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Prashant Krishnamurthy

Publications and source records attributed to Prashant Krishnamurthy.

13 recordsLinked to original sources

HI-QDC: An Isometric Modular Scalable Architecture for Quantum Data Centers

Server-centric quantum data-center architectures offer scalability by distributing communication tasks across QPUs rather than concentrating complexity in a centralized switching core. However, scaling such architectures increases the path length, the number of Bell-state measurements, and the loss of end-to-end fidelity. We ask whether the path diversity of a server-centric topology can be converted into a mechanism for preserving not only rate, but also fidelity. We study this through end-to-end purification as a fidelity-restoration mechanism. First, in a black-box model, we determine the minimum number of raw end-to-end Werner-state copies, each carrying the degraded Werner parameter of a distance-ell path, that purification must consume to recover a single copy matching an elementary link, comparing recursive 2-to-1 and optimized nested r-to-1 purification. Second, we instantiate these requirements in a probabilistic BCube architecture, whose edge-disjoint path diversity supplies the raw copies. Because purification imposes a lower bound on input fidelity, no path redundancy can raise the output below this threshold, which limits scaling. To address it we present the Hop-Independent Quantum Data Center (HI-QDC), an isometric, modular, scalable architecture in which purification transforms end-to-end entanglement across a module into an effective link-level resource for the inter-module topology. Our results identify the regimes in which a BCube module, under end-to-end purification, preserves both the fidelity and the yield of an elementary link. The module then hides its internal hop count and acts as an effective elementary link for a higher-level network. Thus topology supplies the path multiplicity purification requires, while purification converts it into fidelity recovery, enabling recursively scalable quantum data-center networks.

quant-ph

To Purify or Not to Purify: Entanglement Purification under Input Fidelity Asymmetry in Quantum Networks

Entanglement purification with two entangled resource pairs is widely employed in the literature on quantum repeater networks to counteract fidelity degradation introduced by noisy quantum memories and entanglement swapping across multiple hops. Standard purification protocols assume both resource pairs carry identical fidelity. In practice, entanglement generation is stochastic, the two resource pairs are heralded at different times, and so the first pair decoheres in memory while the second is being generated. Thus a fidelity asymmetry is a structural feature of any network operating under realistic memory conditions, leading to the question: when is it beneficial to perform purification? We derive a closed-form fidelity asymmetry tolerance delta(F) that governs whether a purification attempt is beneficial. We determine a universal upper bound delta_max of approximately 0.076 beyond which purification is always counterproductive. Our simulations show that with exponential memory decoherence, purification yields benefits in only approximately 14% of purification attempts on two resource pairs in a two-hop repeater chain. We define three network objectives: fidelity only, time only, and a combination of time and fidelity, to deliver end-to-end entanglement. We show that when the application fidelity requirement is achievable through swapping alone, no-purification is the superior policy, with its advantage increasing with the number of hops. When the fidelity requirement cannot be met with swapping alone and purification is necessary, to be effective, it must be conditioned on delta(F) between resource pairs. We introduce DeltaPurify, a policy that conditions purification decisions on local fidelity information, and show it reduces time-to-serve relative to both naive purification and no-purification across several fidelity thresholds and hops of a repeater chain.

quant-ph

Toward Hop-Independent Fidelity in Quantum Data Centers: Resource Requirements for Entanglement Purification

Quantum data-center networks must distribute entanglement between QPUs over paths whose length grows with system scale, but each entanglement-swapping step reduces the quality of the raw end-to-end state. Topology, multiplexing, and repeated connection attempts can increase the number of raw end-to-end copies available for a request, yet they do not answer the central resource question: whether those copies are sufficient to remove, via entanglement purification, the fidelity loss caused by multi-hop distribution. We study this question through a topology-independent black-box model of the network. Each elementary link is modeled as a Werner state with parameter $w_0$, so ideal swapping over an $\ell$-link path produces equal-quality raw copies with Werner parameter $w_0^\ell$; purification succeeds if it outputs at least one state with Werner parameter at least $w_0$ with probability at least $p_{\mathrm{th}}$. We compare recursive BBPSSW purification with higher-order $r$-to-$1$ bilocal-Clifford purification protocols of Jansen \emph{et al.}, using an all-in recursive schedule whose success probability is computed by exact dynamic programming. The resulting resource landscapes show a threshold structure governed by the Werner entanglement condition $w_0^\ell>1/3$ and demonstrate that multi-copy purification substantially improves both feasibility and copy efficiency. Across the evaluated grid, the Jansen family requires fewer copies than BBPSSW at more than $96\%$ of shared feasible points; at $p_{\mathrm{th}}=0.70$, the median copy budget drops from $268$ to $30$. These results provide a quantitative purification-resource benchmark for assessing whether future quantum data-center architectures can practically support hop-independent end-to-end entanglement quality.

