arXiv subjects

Poramet Pathumsoot

Publications and source records attributed to Poramet Pathumsoot.

6 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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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.

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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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arXiv2024

Boosting end-to-end entanglement fidelity in quantum repeater networks via hybridized strategies

Quantum networks are expected to enhance distributed quantum computing and quantum communication over long distances while providing security dependent upon physical effects rather than mathematical assumptions. Through simulation, we show that a quantum network utilizing only entanglement purification or only quantum error correction as error management strategies cannot create Bell pairs with fidelity that exceeds the requirement for a secured quantum key distribution protocol for a broad range of hardware parameters. We propose hybrid strategies utilizing quantum error correction on top of purification and show that they can produce Bell pairs of sufficiently high fidelity. We identify the error parameter regime for gate and measurement errors in which these hybrid strategies are applicable.

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arXiv2023

Hybrid Error-Management Strategies in Quantum Repeater Networks

A quantum network is expected to enhance distributed quantum computing and quantum communication over a long distance while providing unconditional security. As quantum entanglement is essential for a quantum network, major issues from various types of noise and decoherence prevent it from being realized, and research has been intensively active to obtain optimal configurations for a quantum network. In this work, we address the performance of a quantum network capable of quantum error correction and entanglement purification. Our results show that one should distribute Bell pairs as fast as possible while balancing the deployment of fidelity enhancement. We also show suitable hybrid strategies in quantum cryptography tasks under some noise regimes that need to use purification and quantum error correction together. Our results suggest that using purification to distribute high fidelity Bell pairs and preserving them for application using quantum error correction is a promising way to achieve a near-term quantum network for secure communication.

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arXiv2019

Modeling of Measurement-based Quantum Network Coding on IBM Q Experience Devices

Quantum network coding has been proposed to improve resource utilization to support distributed computation but has not yet been put in to practice. We investigate a particular implementation of quantum network coding using measurement-based quantum computation on IBM Q processors. We compare the performance of quantum network coding with entanglement swapping and entanglement distribution via linear cluster states. These protocols outperform quantum network coding in terms of the final Bell pair fidelities but are unsuitable for optimal resource utilization in complex networks with contention present. We demonstrate the suitability of noisy intermediate-scale quantum (NISQ) devices such as IBM Q for the study of quantum networks. We also identify the factors that limit the performance of quantum network coding on these processors and provide estimates or error rates required to boost the final Bell pair fidelities to a point where they can be used for generation of genuinely random cryptographic keys among other useful tasks. Surprisingly, the required error rates are only around a factor of 2 smaller than the current status and we expect they will be achieved in the near future.

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