arXiv subjects

Jiapeng Zhao

Publications and source records attributed to Jiapeng Zhao.

At least 19 recordsLinked to original sources

arXiv2026

Impact of Network Constraints on Fault-Tolerant Distributed Quantum Computing

As we move towards scalable and modular quantum computing, quantum data centres become imperative. Existing analyses typically treat network constraints in isolation or through simplified models, leaving the interplay between error correction operations and communication resources underexplored. In this work, we present an end-to-end simulation framework that jointly models surface-code operations, internal QPU connectivity, and realistic network constraints including finite entanglement generation rates, limited communication qubits, and bandwidth contention, producing execution latency, from which logical error rate estimates are obtained. The framework is modular by design, allowing individual components such as routing heuristics, scheduling policies, and network topologies to be independently replaced. Numerical evaluation reveals distinct operating regimes in which the optimal resource allocation and code distance selection shift depending on the network characteristics. These results point to tradeoffs in the design of distributed quantum computing architectures that are not visible when computation and communication are modeled separately.

quant-ph↗

arXiv2026

ATHENA: A Compiler For Optimized Scheduling In Distributed Quantum Computers

Distributed Quantum Computers (DQCs) enable large system sizes by connecting smaller chips via photonic interconnects. DQCs use teleportation to relocate qubits and execute CNOTs between qubits on different chips. However, non-local CNOTs are 4.3-7.7$\times$ slower and 4$\times$ more error-prone than local CNOTs within a chip, which degrades program fidelities. Existing compilers group CNOTs with overlapping qubits into blocks and collectively optimize teleportations for each block. However, block-level scheduling has two key drawbacks. First, it lacks lookahead ability across blocks because it selects the optimal schedule for one block before proceeding to the next. As a result, it cannot assess the impact of a teleportation on future blocks. Our studies show that naively expanding the lookahead window to include subsequent blocks does not address this issue. Second, existing approaches do not schedule future block operations or the teleportations they require until preceding blocks are fully scheduled, introducing delay and latency overheads. We propose ATHENA, a DQC compiler that addresses these limitations using two key insights: Utility-driven Lookahead with Multi-Candidate Block Scheduling (UMS) and EPR-Capacity-Aware Early Scheduling (EES). UMS schedules a block by considering only useful future blocks in its lookahead window. A future block has utility if it shares overlapping qubits with the current block being scheduled. UMS also maintains multiple schedules during compilation, allowing it to defer commitment to globally sub-optimal schedules early in the compilation process. EES enables ATHENA to schedule future operations and their relocations early when EPR resources are available. Our evaluations show that ATHENA reduces teleportations by 34% on average and up to 65%, and reduces latency by 2$\times$ on average and up to 2.9$\times$ compared to the state-of-the-art.

quant-ph↗

arXiv2026

A Universal Quantum Information Preserving Photonic Switch for Scalable Quantum Networks

Quantum networks are a keystone of the quantum internet. However, existing implementations remain largely confined to static point-to-point links due to the absence of a switching paradigm capable of dynamically routing fragile quantum entanglement without introducing decoherence. Here, we propose the Universal Quantum Switch, a foundational building block allowing on-demand, non-blocking, and encoding-agnostic routing of quantum information, as well as seamless modality conversion between disparate quantum platforms. We develop a prototype in thin-film lithium niobate and experimentally demonstrate robust switching with $\le 4\%$ decoherence via thermo-optic modulation and high-speed electro-optic switching of arbitrary entangled states at 1 MHz. Moreover, we show that our platform can support reconfiguration speeds up to 1 GHz. To our knowledge, this work represents the first demonstration of multi-node dynamic entanglement distribution at these speeds. Complementing these experimental results, we project the architecture's scalability, showing dimension-independent decoherence, and provide a scalable, interoperable building block for heterogeneous quantum network fabrics.

quant-ph↗

arXiv2026

High-rate Scalable Entanglement Swapping Between Remote Entanglement Sources on Deployed New York City Fibers

