arXiv subjects

Wenhan Dai

Publications and source records attributed to Wenhan Dai.

At least 19 recordsLinked to original sources

arXiv2026

Simulation Study on the Discrimination of $0νββ$ Events from Single-Electron Events Using Orthogonal-Strip HPGe Detectors

Neutrinoless double beta decay ($0νββ$) offers a sensitive probe of neutrino mass and its Majorana nature. Orthogonal-strip high-purity germanium (HPGe) detectors with high spatial resolution provide a promising approach for distinguishing $0νββ$ events from single-electron backgrounds. In this work, a simulation framework was developed to evaluate the discrimination performance of these detectors. The framework combined Geant4 simulations with a hybrid numerical-analytical approach to model charge cloud dynamics. A dual-branch convolutional neural network (CNN) was implemented to extract topological features for event classification. The impact of detector geometry on discrimination performance was quantitatively assessed. For a fixed crystal thickness of 15 mm, the background rejection efficiency decreased from 79.5\% to 59.0\% as the strip pitch increased from 0.1 mm to 0.5 mm. For a strip pitch of 0.25 mm, a crystal thickness of 20 mm was found to be optimal, balancing full-energy peak (FEP) efficiency with discrimination capability. These results demonstrate that orthogonal-strip HPGe detectors can effectively suppress single-electron backgrounds, and provide quantitative guidance for detector design in $^{76}$Ge $0νββ$ decay searches.

physics.ins-det↗

arXiv2023

Resource-efficient simulation of noisy quantum circuits and application to network-enabled QRAM optimization

Giovannetti, Lloyd, and Maccone [Phys. Rev. Lett. 100, 160501] proposed a quantum random access memory (QRAM) architecture to retrieve arbitrary superpositions of $N$ (quantum) memory cells via $O(\log(N))$ quantum switches and $O(\log(N))$ address qubits. Towards physical QRAM implementations, Chen et al. [PRX Quantum 2, 030319] recently showed that QRAM maps natively onto optically connected quantum networks with $O(\log(N))$ overhead and built-in error detection. However, modeling QRAM on large networks has been stymied by exponentially rising classical compute requirements. Here, we address this bottleneck by: (i) introducing a resource-efficient method for simulating large-scale noisy entanglement, allowing us to evaluate hundreds and even thousands of qubits under various noise channels; and (ii) analyzing Chen et al.'s network-based QRAM as an application at the scale of quantum data centers or near-term quantum internet; and (iii) introducing a modified network-based QRAM architecture to improve quantum fidelity and access rate. We conclude that network-based QRAM could be built with existing or near-term technologies leveraging photonic integrated circuits and atomic or atom-like quantum memories.

quant-ph↗

arXiv2023

Universal Quantum Walk Control Plane for Quantum Networks

Quantum networks are complex systems formed by the interaction among quantum processors through quantum channels. Analogous to classical computer networks, quantum networks allow for the distribution of quantum operations among quantum processors. In this work, we describe a Quantum Walk Control Protocol (QWCP) to perform distributed quantum operations in a quantum network. We consider a generalization of the discrete-time coined quantum walk model that accounts for the interaction between quantum walks in the network graph with quantum registers inside the network nodes. QWCP allows for the implementation of networked quantum services, such as distributed quantum computing and entanglement distribution, abstracting hardware implementation and the transmission of quantum information through channels. Multiple interacting quantum walks can be used to propagate entangled control signals across the network in parallel. We demonstrate how to use QWCP to perform distributed multi-qubit controlled gates, which shows the universality of the protocol for distributed quantum computing. Furthermore, we apply the QWCP to the task of entanglement distribution in a quantum network.

quant-ph↗

arXiv2023

Entanglement Swapping in Quantum Switches: Protocol Design and Stability Analysis

Quantum switches are critical components in quantum networks, distributing maximally entangled pairs among end nodes by entanglement swapping. In this work, we design protocols that schedule entanglement swapping operations in quantum switches. Entanglement requests randomly arrive at the switch, and the goal of an entanglement swapping protocol is to stabilize the quantum switch so that the number of unfinished entanglement requests is bounded with a high probability. We determine the capacity region for the rates of entanglement requests and develop entanglement swapping protocols to stabilize the switch. Among these protocols, the on-demand protocols are not only computationally efficient, but also achieve high fidelity and low latency demonstrated by results obtained using a quantum network discrete event simulator.

quant-ph↗

arXiv2023

Zero-Added-Loss Entangled Photon Multiplexing for Ground- and Space-Based Quantum Networks