quant-ph

Sequential vs. Simultaneous Entanglement Swapping under Optimal Link-Layer Control

Connection-less, packet-switched quantum network architectures distribute entanglement across multi-hop paths through sequential entanglement swapping, in which each node acts on purely local state information. The architectural advantages over the connection-oriented alternative -- simultaneous SWAP-ASAP -- are compelling, but sequential swapping holds partial chains in intermediate buffers between successive swaps, exposing them to memory decoherence in a way simultaneous SWAP-ASAP avoids by design. We present a proof-of-principle study at fixed chain length $n = 4$ in which each elementary link is governed by a fixed reinforcement-learning policy optimizing the secret-key rate of the six-state protocol, leaving the network-layer protocol as the sole independent variable. Sweeping the network-layer memory coherence time $T_c^{\mathrm{ext}}$ over four orders of magnitude reveals a clear regime structure governed by the dimensionless ratio $T_c^{\mathrm{ext}}/\tau$, where $\tau$ is the per-link entanglement heralding latency. Simultaneous SWAP-ASAP delivers a constant rate across the full sweep. Sequential swapping, by contrast, collapses to zero end-to-end deliveries below $T_c^{\mathrm{ext}}/\tau = 25$, and begins recovering at $T_c^{\mathrm{ext}}/\tau = 50$. It remains limited by the simultaneous rate, which it saturates only at the relaxed end of the sweep. These results suggest that the connection-less penalty is a near-term phenomenon tied to present-day memory coherence rather than a fundamental property of sequential swapping.

quant-ph

Fidelity-Guaranteed Entanglement Routing with Distributed Purification Planning

Many quantum-network applications require end-to-end Bell pairs whose fidelity exceeds a request-specific threshold, but existing entanglement routing algorithms either optimize only throughput without regard for fidelity or enforce fidelity guarantees using centralized controllers with global link-state knowledge. We present Q-GUARD, an online entanglement routing algorithm that enforces per-request fidelity thresholds within a distributed protocol model in which nodes exchange link-state information only with their $k$-hop neighbors. After link outcomes are realized in each slot, Q-GUARD builds per-link purification cost tables from realized Bell pairs, allocates per-hop fidelity targets using a Werner-state equal-split rule, and selects between candidate path segments using a segment-local expected-goodput (EXG) metric that jointly accounts for swap success, purification overhead, and resource availability. We also introduce Q-GUARD-WS, an extension that exploits per-link hardware quality estimates to allocate purification effort non-uniformly across hops. On synthetic 100-node topologies with heterogeneous link fidelity and stochastic BBPSSW purification, Q-GUARD raises the qualified success rate from under 20\% to over 85\% on 4-hop paths and nearly doubles the qualified service radius in Euclidean distance relative to throughput-only and naive-purification baselines, while Q-GUARD-WS provides additional throughput gains under high hardware heterogeneity.

cs.NI

On-Chain Decentralized Learning and Cost-Effective Inference for DeFi Attack Mitigation

Billions of dollars are lost every year in DeFi platforms by transactions exploiting business logic or accounting vulnerabilities. Existing defenses focus on static code analysis, public mempool screening, attacker contract detection, or trusted off-chain monitors, none of which prevents exploits submitted through private relays or malicious contracts that execute within the same block. We present the first decentralized, fully on-chain learning framework that: (i) performs gas-prohibitive computation on Layer-2 to reduce cost, (ii) propagates verified model updates to Layer-1, and (iii) enables gas-bounded, low-latency inference inside smart contracts. A novel Proof-of-Improvement (PoIm) protocol governs the training process and verifies each decentralized micro update as a self-verifying training transaction. Updates are accepted by \textit{PoIm} only if they demonstrably improve at least one core metric (e.g., accuracy, F1-score, precision, or recall) on a public benchmark without degrading any of the other core metrics, while adversarial proposals get financially penalized through an adaptable test set for evolving threats. We develop quantization and loop-unrolling techniques that enable inference for logistic regression, SVM, MLPs, CNNs, and gated RNNs (with support for formally verified decision tree inference) within the Ethereum block gas limit, while remaining bit-exact to their off-chain counterparts, formally proven in Z3. We curate 298 unique real-world exploits (2020 - 2025) with 402 exploit transactions across eight EVM chains, collectively responsible for \$3.74 B in losses.

