SearcharxivSearch

arXiv subjects

Amy Babay

Publications and source records attributed to Amy Babay.

10 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

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

Controlling Epidemic Spread using Probabilistic Diffusion Models on Networks

The spread of an epidemic is often modeled by an SIR random process on a social network graph. The MinINF problem for optimal social distancing involves minimizing the expected number of infections, when we are allowed to break at most $B$ edges; similarly the MinINFNode problem involves removing at most $B$ vertices. These are fundamental problems in epidemiology and network science. While a number of heuristics have been considered, the complexity of these problems remains generally open. In this paper, we present two bicriteria approximation algorithms for MinINF, which give the first non-trivial approximations for this problem. The first is based on the cut sparsification result of Karger \cite{karger:mathor99}, and works when the transmission probabilities are not too small. The second is a Sample Average Approximation (SAA) based algorithm, which we analyze for the Chung-Lu random graph model. We also extend some of our results to tackle the MinINFNode problem.

cs.DS

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

Characterizing Demand Graphs for (Fixed-Parameter) Shallow-Light Steiner Network

We consider the Shallow-Light Steiner Network problem from a fixed-parameter perspective. Given a graph $G$, a distance bound $L$, and $p$ pairs of vertices $(s_1,t_1),\cdots,(s_p,t_p)$, the objective is to find a minimum-cost subgraph $G'$ such that $s_i$ and $t_i$ have distance at most $L$ in $G'$ (for every $i \in [p]$). Our main result is on the fixed-parameter tractability of this problem with parameter $p$. We exactly characterize the demand structures that make the problem "easy", and give FPT algorithms for those cases. In all other cases, we show that the problem is W$[1]$-hard. We also extend our results to handle general edge lengths and costs, precisely characterizing which demands allow for good FPT approximation algorithms and which demands remain W$[1]$-hard even to approximate.

cs.DS