Entanglement swapping between photon pairs generated at physically separated nodes over telecommunication fiber infrastructure is an essential step towards the quantum internet, enabling applications such as quantum repeaters, blind quantum computing, distributed quantum computing, and distributed quantum sensing. However, successful networked entanglement swapping relies on generating indistinguishable pairs of photons and preserving them over deployed fibers. This has limited most previous demonstrations to laboratory settings or relied on sophisticated methods to maintain the necessary indistinguishability. Here, we demonstrate a scalable entanglement swapping experiment using naturally indistinguishable entanglement sources based on warm atomic vapor cells. Without sharing lasers or optical frequency references between nodes, nor the need for pulsing the sources, we achieve a swapping rate of nearly 500 pairs/s while maintaining the CHSH parameter above 2. Additionally, we demonstrate the scalability of our method by maintaining the quality of the entanglement swapping on 17.6-km of deployed fibers in NYC, relying on commercially available SPADs at the spoke nodes, SNSPDs at the hub and standard time-synchronization techniques. Our work paves the way for the practical deployment of large-scale hub-and-spoke quantum networks within cities and data centers.

quant-ph↗

arXiv2025

Scalable low-latency entanglement distribution for distributed quantum computing

Practical distributed quantum computing and error correction require quantum networks with high-qubit-rate, high-fidelity, and low-reconfiguration-latency. Unfortunately, current approaches are limited by fundamental constraints: single-channel entanglement rates remain at the MHz level with millisecond-level reconfiguration, which is insufficient for fault-tolerant distributed quantum computing. Here, we propose a quantum network architecture that leverages reconfigurable quantum interfaces and wavelength-selective switches to overcome bandwidth and latency constraints. By tuning the frequency and temporal modes of photonic qubits across dense wavelength division multiplexing (DWDM) channels, our protocol achieves an entanglement generation rate of up to 183.4 MHz based on our comprehensive modeling of the networked cold atom computing systems. Our architecture enables nanosecond-scale network reconfiguration with low loss, low infidelity, and high dimensionality. Our modeling and simulation are designed for deployable distributed quantum computing and error correction, integrating the quantum interface, network switching, circuit compilation, and execution into a unified framework. The proposed architecture is fully compatible with industry-standard DWDM infrastructure, providing a scalable and cost-effective foundation for distributed quantum computing.

quant-ph↗

arXiv2025

4D Virtual Imaging Platform for Dynamic Joint Assessment via Uni-Plane X-ray and 2D-3D Registration

Conventional computed tomography (CT) lacks the ability to capture dynamic, weight-bearing joint motion. Functional evaluation, particularly after surgical intervention, requires four-dimensional (4D) imaging, but current methods are limited by excessive radiation exposure or incomplete spatial information from 2D techniques. We propose an integrated 4D joint analysis platform that combines: (1) a dual robotic arm cone-beam CT (CBCT) system with a programmable, gantry-free trajectory optimized for upright scanning; (2) a hybrid imaging pipeline that fuses static 3D CBCT with dynamic 2D X-rays using deep learning-based preprocessing, 3D-2D projection, and iterative optimization; and (3) a clinically validated framework for quantitative kinematic assessment. In simulation studies, the method achieved sub-voxel accuracy (0.235 mm) with a 99.18 percent success rate, outperforming conventional and state-of-the-art registration approaches. Clinical evaluation further demonstrated accurate quantification of tibial plateau motion and medial-lateral variance in post-total knee arthroplasty (TKA) patients. This 4D CBCT platform enables fast, accurate, and low-dose dynamic joint imaging, offering new opportunities for biomechanical research, precision diagnostics, and personalized orthopedic care.

cs.CV↗

arXiv2025

Adaptive Job Scheduling in Quantum Clouds Using Reinforcement Learning

Present-day quantum systems face critical bottlenecks, including limited qubit counts, brief coherence intervals, and high susceptibility to errors-all of which obstruct the execution of large and complex circuits. The advancement of quantum algorithms has outpaced the capabilities of existing quantum hardware, making it difficult to scale computations effectively. Additionally, inconsistencies in hardware performance and pervasive quantum noise undermine system stability and computational accuracy. To optimize quantum workloads under these constraints, strategic approaches to task scheduling and resource coordination are essential. These methods must aim to accelerate processing, retain operational fidelity, and reduce the communication burden inherent to distributed setups. One of the persistent challenges in this domain is how to efficiently divide and execute large circuits across multiple quantum processors (QPUs), especially in error-prone environments. In response, we introduce a simulation-based tool that supports distributed scheduling and concurrent execution of quantum jobs on networked QPUs connected via real-time classical channels. The tool models circuit decomposition for workloads that surpass individual QPU limits, allowing for parallel execution through inter-processor communication. Using this simulation environment, we compare four distinct scheduling techniques-among them, a model informed by reinforcement learning. These strategies are evaluated across multiple metrics, including runtime efficiency, fidelity preservation, and communication costs. Our analysis underscores the trade-offs inherent in each approach and highlights how parallelized, noise-aware scheduling can meaningfully improve computational throughput in distributed quantum infrastructures.

