arXiv subjects

Siddhartha Visveswara Jayanti

Publications and source records attributed to Siddhartha Visveswara Jayanti.

3 recordsLinked to original sources

arXiv2026

Asynchronous Quantum Distributed Computing: Causality, Snapshots, and Global Operations

We initiate the study of asynchronous quantum distributed systems, focusing on the case of implementing atomic quantum global operations that can be decomposed into a collection of local operations on the components of the system. A simple example of such an operation is a quantum snapshot in which the whole system is instantaneously measured. Based on the classical snapshot algorithm of Chandy and Lamport, we design a quantum distributed algorithm to implement such decomposable global operations, which we call the QGO Algorithm. The analysis of our algorithm shows that arguments based on Lamport's computational causality remain valid in the quantum world, even though, due to entanglement, causality is not manifest from the standard description of the system in terms of a (global) quantum state. Our other contributions include a formal model of quantum distributed computing, and a formal specification for the desired behavior of a global operation, which may be of interest even in classical settings (such as in the setting of randomized algorithms).

cs.DC↗

arXiv2026

On the First Computer Science Research Paper in an Indian Language and the Future of Science in Indian Languages

I describe my experience writing the first original, modern Computer Science research paper expressed entirely in an Indian language. The paper is in Telugu, a language with approximately 100 million speakers. The paper is in the field of distributed computing and it introduces a technique for proving epistemic logic based lower bounds for multiprocessor algorithms. A key hurdle to writing the paper was developing technical terminology for advanced computer science concepts, including those in algorithms, distributed computing, and discrete mathematics. I overcame this challenge by deriving and coining native language scientific terminology through the powerful, productive, Pāninian grammar of Samskrtam. The typesetting of the paper was an additional challenge, since mathematical typesetting in Telugu is underdeveloped. I overcame this problem by developing a Telugu XeLaTeX template, which I call TeluguTeX. Leveraging this experience of writing an original computer science research paper in an Indian language, I lay out a vision for how to ameliorate the state of scientific writing at all levels in Indic languages -- languages whose native speakers exceed one billion people -- through the further development of the Sanskrit technical lexicon and through technological internationalization.

cs.GL↗

arXiv2022

Generalized Wake-Up: Amortized Shared Memory Lower Bounds for Linearizable Data Structures

In this work, we define the generalized wake-up problem, $GWU(s)$, for a shared memory asynchronous system with $n$ processes. Informally, the problem, which is parametrized by an increasing sequence $s = s_1,\ldots,s_p$, asks that at least $n - i + 1$ processes identify that at least $s_i$ other processes have "woken up" and taken at least one step for each $1 \le i \le n$. We prove that any solution to $GWU(s)$ that uses read/write/compare-and-swap variables requires at least $Ω\left(\sum_{i = 1}^n \log s_i \right)$ steps to solve. The generalized wake-up lower bound serves as a technique for proving lower bounds on the amortized complexities of operations on many linearizable concurrent data types through reductions. We illustrate this with several examples: (1) We show an $Ω(\log n)$ amortized lower bound on the complexity of implementing counters and {\em fetch-and-increment} objects which match the complexities of the algorithms given by Jayanti and Ellen & Woelfel; the lower bound even extends to a significantly relaxed version of the object. (2) We show an $Ω(\log n)$ amortized lower bound on the complexity of the pop, dequeue, and deleteMin operations of a concurrent stack, queue, and priority queue respectively that hold even if the data type definitions are significantly relaxed; (3) In another paper, we have shown an $Ω(\log\log(n \ell/m))$ amortized lower bound on the complexity of operations on a union-find object of size $\ell$ (when $m$ operations are performed).

cs.DS↗