We propose a scheme for optical entanglement distribution in quantum networks based on a quasi-deterministic entangled photon pair source. By combining heralded photonic Bell pair generation with spectral mode conversion to interface with quantum memories, the scheme eliminates switching losses due to multiplexing. We analyze this `zero-added-loss multiplexing' (ZALM) Bell pair source for the particularly challenging problem of long-baseline entanglement distribution via satellites and ground-based memories, where it unlocks additional advantages: (i) the substantially higher channel efficiency $η$ of \textit{downlinks} vs.\ \textit{uplinks} with realistic adaptive optics, and (ii) photon loss occurring \textit{before} interaction with the quantum memory -- i.e., Alice and Bob receiving rather than transmitting -- improve entanglement generation rate scaling by $\mathcal{O}(\sqrtη)$. Based on numerical analyses, we estimate our protocol to achieve $>$10$~$ebits/s at memory multiplexing of $10^2$ spin qubits for ground distance $>$10$^2~$km, with the spin-spin Bell state fidelity exceeding 99$\%$. Our architecture presents a blueprint for realizing global-scale quantum networks in the near-term.

quant-ph↗

arXiv2022

A feasibility study of multi-electrode high-purity germanium detector for Ge-76 neutrinoless double beta decay searching

Experiments to search for neutrinoless double-beta (0ν\b{eta}\b{eta}) decay of 76Ge using a high-purity germanium (HPGe) detector rely heavily on background suppression technologies to enhance their sensitivities. In this work, we proposed a pulse-shape analysis method based on a neural network (NN) and a light gradient boosting machine (lightGBM; LGB) to discriminate single-electron (background) and double-electrons (0ν\b{eta}\b{eta} signal) events in a multi-electrode HPGe detector. In this paper, we describe a multi-electrode HPGe detector system, a data-processing system, and pulse-shape simulation procedures. We built a fully connected (FC) neural network and an LGB model to classify the single- and double-electron events. The FC network is trained with simulated single- and double-electron-induced pulses and tested in an independent dataset generated by the pulse-shape simulation. The discrimination efficiency of the FC neural network in the test set for the 0ν\b{eta}\b{eta} double-electron events signal was 77.4%, the precision was 57.7%, and the training time was 430 min. The discrimination efficiency of LGB model was 73.1%, the precision was 64.0%, and the training time was 1.5 min. This study demonstrated that it is feasible to realize single- and double-electron discrimination on multi-electrode HPGe detectors using an FC neural network and LGB model. These results can be used as a reference for future 76Ge 0ν\b{eta}\b{eta} experiments.

physics.ins-det↗

arXiv2022

Quantum Network Utility: A Framework for Benchmarking Quantum Networks

The absence of a common framework for benchmarking quantum networks is an obstacle to comparing the capabilities of different quantum networks. We propose a general framework for quantifying the performance of a quantum network, which is based on the value created by connecting users through quantum channels. In this framework, we define the quantum network utility metric $U_{QN}$ to capture the social and economic value of quantum networks. While the quantum network utility captures a variety of applications from secure communications to distributed sensing, we study the example of distributed quantum computing in detail. We hope that the adoption of the utility-based framework will serve as a foundation for guiding and assessing the development of new quantum network technologies and designs.

quant-ph↗

arXiv2022

Scheduling Quantum Teleportation with Noisy Memories

Quantum teleportation channels can overcome the effects of photonic loss, a major challenge in the implementation of a quantum network over fiber. Teleportation channels are created by distributing an entangled state between two nodes which is a probabilistic process requiring classical communication. This causes critical delays that can cause information loss as quantum data suffers from decoherence when stored in memory. In this work, we quantify the effect of decoherence on fidelity at a node in a quantum network due to the storage of qubits in noisy memory platforms. We model the memory platform as a buffer that stores incoming qubits waiting for the creation of a teleportation channel. Memory platforms are parameterized with decoherence rate and buffer size, in addition to the order in which the incoming qubits are served. We show that fidelity at a node is a linear sum of terms, exponentially decaying with time, where the decay rate depends on the decoherence rate of the memory platform. This allows us to utilize Laplace Transforms to derive efficiently computable functions of average fidelity with respect to the load, buffer size, and decoherence rate of the memory platform. We prove that serving qubits in a Last In First Out order with pushout for buffer overflow management is optimal in terms of average fidelity. Lastly, we apply this framework to model a single repeater node to calculate the average fidelity of the teleportation channels created by this repeater assuming perfect gate operations.

quant-ph↗

arXiv2021

Entanglement Swapping for Repeater Chains with Finite Memory Sizes

We develop entanglement swapping protocols and memory allocation methods for quantum repeater chains. Unlike most of the existing studies, the memory size of each quantum repeater in this work is a parameter that can be optimized. Based on Markov chain modeling of the entanglement distribution process, we determine the trade-off between the entanglement distribution rate and the memory size for temporal multiplexing techniques. We then propose three memory allocation methods that achieve entanglement distribution rates decaying polynomially with respect to the distance while using constant average memory slots per node. We also quantify the average number of memory slots required due to classical communication delay, as well as the delay of entanglement distribution. Our results show that a moderate memory size suffices to achieve a polynomial decay of entanglement distribution rate with respect to the distance, which is the scaling achieved by the optimal protocol even with infinite memory size at each node.

