arXiv subjects

Ruchika Gupta

Publications and source records attributed to Ruchika Gupta.

7 recordsLinked to original sources

arXiv2026

Adaptive Entanglement Management in Quantum Multi-Core Architectures

Scalable quantum computing architectures increasingly rely on multi-core designs, where qubits are distributed across multiple processing cores interconnected through a quantum Network-on-Chip (NoC). In such systems, inter-core communication is typically realized through entanglement-assisted quantum teleportation, making efficient entanglement generation critical for performance. In this paper, we perform a comparative study of three entanglement management paradigms for multi-core quantum processors: reactive on-demand generation (ODG), proactive continuous pre-generation (CGP), and an adaptive continuous pre-generation approach (ACGP). While ODG generates entanglement only when required, CGP reduces average teleportation latency by pre-generating EPR pairs in the background. To improve upon this, we propose ACGP which dynamically adjusts entanglement generation probabilities based on observed inter-core communication patterns. We evaluate these approaches using an extended SeQUeNCe simulator on mesh-based multi-core architectures on real benchmark circuits. Results show that ACGP significantly reduces average teleportation latency compared to ODG and CGP. Although pre-generation introduces fidelity degradation due to storage time, entanglement purification effectively restores fidelity with minimal impact on latency. These results demonstrate that adaptive entanglement managements can substantially improve communication efficiency in scalable quantum multi-core systems.

quant-ph↗

arXiv2026

Dependency-Aware Circuit Scheduling for Multi-Core Quantum Systems to Minimize Makespan

Multi-core quantum computing architectures have emerged as a promising solution to the qubit scalability limitations of monolithic NISQ devices. Quantum algorithms are expressed as quantum circuits composed of single- and two-qubit gates. However, circuit scheduling in multi-core quantum systems remains largely unexplored. Reducing overall execution time (makespan), increasing core utilization, and hiding communication latency behind computation depends on effective scheduling. In this paper, we first introduce a layered scheduling approach as a baseline where quantum gates within the same layer are executed in parallel, while layers themselves are executed sequentially. We then propose a greedy scheduling strategy which schedules each gate as soon as all its dependencies and required resources are available. This allows fine-grained parallelism across cores. Our evaluation shows that on real benchmarks, greedy scheduling achieves an average 40% reduction in makespan and improvement in core utilization. The results suggest that the use of intelligent circuit scheduling to exploit parallelism can greatly enhance the speed of circuit execution in multi-core quantum architectures.

quant-ph↗

arXiv2026

The Effects of Population Size on the Performance of BEAGLE GPU-Based Genetic Programming Runs

The Beagle framework, through GPU-based Genetic Programming, enables population dynamics previously unattainable (within practical time frames) by CPU-constrained Genetic Programming systems. This work explores how GPU-enabled population sizes impact the success of training for symbolic regression problems. Specifically, when using constant population sizes, we see benefits of using very narrow and deep searches (as narrow as 1000 individuals) for some problems, while other problems benefit from very broad and shallow searches (as broad as 10 million individuals). We also explore stepped population sizes that start with large populations and drop to small populations to balance the breadth and depth of search.

cs.NE↗

arXiv2026

EvoIQA - Explaining Image Distortions with Evolved White-Box Logic

Traditional Image Quality Assessment (IQA) metrics typically fall into one of two extremes: rigid, hand-crafted mathematical models or "black-box" deep learning architectures that completely lack interpretability. To bridge this gap, we propose EvoIQA, a fully explainable symbolic regression framework based on Genetic Programming that Evolves explicit, human-readable mathematical formulas for image quality assessment (IQA). Utilizing a rich terminal set from the VSI, VIF, FSIM, and HaarPSI metrics, our framework inherently maps structural, chromatic, and information-theoretic degradations into observable mathematical equations. Our results demonstrate that the evolved GP models consistently achieve strong alignment between the predictions and human visual preferences. Furthermore, they not only outperform traditional hand-crafted metrics but also achieve performance parity with complex, state-of-the-art deep learning models like DB-CNN, proving that we no longer have to sacrifice interpretability for state-of-the-art performance.

cs.CV↗

arXiv2026

GPU-Accelerated Genetic Programming for Symbolic Regression with Beagle Framework

Beagle is a new software framework that enables execution of Genetic Programming tasks on the GPU. Currently available for symbolic regression, it processes individuals of the population and fitness cases for training in a way that maximizes throughput on extant GPU platforms. In this contribution, we report on the benchmarking of Beagle on the Feynman Symbolic Regression dataset and compare its performance with a fast CPU system called StackGP and the widely available PySR system under the same wall clock budget. We also report on the use of two different fitness functions, one a point-to-point error function, the other a correlation fitness function. The results demonstrate that the Beagle's GPU-aided Symbolic Regression significantly outperforms leading CPU-based frameworks.

cs.NE↗

arXiv2025

Benchmarking is Broken -- Don't Let AI be its Own Judge

The meteoric rise of AI, with its rapidly expanding market capitalization, presents both transformative opportunities and critical challenges. Chief among these is the urgent need for a new, unified paradigm for trustworthy evaluation, as current benchmarks increasingly reveal critical vulnerabilities. Issues like data contamination and selective reporting by model developers fuel hype, while inadequate data quality control can lead to biased evaluations that, even if unintentionally, may favor specific approaches. As a flood of participants enters the AI space, this "Wild West" of assessment makes distinguishing genuine progress from exaggerated claims exceptionally difficult. Such ambiguity blurs scientific signals and erodes public confidence, much as unchecked claims would destabilize financial markets reliant on credible oversight from agencies like Moody's. In high-stakes human examinations (e.g., SAT, GRE), substantial effort is devoted to ensuring fairness and credibility; why settle for less in evaluating AI, especially given its profound societal impact? This position paper argues that the current laissez-faire approach is unsustainable. We contend that true, sustainable AI advancement demands a paradigm shift: a unified, live, and quality-controlled benchmarking framework robust by construction, not by mere courtesy and goodwill. To this end, we dissect the systemic flaws undermining today's AI evaluation, distill the essential requirements for a new generation of assessments, and introduce PeerBench (with its prototype implementation at https://www.peerbench.ai/), a community-governed, proctored evaluation blueprint that embodies this paradigm through sealed execution, item banking with rolling renewal, and delayed transparency. Our goal is to pave the way for evaluations that can restore integrity and deliver genuinely trustworthy measures of AI progress.

cs.AI↗

arXiv2025

Decentralized Framework for Teleportation in Quantum Core Interconnects

Multi-core quantum computing architectures offer a promising and scalable solution to the challenges of integrating large number of qubits into existing monolithic chip design. However, the issue of transferring quantum information across the cores remains unresolved. Quantum Teleportation offers a potential approach for efficient qubit transfer, but existing methods primarily rely on centralized interconnection mechanisms for teleportation, which may limit scalability and parallel communication. We proposes a decentralized framework for teleportation in multi-core quantum computing systems, aiming to address these limitations. We introduce two variants of teleportation within the decentralized framework and evaluate their impact on reducing end-to-end communication delay and quantum circuit depth. Our findings demonstrate that the optimized teleportation strategy, termed two-way teleportation, results in a substantial 40% reduction in end-to-end communication latency for synthetic benchmarks and a 30% reduction for real benchmark applications, and 24% decrease in circuit depth compared to the baseline teleportation strategy. These results highlight the significant potential of decentralized teleportation to improve the performance of large-scale quantum systems, offering a scalable and efficient solution for future quantum architectures.

quant-ph↗