cs.DC↗

arXiv2025

40Gbps Tri-type Quantum Random Number Generator

Traditional quantum random number generators can produce only one type of random number, while the optimal distribution of random numbers for different applications is usually distinct. The typical solution to this challenge is either using different quantum phenomena for different types of random number, or converting one distribution of random numbers to another type. However, the former solution requires multiple hardware systems, while the latter one sacrifices a lot of secure bits. Here, we develop a quantum random number generator that can on-demand produce three distribution types of random numbers at over 60 Gbits/s (Gbps) raw bits by measuring the quantum vacuum noise. After randomness extraction, over 42 Gbps secure bit rate is demonstrated for uniform random numbers, and over 14 Gbps secure bit rate for Gaussian random number. Due to the lack of Rayleigh randomness extraction, only denoised Rayleigh raw bits are generated. Switching between different types of random numbers is achieved in electronics, which does not affect the generation rate. The random numbers pass NIST and Dieharder tests, and are available for various applications, which can be continuously accessed via Cisco Quantum Random Number web service.

quant-ph↗

arXiv2025

Optimized Quantum Circuit Partitioning Across Multiple Quantum Processors

This paper addresses the challenge of scaling quantum computing by employing distributed quantum algorithms across multiple processors. We propose a novel circuit partitioning method that leverages graph partitioning to optimize both qubit and gate teleportation, minimizing the required Einstein-Podolsky-Rosen (EPR) pairs for executing general quantum circuits. Additionally, we formulate an integer linear program to further reduce entanglement requirements by mapping the logical resources of partitioned circuits to the physical constraints of the quantum network. Finally, we analyze the entanglement cost of implementing the Quantum Fourier Transform (QFT) across multiple QPUs, exploiting the circuit's structure to minimize total entanglement consumption.

quant-ph↗

arXiv2025

Quantum Data Center Infrastructures: A Scalable Architectural Design Perspective

This paper presents the design of scalable quantum networks that utilize optical switches to interconnect multiple quantum processors, facilitating large-scale quantum computing. By leveraging these novel architectures, we aim to address the limitations of current quantum processors and explore the potential of quantum data centers. We provide an in-depth analysis of these architectures through the development of simulation tools and performance metrics, offering a detailed comparison of their advantages and trade-offs. We hope this work serves as a foundation for the development of efficient and resilient quantum networks, designed to meet the evolving demands of future quantum computing applications.

quant-ph↗

arXiv2024

Optimizing Quantum Communication for Quantum Data Centers with Reconfigurable Networks

Distributed Quantum Computing (DQC) enables scalability by interconnecting multiple QPUs. Among various DQC implementations, quantum data centers (QDCs), which utilize reconfigurable optical switch networks to link QPUs across different racks, are becoming feasible in the near term. However, the latency of cross-rack communications and dynamic reconfigurations poses unique challenges to quantum communication, significantly increasing the overall latency and exacerbating qubit decoherence. In this paper, we introduce a new optimization space to parallelize cross-rack communications and avoid frequent reconfigurations, which incurs additional in-rack communications that can be further minimized. Based on this, we propose a flexible scheduler that improves communication efficiency while preventing deadlocks and congestion caused by the flexibility. Through a comprehensive evaluation, we show that our approach reduces the overall latency by a factor of 8.02, thereby mitigating qubit decoherence, with a small overhead.

