arXiv subjects

Rodney Van Meter

Publications and source records attributed to Rodney Van Meter.

At least 19 recordsLinked to original sources

arXiv2026

Topology-Dependent Enhancement of Entanglement Extraction in Repeater Graph States

Quantum repeaters are essential for establishing long-distance quantum communication to overcome the exponential decay of entanglement due to photon loss. Traditional repeater architectures rely on physical quantum memory, which introduces decoherence and poses significant practical implementation challenges. The repeater graph state (RGS) architecture offers a promising memory-less alternative that is inherently resilient to photon losses. A key challenge in implementing RGS lies in the requirement for highly efficient graph state generators and complex qubit measurement. In this work, we aim to investigate the strategies for extracting the maximum number of Bell pairs from the RGS structure via its qubit connection to resolve the well-known bottleneck problem of RGS in which only a single Bell pair can be extracted from a complete bipartite graph state. From simulations, we observe that the maximum number of extracted Bell pairs depends on its connection topology, where the Bell-pair yield tends to be maximal at low to moderate edge densities. As the number of network hops increases, the RGS must be equipped with higher inner-qubit connectivity to maintain a sufficient yield of extractable Bell pairs. Thus, the expected resource requirement shifts toward the use of RGSs with higher inner-qubit connectivity.

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arXiv2026

Promise and Challenges of Distimation

Estimating the quality of raw entangled states and distilling high-fidelity entanglement traditionally require two separate link-layer protocols in a quantum network stack, each consuming its own share of fragile entangled pairs. The recently introduced distimation concept merges these two protocols by directly extracting state estimation data from the classical syndromes generated during entanglement distillation. By removing the dedicated "test-then-distill'' stage, distimation lowers the number of raw entangled pairs required to operate the network, reduces latency, and streamlines the network control plane. The paradigm is especially attractive for near-term hardware platforms, where entanglement generation remains a severe bottleneck. Alongside these promises, distimation introduces new engineering challenges, ranging from accurate local-device modeling to protocols for tracking time-varying sources. This article surveys the core principles of distimation, quantifies expected gains for realistic architectures, and outlines a research roadmap toward making distimation a standard building block in future quantum networks.

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arXiv2026

Performance Analysis of QAOA Across Distributed Quantum Network Topologies Using SwitchQNet

Quantum data-center (QDC) architectures aim to scale distributed quantum computing (DQC) by interconnecting multiple quantum processing units (QPUs), but their performance depends strongly on how algorithmic communication patterns interact with entanglement generation, switch reconfiguration, and network topology. This paper studies the Quantum Approximate Optimization Algorithm (QAOA) as a graph-structured optimization workload for QDC-based distributed quantum computing. We adapt QAOA to SwitchQNet, a distributed quantum compiler framework that schedules communication and entanglement generation over switch-based QDC networks, by adding a routing generator that converts graph-dependent two-qubit cost interactions into remote-CX communication requests across QPUs. Using this extension, we evaluate QAOA instances across Clos, fat-tree, and spine-leaf topologies, measuring communication latency, EPR-pair overhead, EPR wait time, retry overhead, and sensitivity to buffer size, look-ahead depth, communication-qubit count, EPR latency, and EPR fidelity assumptions. The results show that QAOA obtains modest but consistent latency reductions, highlighting its value as a diagnostic benchmark for studying the interaction between algorithm structure, entanglement management, and quantum-network architecture.

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arXiv2026

Structure-Informed Multiple Sequence Alignment: A Formal Model and Hardness Results