quant-ph↗

arXiv2021

A quantum walk control plane for distributed quantum computing in quantum networks

Quantum networks are complex systems formed by the interaction among quantum processors through quantum channels. Analogous to classical computer networks, quantum networks allow for the distribution of quantum computation among quantum computers. In this work, we describe a quantum walk protocol to perform distributed quantum computing in a quantum network. The protocol uses a quantum walk as a quantum control signal to perform distributed quantum operations. We consider a generalization of the discrete-time coined quantum walk model that accounts for the interaction between a quantum walker system in the network graph with quantum registers inside the network nodes. The protocol logically captures distributed quantum computing, abstracting hardware implementation and the transmission of quantum information through channels. Control signal transmission is mapped to the propagation of the walker system across the network, while interactions between the control layer and the quantum registers are embedded into the application of coin operators. We demonstrate how to use the quantum walker system to perform a distributed CNOT operation, which shows the universality of the protocol for distributed quantum computing. Furthermore, we apply the protocol to the task of entanglement distribution in a quantum network.

quant-ph↗

arXiv2021

SAGE : A Monte Carlo Simulation Framework for Experiments with Germanium Detectors

A Geant4-based simulation framework for rare event searching experiments with germanium detectors named SAGE is presented with details. It is designed for simulating, assessing background distribution, and investigating the response of the germanium detectors. The SAGE framework incorporates its experiment-specific geometries and custom attributes, including the event generators, physics lists and output format, to satisfy various simulation objectives. Its docker image has been prepared for virtualizing and distributing the SAGE framework. Deployment of a Geant4-based simulation will be convenient under this docker image. The implemented geometries include p-type point contact and broad energy germanium detectors with environmental surroundings, and these hierarchical geometries can be easily extended. Users select these custom attributes via the JSON configuration files. The aforementioned attributes satisfy the simulation demands and make SAGE a generic and powerful simulation framework for the CDEX experiment.

physics.ins-det↗

arXiv2021

A marine radioisotope gamma-ray spectrum analysis method based on Monte Carlo simulation and MLP neural network

The monitoring of Cs-137 in seawater using scintillation detector relies on the spectrum analysis method to extract the Cs-137 concentration. And when in poor statistic situation, the calculation result of the traditional net peak area (NPA) method has a large uncertainty. We present a machine learning based method to better analyze the gamma-ray spectrum with low Cs-137 concentration. We apply multilayer perceptron (MLP) to analyze the 662 keV full energy peak of Cs-137 in the seawater spectrum. And the MLP can be trained with a few measured background spectrums by combining the simulated Cs-137 signal with measured background spectrums. Thus, it can save the time of preparing and measuring the standard samples for generating the training dataset. To validate the MLP-based method, we use Geant4 and background gamma-ray spectrums measured by a seaborne monitoring device to generate an independent test dataset to test the result by our method and the traditional NPA method. We find that the MLP-based method achieves a root mean squared error of 0.159, 2.3 times lower than that of the traditional net peak area method, indicating the MLP-based method improves the precision of Cs-137 concentration calculation

physics.ins-det↗

arXiv2021

Scalable and High-Fidelity Quantum Random Access Memory in Spin-Photon Networks

A quantum random access memory (qRAM) is considered an essential computing unit to enable polynomial speedups in quantum information processing. Proposed implementations include using neutral atoms and superconducting circuits to construct a binary tree, but these systems still require demonstrations of the elementary components. Here, we propose a photonic integrated circuit (PIC) architecture integrated with solid-state memories as a viable platform for constructing a qRAM. We also present an alternative scheme based on quantum teleportation and extend it to the context of quantum networks. Both implementations rely on already demonstrated components: electro-optic modulators, a Mach-Zehnder interferometer (MZI) network, and nanocavities coupled to artificial atoms for spin-based memory writing and retrieval. Our approaches furthermore benefit from built-in error-detection based on photon heralding. Detailed theoretical analysis of the qRAM efficiency and query fidelity shows that our proposal presents viable near-term designs for a general qRAM.

quant-ph↗

arXiv2017

Adaptive recurrence quantum entanglement distillation for two-Kraus-operator channels

Quantum entanglement serves as a valuable resource for many important quantum operations. A pair of entangled qubits can be shared between two agents by first preparing a maximally entangled qubit pair at one agent, and then sending one of the qubits to the other agent through a quantum channel. In this process, the deterioration of entanglement is inevitable since the noise inherent in the channel contaminates the qubit. To address this challenge, various quantum entanglement distillation (QED) algorithms have been developed. Among them, recurrence algorithms have advantages in terms of implementability and robustness. However, the efficiency of recurrence QED algorithms has not been investigated thoroughly in the literature. This paper put forth two recurrence QED algorithms that adapt to the quantum channel to tackle the efficiency issue. The proposed algorithms have guaranteed convergence for quantum channels with two Kraus operators, which include phase-damping and amplitude-damping channels. Analytical results show that the convergence speed of these algorithms is improved from linear to quadratic and one of the algorithms achieves the optimal speed. Numerical results confirm that the proposed algorithms significantly improve the efficiency of QED.