cs.CR

Comparing GHZ-Based Strategies for Multipartite Entanglement Distribution in 2D Repeater Networks

We conduct a comparative study to determine the initial quality necessary to extend the distance range of an $N$-qubit GHZ state (the parent state) using two-dimensional repeaters. We analyzed two strategies for distributing initial GHZ states using a centralized quantum switch to determine if any of the strategies show benefits: i) A strategy that employs quantum memories at the switch to retain quantum states entangled with each client node, where memory usage at the switch scales linearly with the number of clients, and ii) A strategy predicated on GHZ measurements at the switch node without reliance on memory assistance. In the former scenario, the switches generate GHZ states and teleport them to the clients by utilizing remote Bell pairs that are asynchronously generated and stored in memory. Conversely, in the latter scenario, the switches perform GHZ projective measurements on freshly generated remote Bell pairs without requiring local storage at the switch. To enhance the distance range of GHZ-type entanglement distribution, we analyze the two approaches as foundational elements for a self-repeating, two-dimensional quantum repeater architecture. Here, the clients of the switch nodes become the 2D repeater nodes that store elementary GHZ states in quantum memories, that can then be fused together to generate long-distance GHZ-type entanglement between end users of the network. By examining the two strategies' entanglement distribution rates and fidelities, we identify the conditions under which the 2D repeater architecture enhances overall performance, and we determine whether either method is a superior building block for such a repeater structure. Our findings illuminate the identification of effective modalities for the long-distance multipartite entanglement distribution within quantum networks.

quant-ph

On Selecting Paths for End-to-End Entanglement Creation in Quantum Networks

Optimal routing is a fundamental challenge in quantum networking, with several approaches proposed to identify the most efficient path for end-to-end (e2e) entanglement generation between pairs of nodes. In this paper, we show that \textit{prior entanglements} -- entanglements generated in a previous network cycle but not yet utilized -- are an important consideration in optimal path selection due to the dynamic nature of quantum networks. Specifically, we investigate whether a longer path with pre-existing entanglements can outperform a shorter path that starts from scratch. We account for key quantum constraints, including noisy entanglement generation and swapping, fidelity decay, probabilistic operations, and link discarding upon swap failure. Simulations reveal that longer paths with prior entanglements can establish e2e entanglement faster than shorter paths under certain conditions. We further introduce the notion of \textit{entanglement diversity}, where multiple paths can be used to improve performance -- either by selecting the first successful path to minimize time or using both paths to enhance fidelity through distillation. These findings highlight the importance of incorporating prior entanglements into path selection strategies for optimizing quantum communication networks.

quant-ph

Protecting DeFi Platforms against Non-Price Flash Loan Attacks

Smart contracts in Decentralized Finance (DeFi) platforms are attractive targets for attacks as their vulnerabilities can lead to massive amounts of financial losses. Flash loan attacks, in particular, pose a major threat to DeFi protocols that hold a Total Value Locked (TVL) exceeding \$106 billion. These attacks use the atomicity property of blockchains to drain funds from smart contracts in a single transaction. While existing research primarily focuses on price manipulation attacks, such as oracle manipulation, mitigating non-price flash loan attacks that often exploit smart contracts' zero-day vulnerabilities remains largely unaddressed. These attacks are challenging to detect because of their unique patterns, time sensitivity, and complexity. In this paper, we present FlashGuard, a runtime detection and mitigation method for non-price flash loan attacks. Our approach targets smart contract function signatures to identify attacks in real-time and counterattack by disrupting the attack transaction atomicity by leveraging the short window when transactions are visible in the mempool but not yet confirmed. When FlashGuard detects an attack, it dispatches a stealthy dusting counterattack transaction to miners to change the victim contract's state which disrupts the attack's atomicity and forces the attack transaction to revert. We evaluate our approach using 20 historical attacks and several unseen attacks. FlashGuard achieves an average real-time detection latency of 150.31ms, a detection accuracy of over 99.93\%, and an average disruption time of 410.92ms. FlashGuard could have potentially rescued over \$405.71 million in losses if it were deployed prior to these attack instances. FlashGuard demonstrates significant potential as a DeFi security solution to mitigate and handle rising threats of non-price flash loan attacks.

cs.CR

TBAM: Towards An Agent-Based Model to Enrich Twitter Data

Twitter (one example of microblogging) is widely being used by researchers to understand human behavior, specifically how people behave when a significant event occurs and how it changes user microblogging patterns. The changing microblogging behavior can reveal patterns that can help in detecting real-world events. However, the Twitter data that is available has limitations, such as, it is incomplete and noisy and the samples are irregular. In this paper we create a model, called Twitter Behavior Agent-Based Model (TBAM) to simulate Twitter pattern and behavior using Agent-Based Modeling (ABM). The generated data from ABM simulations can be used in place or to complement the real-world data toward improving the accuracy of event detection. We confirm the validity of our model by finding the cross-correlation between the real data collected from Twitter and the data generated using TBAM.