We formulate a structure-informed multiple sequence alignment problem, denoted MSA-S. The model abstracts biological sequences as strings and structural information as designated position-pairs. It augments a fixed pairwise string score, defined by a fixed non-gap symbol-pair scoring rule and fixed affine gap penalties, with a binary overlap score on designated position-pairs, which can be interpreted as a contact-map overlap score in structural applications. This yields a fixed-score, integer-valued optimization model suitable for complexity-theoretic analysis. Under this formulation, we show that the decision problem MSA-S-DEC is NP-complete for a broad class of fixed pairwise string scoring schemes. We also show that NP-hardness persists even under the restriction that every designated position-pair set is nonempty and the pair-overlap threshold is strictly positive. For the associated scalarized optimization problem MSA-S-OPT(lambda) with any fixed rational constant lambda >= 1, we further show that, under the canonical unit scheme for the non-gap symbol-pair scoring rule, MSA-S-OPT(lambda) admits no polynomial-time approximation scheme (PTAS) even for two input strings (k = 2), unless P = NP. These results establish a formal complexity-theoretic baseline for structure-informed multiple sequence alignment.

cs.CC↗

arXiv2026

A Distributed Switching Protocol for Quantum Networks

With the advent of the construction and deployment of entanglement-based quantum networks, the efficient use of network resources will become a critical challenge for the scalable operation of such a system. Recently, architectures that incorporate memoryless optical switches have gained attention for forwarding entangled photons. By leveraging these architectures, costly resources such as high efficiency Bell State Analyzers (BSAs) can be shared across the network. Nevertheless, the introduction of switching substantially complicates the process of multiplexing and resource allocation compared to an individual link. In this work, we propose a switching protocol for unbuffered, multidrop quantum networks in a photonic synchronization domain that establishes a link between two end nodes using a shared BSA in the switched network. To achieve this, two end nodes cooperatively select the target BSA node with the lowest path cost and independently reserve each path within the network. Bi-path reservations are performed to allocate resources in a distributed manner. The proposed protocol is evaluated through simulation on Q-Fly network topologies under varying traffic conditions. The results demonstrate high link establishment success with stable performance even under increased network load. These capabilities which are driven by our proposed protocol are an essential way to realize large-scale, managed, and automated quantum networks.

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arXiv2026

Space-Time Tradeoffs of Pauli-Based Computation in Distributed qLDPC Architectures

Pauli-based computation (PBC) provides a universal framework for executing fault-tolerant quantum algorithms using Pauli measurements and magic states. In monolithic architectures, the serialized nature of PBC directly ties runtime to a circuit's T-gate count, making it slow on metrics like circuit depth. However, in distributed quantum computing (DQC), the primary bottleneck is remote Bell pair generation. We investigate the tradeoff between error-correcting code block size and execution time of PBC within the Q-Fly architecture at intermediate scale, limiting individual node capacities to reflect near-term constraints while supplying abundant network nodes to minimize routing and compilation effects. We find that large qLDPC code blocks outperform the surface code baseline in terms of execution time by up to an order of magnitude when evaluated against quantum optimization algorithms. By moving groups of qubits to free nodes to bypass the sequential bottleneck of PBC, the large-block architecture minimizes network operations and achieves faster overall execution. This demonstrates that PBC is a competitive model in the distributed regime, establishing it as a practical compilation baseline for qLDPC systems before invoking more efficient transversal or homological gates.

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arXiv2026

Quantum-Enhanced Processing with Tensor-Network Frontends for Privacy-Aware Federated Medical Diagnosis

We propose a privacy-aware hybrid framework for federated medical image classification that combines tensor-network representation learning, MPC-secured aggregation, and post-aggregation quantum refinement. The framework is motivated by two practical constraints in privacy-aware federated learning: MPC can introduce substantial communication overhead, and direct quantum processing of high-dimensional medical images is unrealistic with a small number of qubits. To address both constraints within a single architecture, client-side tensor-network frontends, Matrix Product State (MPS), Tree Tensor Network (TTN), and Multi-scale Entanglement Renormalization Ansatz (MERA), compress local inputs into compact latent representations, after which a Quantum-Enhanced Processor (QEP) refines the aggregated latent feature through quantum-state embedding and observable-based readout. Experiments on PneumoniaMNIST show that the effect of the QEP is frontend-dependent rather than uniform across architectures. In the present setting, the TTN+QEP combination exhibits the most balanced overall profile. The results also suggest that the QEP behaves more stably when the qubit count is sufficiently matched to the latent dimension, while noisy conditions degrade performance relative to the noiseless setting. The MPC benchmark further shows that communication cost is governed primarily by the dimension of the protected latent representation. This indicates that tensor-network compression plays a dual role: it enables small-qubit quantum processing on compressed latent features and reduces the communication overhead associated with secure aggregation. Taken together, these results support a co-design perspective in which representation compression, post-aggregation quantum refinement, and privacy-aware deployment should be optimized jointly.