quant-ph↗

arXiv2015

Indoor Localization Algorithm For Smartphones

Increasing sources of sensor measurements and prior knowledge have become available for indoor localization on smartphones. How to effectively utilize these sources for enhancing localization accuracy is an important yet challenging problem. In this paper, we present an area state-aided localization algorithm that exploits various sources of information. Specifically, we introduce the concept of area state to indicate the area where the user is on an indoor map. The position of the user is then estimated using inertial measurement unit (IMU) measurements with the aid of area states. The area states are in turn updated based on the position estimates. To avoid accumulated errors of IMU measurements, our algorithm uses WiFi received signal strength indicator (RSSI) to indicate the vicinity of the user to the routers. The experiment results show that our system can achieve satisfactory localization accuracy in a typical indoor environment.

cs.NI↗

arXiv2013

Power Optimization for Network Localization

Reliable and accurate localization of mobile objects is essential for many applications in wireless networks. In range-based localization, the position of the object can be inferred using the distance measurements from wireless signals exchanged with active objects or reflected by passive ones. Power allocation for ranging signals is important since it affects not only network lifetime and throughput but also localization accuracy. In this paper, we establish a unifying optimization framework for power allocation in both active and passive localization networks. In particular, we first determine the functional properties of the localization accuracy metric, which enable us to transform the power allocation problems into second-order cone programs (SOCPs). We then propose the robust counterparts of the problems in the presence of parameter uncertainty and develop asymptotically optimal and efficient near-optimal SOCP-based algorithms. Our simulation results validate the efficiency and robustness of the proposed algorithms.

cs.IT↗

arXiv2011

The Non-Bayesian Restless Multi-Armed Bandit: A Case of Near-Logarithmic Strict Regret

In the classic Bayesian restless multi-armed bandit (RMAB) problem, there are $N$ arms, with rewards on all arms evolving at each time as Markov chains with known parameters. A player seeks to activate $K \geq 1$ arms at each time in order to maximize the expected total reward obtained over multiple plays. RMAB is a challenging problem that is known to be PSPACE-hard in general. We consider in this work the even harder non-Bayesian RMAB, in which the parameters of the Markov chain are assumed to be unknown \emph{a priori}. We develop an original approach to this problem that is applicable when the corresponding Bayesian problem has the structure that, depending on the known parameter values, the optimal solution is one of a prescribed finite set of policies. In such settings, we propose to learn the optimal policy for the non-Bayesian RMAB by employing a suitable meta-policy which treats each policy from this finite set as an arm in a different non-Bayesian multi-armed bandit problem for which a single-arm selection policy is optimal. We demonstrate this approach by developing a novel sensing policy for opportunistic spectrum access over unknown dynamic channels. We prove that our policy achieves near-logarithmic regret (the difference in expected reward compared to a model-aware genie), which leads to the same average reward that can be achieved by the optimal policy under a known model. This is the first such result in the literature for a non-Bayesian RMAB. For our proof, we also develop a novel generalization of the Chernoff-Hoeffding bound.

math.OC↗

arXiv2011

Efficient Online Learning for Opportunistic Spectrum Access

The problem of opportunistic spectrum access in cognitive radio networks has been recently formulated as a non-Bayesian restless multi-armed bandit problem. In this problem, there are N arms (corresponding to channels) and one player (corresponding to a secondary user). The state of each arm evolves as a finite-state Markov chain with unknown parameters. At each time slot, the player can select K < N arms to play and receives state-dependent rewards (corresponding to the throughput obtained given the activity of primary users). The objective is to maximize the expected total rewards (i.e., total throughput) obtained over multiple plays. The performance of an algorithm for such a multi-armed bandit problem is measured in terms of regret, defined as the difference in expected reward compared to a model-aware genie who always plays the best K arms. In this paper, we propose a new continuous exploration and exploitation (CEE) algorithm for this problem. When no information is available about the dynamics of the arms, CEE is the first algorithm to guarantee near-logarithmic regret uniformly over time. When some bounds corresponding to the stationary state distributions and the state-dependent rewards are known, we show that CEE can be easily modified to achieve logarithmic regret over time. In contrast, prior algorithms require additional information concerning bounds on the second eigenvalues of the transition matrices in order to guarantee logarithmic regret. Finally, we show through numerical simulations that CEE is more efficient than prior algorithms.

cs.LG↗