstrating its viability for near-term FTQC.

quant-ph↗

arXiv2025

The Art of Optimizing T-Depth for Quantum Error Correction in Large-Scale Quantum Computing

Quantum Error Correction (QEC), combined with magic state distillation, ensures fault tolerance in large-scale quantum computation. To apply QEC, a circuit must first be transformed into a non-Clifford (or T) gate set. T-depth, the number of sequential T-gate layers, determines the magic state cost, impacting both spatial and temporal overhead. Minimizing T-depth is crucial for optimizing resource efficiency in fault-tolerant quantum computing. While QEC scalability has been widely studied, T-depth reduction remains an overlooked challenge. We establish that T-depth reduction is an NP-hard problem and systematically evaluate multiple approximation techniques: greedy, divide-and-conquer, Lookahead-based brute force, and graph-based. The Lookahead-based brute-force algorithm (partition size 4) performs best, optimizing 90\% of reducible cases (i.e., circuits where at least one algorithm achieved optimization) with an average T-depth reduction of around 51\%. Additionally, we introduce an expansion factor-based identity gate insertion strategy, leveraging controlled redundancy to achieve deeper reductions in circuits initially classified as non-reducible. With this approach, we successfully convert up to 25\% of non-reducible circuits into reducible ones, while achieving an additional average reduction of up to 11.8\%. Furthermore, we analyze the impact of different expansion factor values and explore how varying the partition size in the Lookahead-based brute-force algorithm influences the quality of T-depth reduction.

quant-ph↗