quant-ph↗

arXiv2026

Quantum Hamlets: Distributed Compilation of Large Algorithmic Graph States

We investigate the problem of compiling the generation of graph states to arbitrarily many distributed homogeneous quantum processing units (QPUs), providing a scalable partitioning algorithm and graph state generation protocol to minimize the number of Bell pairs required. Current approaches focus on the naive metric of cut edges to estimate the quantum communication cost. We show that the problem of balanced k graph partitioning, with the objective of minimizing the sizes of the maximum matchings between the partitions, leads to lower entanglement requirements across partitions. Our heuristic algorithm, BURY, partitions graph states to require fewer Bell pairs for generation than state-of-the-art k partition algorithms. Furthermore, we show that BURY reduces the cut-rank of the partitions, demonstrating that the partitioning found by our algorithm is likely to minimize the Bell pair utilization of any future improved distributed graph state generation protocol. We also discuss how our methods apply to the dynamic case where the graph state generation and measurement are performed concurrently. Our compilation approach provides a scalable foundation for reducing quantum network overhead for distributed measurement-based quantum computation (MBQC), as well as any scheme where distributed graph state generation is desired.

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arXiv2026

A Tailored Fidelity Estimation and Purification Method for Entangled Quantum Networks

We present a method to conduct both quantum state reconstruction and entanglement purification simultaneously that is advantageous in several respects over previous work in this direction, showing that the number of Bell pairs necessary to boot a quantum network can be significantly reduced compared to an existing method. The existing method requires at least $10^5$ Bell pairs for the state reconstruction phase to estimate that the state is of fidelity $0.99$ within the error range of $10^{-2}$, whereas our approach only requires around $2,841$ to be certain with $99.7\%$ of confidence that the estimated fidelity lies within $[0.99-0.01, 0.99+0.01]$. In addition, in our approach we can start with a lower fidelity Bell pair and purify it multiple times, estimating at the same time the resultant fidelity with guarantee of $99.7\%$ that the fidelity estimate lies within a certain range. Moreover, the existing method cannot correct both bit-flip and phase-flip errors at the same time and can only correct one of these, whereas our approach can correct both bit-flip and phase-flip errors simultaneously. This research produces numerical estimates for the number of Bell pairs actually needed to guarantee a certain threshold fidelity $F$. The research can support the functioning real-world quantum networking by providing the information of the time needed for the bootstrapping of a quantum network to finish.

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arXiv2025

Efficient graph-diagonal characterization of noisy states distributed over quantum networks via Bell sampling

Graph states are an important class of entangled states that serve as a key resource for distributed information processing and communication in quantum networks. In this work, we propose a protocol that utilizes a Bell sampling subroutine to characterize the diagonal elements in the graph basis of noisy graph states distributed across a network. Our approach offers significant advantages over direct diagonal estimation using unentangled single-qubit measurements in terms of scalability. Specifically, we prove that estimating the full vector of diagonal elements requires a sample complexity that scales linearly with the number of qubits ($\mathcal{O}(n)$), providing an exponential reduction in resource overhead compared to the best known $\mathcal{O}(2^n)$ scaling of direct estimation. Furthermore, we demonstrate that global properties, such as state fidelity, can be estimated with a sample complexity independent of the network size. Finally, we present numerical results indicating that the estimation in practice is more efficient than the derived theoretical bounds. Our work thus establishes a promising technique for efficiently estimating noisy graph states in large networks under realistic experimental conditions.