cs.SI

Spotting Anomalous Trades in NFT Markets: The Case of NBA Topshot

Non-Fungible Token (NFT) markets are one of the fastest growing digital markets today, with the sales during the third quarter of 2021 exceeding $10 billions! Nevertheless, these emerging markets - similar to traditional emerging marketplaces - can be seen as a great opportunity for illegal activities (e.g., money laundering, sale of illegal goods etc.). In this study we focus on a specific marketplace, namely NBA TopShot, that facilitates the purchase and (peer-to-peer) trading of sports collectibles. Our objective is to build a framework that is able to label peer-to-peer transactions on the platform as anomalous or not. To achieve our objective we begin by building a model for the profit to be made by selling a specific collectible on the platform. We then use RFCDE - a random forest model for the conditional density of the dependent variable - to model the errors from the profit models. This step allows us to estimate the probability of a transaction being anomalous. We finally label as anomalous any transaction whose aforementioned probability is less than 1%. Given the absence of ground truth for evaluating the model in terms of its classification of transactions, we analyze the trade networks formed from these anomalous transactions and compare it with the full trade network of the platform. Our results indicate that these two networks are statistically different when it comes to network metrics such as, edge density, closure, node centrality and node degree distribution. This network analysis provides additional evidence that these transactions do not follow the same patterns that the rest of the trades on the platform follow. However, we would like to emphasize here that this does not mean that these transactions are also illegal. These transactions will need to be further audited from the appropriate entities to verify whether or not they are illicit.

cs.SI

Gaming the Game: Honeypot Venues Against Cheaters in Location-based Social Networks

The proliferation of location-based social networks (LBSNs) has provided the community with an abundant source of information that can be exploited and used in many different ways. LBSNs offer a number of conveniences to its participants, such as - but not limited to - a list of places in the vicinity of a user, recommendations for an area never explored before provided by other peers, tracking of friends, monetary rewards in the form of special deals from the venues visited as well as a cheap way of advertisement for the latter. However, service convenience and security have followed disjoint paths in LBSNs and users can misuse the offered features. The major threat for the service providers is that of fake check-ins. Users can easily manipulate the localization module of the underlying application and declare their presence in a counterfeit location. The incentives for these behaviors can be both earning monetary as well as virtual rewards. Therefore, while fake check-ins driven from the former motive can cause monetary losses, those aiming in virtual rewards are also harmful. In particular, they can significantly degrade the services offered from the LBSN providers (such as recommendations) or third parties using these data (e.g., urban planners). In this paper, we propose and analyze a honeypot venue-based solution, enhanced with a challenge-response scheme, that flags users who are generating fake spatial information. We believe that our work will stimulate further research on this important topic and will provide new directions with regards to possible solutions.

cs.SI

Cognitive Radio Networks: Realistic or Not?

A large volume of research has been conducted in the cognitive radio (CR) area the last decade. However, the deployment of a commercial CR network is yet to emerge. A large portion of the existing literature does not build on real world scenarios, hence, neglecting various important interactions of the research with commercial telecommunication networks. For instance, a lot of attention has been paid to spectrum sensing as the front line functionality that needs to be completed in an efficient and accurate manner to enable an opportunistic CR network architecture. This is necessary to detect the existence of spectrum holes without which no other procedure can be fulfilled. However, simply sensing (cooperatively or not) the energy received from a primary transmitter cannot enable correct dynamic spectrum access. For example, the low strength of a primary transmitter's signal does not assure that there will be no interference to a nearby primary receiver. In addition, the presence of a primary transmitter's signal does not mean that CR network users cannot access the spectrum since there might not be any primary receiver in the vicinity. Despite the existing elegant and clever solutions to the DSA problem no robust, implementable scheme has emerged. In this paper, we challenge the basic premises of the proposed schemes. We further argue that addressing the technical challenges we face in deploying robust CR networks can only be achieved if we radically change the way we design their basic functionalities. In support of our argument, we present a set of real-world scenarios, inspired by realistic settings in commercial telecommunications networks, focusing on spectrum sensing as a basic and critical functionality in the deployment of CRs. We use these scenarios to show why existing DSA paradigms are not amenable to realistic deployment in complex wireless environments.

cs.NI