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arXiv2025

Probabilistic Graybox Characterization of Quantum Devices with Bayesian Neural Networks

While the Graybox characterization method allows for implicit noise models and is platform-agnostic, the method lacks uncertainty quantification. Characterization of quantum devices is a crucial process that enables researchers to gain insight from experimental settings. Graybox characterization combines known system dynamics with unknown transformations, where the latter is modeled using machine learning. Prediction uncertainty helps researchers make informed decisions. It allows valuable insights from the devices without overconfidence. We therefore develop a probabilistic Graybox characterization model using probabilistic machine learning, specifically Bayesian Neural Networks, and utilize binary measurement outcomes directly for inference. With stochastic noise in a quantum device, we analyze statistical properties of the measurement data. Our results show that the model's prediction performance solely depends on its ability to capture the expected value of the true expectation value. Our proposed probabilistic Graybox model outperforms the original model by up to 1.9 times in capturing the distribution of observed data. We expect that our results will serve as an additional tool for characterizing quantum devices with uncertainty estimation, as they provide a flexible choice that can be utilized even without extensive prior knowledge of the noise model of the devices.

quant-ph↗

arXiv2025

Graybox characterization and calibration with finite-shot estimation on superconducting-qubit experiments

Characterization and calibration of quantum devices are necessary steps to achieve fault-tolerant quantum computing. As quantum devices become more sophisticated, it is increasingly essential to rely not only on physics-based models, but also on predictive models with open-loop optimization. Therefore, we choose the Graybox approach, which is composed of an explicit (whitebox) model describing the known dynamics and an implicit (blackbox) model describing the noisy dynamics in the form of a deep neural network, to characterize and calibrate superconducting-qubit devices. By sending a set of selected pulses to the devices and measuring Pauli expectation values, the Graybox approach can train the implicit model and optimize gates based on specified loss functions. We also benchmark our optimized gates on the devices and cross-testing predictive models with two types of loss functions, i.e., the mean squared errors (MSE) of expectation values and the absolute errors (AE) of average gate fidelities (AGF). While the Graybox method allows for flexibility of the implicit noise model, its construction relies on a finite measurement shots dataset. We thus apply the decomposition of expected MSE loss to show that the finite-shot estimation of expectation values is the main contribution to the minimum value achievable of the expected MSE loss. We also show that the expected loss is an upper bound of the expected absolute error of AGF between the exact value and model prediction. Our results provide insights for quantum device characterization and gate optimization in experiments where only finite shots of data are available.

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arXiv2025

A double selection entanglement distillation-based state estimator

With the advent of practical quantum communication networks drawing closer, there is a growing need for reliable estimation protocols that can efficiently characterize quantum resources with minimum resource overhead requirement. A novel approach to this problem is to integrate an estimator into an existing network task, thereby removing the need for an additional characterization protocol. In this work, we show that the measurement statistics of a double selection distillation protocol alone can be used to efficiently estimate the Bell-diagonal parameters of the undistilled states, as well as the resulting distilled states after additional post-processing. We also demonstrate that this novel estimator outperforms the previously proposed distillation-based estimator in terms of resource complexity.

quant-ph↗

arXiv2025

A Troubleshooting Framework for Trapping Ions

Practical knowledge about troubleshooting and error handling in trapped-ion systems remains largely undocumented and held within individual labs, creating a barrier to cross-disciplinary collaboration towards engineering scalable systems. This paper presents a structured troubleshooting framework for trapping ions, developed through hands-on experience in the lab. The framework categorizes standard failure modes across subsystems -- vacuum, electronics, optics, and imaging -- and organizes them into a modular decision-tree structure. Each troubleshooting action is annotated with estimates of cost and operational risk inspired by the principles of Failure Mode and Effects Analysis (FMEA). By categorizing failure modes and their associated costs, this work bridges the gap between physicists and engineers, enabling collaborative system design and setting the stage for turning experimental setups into robust, reproducible products. This framework provides a structured basis for future error-handled ion-trap systems, designed with diagnostics and maintainability in mind -- for use in distributed quantum computing and networks.

quant-ph↗

arXiv2025

Automatic Configuration Protocols for Optical Quantum Networks

Before quantum networks can scale up to practical sizes, there are many deployment and configuration tasks that must be automated. Currently, quantum networking testbeds are largely manually configured: network nodes are constructed out of a combination of free-space and fiber optics before being connected to shared single-photon detectors, time-to-digital converters, and optical switches. Information about these connections must be tracked manually; mislabeling may result in experimental failure and protracted debugging sessions. In this paper, we propose protocols and algorithms to automate two such manual processes. First, we address the problem of automatically identifying connections between quantum network nodes and time-to-digital converters. Then, we turn to the more complex challenge of identifying the nodes attached to a quantum network's optical switches. Implementation of these protocols will help enable the development of other protocols necessary for quantum networks, such as network topology discovery, link quality monitoring, resource naming, and routing. We intend for this paper to serve as a roadmap for near-term implementation.

quant-ph↗

arXiv2025

Optimizing Resource Allocation in a Distributed Quantum Computing Cloud: A Game-Theoretic Approach

Quantum cloud computing is essential for achieving quantum supremacy by utilizing multiple quantum computers connected via an entangling network to deliver high performance for practical applications that require extensive computational resources. With such a platform, various clients can execute their quantum jobs (quantum circuits) without needing to manage the quantum hardware and pay based on resource usage. Hence, defining optimal quantum resource allocation is necessary to avoid overcharging clients and to allow quantum cloud providers to maximize resource utilization. Prior work has mainly focused on minimizing communication delays between nodes using multi-objective techniques. Our approach involves analyzing the problem from a game theory perspective. We propose a quantum circuit partitioning resource allocation game model (QC-PRAGM) that minimizes client costs while maximizing resource utilization in quantum cloud environments. We extend QC-PRAGM to QC-PRAGM++ to maximize local gates in a partition by selecting the best combinations of qubits, thereby minimizing both cost and inter-node communication. We demonstrate analytically that clients are charged appropriately (with a total cost at most $\frac{4}{3}$ the optimal cost) while optimizing quantum cloud resources. Further, our simulations indicate that our solutions perform better than traditional ones in terms of the cost per quantum node, total cost, maximum cost, number of partitions, and number of remote gates.

quant-ph↗

arXiv2025

Quantum Circuit Design for Decoded Quantum Interferometry

Decoded Quantum Interferometry (DQI) is a recently proposed quantum algorithm for approximating solutions to combinatorial optimization problems by reducing instances of linear satisfiability to bounded-distance decoding over superpositions of quantum states. A central challenge in realizing DQI is the design of a decoder that operates coherently on quantum superpositions. In this work, we present a concrete quantum circuit implementation of DQI, with a focus on the decoding subroutine. Our design leverages a reversible Gauss-Jordan elimination circuit for the decoding stage. We analyze the circuit's depth and gate complexity and validate its performance through simulations on systems with up to 30 qubits. These results establish a concrete foundation for scalable implementations of DQI and open the door to future algorithmic refinements and hardware-level realizations.

quant-ph↗

arXiv2025

Integrating Entanglement Purification into All-Photonic Quantum Repeaters

We propose a purification-enhanced all-photonic quantum repeater scheme based on repeater graph states (RGS) framework that leverages the recently proposed half-RGS building block. This framework addresses a longstanding open question--how to naturally integrate entanglement purification with an all-photonic scheme--by enabling long-distance purification without disrupting the core design. Our framework utilizes optimistic purification performed directly on the half-RGS primitives across long distances without waiting for heralding outcomes. The overhead is modest: the RGS generation slows down proportionally with the number of purification rounds, and each round requires only one additional quantum emitter per half-RGS source. However, since the generation time is negligible compared to the end-to-end communication delay, the total latency remains effectively dominated by communication time, similar to frameworks without purification. Our framework enables flexible purification scheduling along the connection path, making it compatible with memory-based strategies, a rich body of research on purification scheduling and optimization that was previously thought inapplicable to the RGS scheme. Through numerical evaluation, we compare the performance of our framework with purification between memories at end nodes.

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