quant-ph↗

arXiv2023

URM4DMU: an user represention model for darknet markets users

Darknet markets provide a large platform for trading illicit goods and services due to their anonymity. Learning an invariant representation of each user based on their posts on different markets makes it easy to aggregate user information across different platforms, which helps identify anonymous users. Traditional user representation methods mainly rely on modeling the text information of posts and cannot capture the temporal content and the forum interaction of posts. While recent works mainly use CNN to model the text information of posts, failing to effectively model posts whose length changes frequently in an episode. To address the above problems, we propose a model named URM4DMU(User Representation Model for Darknet Markets Users) which mainly improves the post representation by augmenting convolutional operators and self-attention with an adaptive gate mechanism. It performs much better when combined with the temporal content and the forum interaction of posts. We demonstrate the effectiveness of URM4DMU on four darknet markets. The average improvements on MRR value and Recall@10 are 22.5% and 25.5% over the state-of-the-art method respectively.

cs.LG↗

arXiv2021

Direct tomography of high-dimensional density matrices for general quantum states of photons

Quantum state tomography is the conventional method used to characterize density matrices for general quantum states. However, the data acquisition time generally scales linearly with the dimension of the Hilbert space, hindering the possibility of dynamic monitoring of a high-dimensional quantum system. Here, we demonstrate a direct tomography protocol to measure density matrices of photons in the position basis through the use of a polarization-resolving camera, where the dimension of density matrices can be as large as 580$\times$580 in our experiment. The use of the polarization-resolving camera enables parallel measurements in the position and polarization basis and as a result, the data acquisition time of our protocol does not increase with the dimension of the Hilbert space and is solely determined by the camera exposure time (on the order of 10 ms). Our method is potentially useful for the real-time monitoring of the dynamics of quantum states and paves the way for the development of high-dimensional, time-efficient quantum metrology techniques.

quant-ph↗

arXiv2021

Ultrafast pulse measurement via time-domain single-pixel imaging

In contrast with imaging using position-resolving cameras, single-pixel imaging uses a bucket detector along with spatially structured illumination for image recovery. This emerging imaging technique is a promising candidate for a broad range of applications due to high signal-to-noise ratio (SNR) and sensitivity, and applicability in a wide range of frequency bands. Here, inspired by single-pixel imaging in the spatial domain, we demonstrate a temporal single-pixel imaging (TSPI) system that covers frequency bands including both terahertz (THz) and near-infrared (NIR) region. By implementing a programmable temporal fan-out (TFO) gate based on a digital micromirror device (DMD), we can deterministically prepare temporally structured pulses with a temporal sampling size down to 16.00$\pm$0.01 fs. By inheriting the advantages in detection efficiency and sensitivity from spatial single-pixel imaging, TSPI enables the compressive recovery of a 5 fJ THz pulse and two NIR pulses with over 97$\%$ fidelity. We demonstrate that the TSPI is robust against temporal distortions in the probe pulse train as well. As a direct application, we apply TSPI to machine-learning-aided THz spectroscopy and demonstrate a high sample identification accuracy (97.5$\%$) even under low SNRs (SNR $\sim$ 10).

physics.optics↗

arXiv2021

High-fidelity spatial mode transmission through a 1-km-long multimode fiber via vectorial time reversal

The large number of spatial modes supported by standard multimode fibers is a promising platform for boosting the channel capacity of quantum and classical communications by orders of magnitude. However, the practical use of long multimode fibers is severely hampered by modal crosstalk and polarization mixing. To overcome these challenges, we develop and experimentally demonstrate a vectorial time reversal technique, which is accomplished by digitally pre-shaping the wavefront and polarization of the forward-propagating signal beam to be the phase conjugate of an auxiliary, backward-propagating probe beam. Here, we report an average modal fidelity above 80% for 210 Laguerre-Gauss and Hermite-Gauss modes by using vectorial time reversal over an unstabilized 1-km-long fiber. We also propose a practical and scalable spatial-mode-multiplexed quantum communication protocol over long multimode fibers to illustrate potential applications that can be enabled by our technique.

physics.optics↗

arXiv2021

Multiprobe time reversal for high-fidelity vortex-mode-division multiplexing over a turbulent free-space link

The orbital angular momentum (OAM) of photons presents a degree of freedom for enhancing the secure key rate of free-space quantum key distribution (QKD) through mode-division multiplexing (MDM). However, atmospheric turbulence can lead to substantial modal crosstalk, which is a long-standing challenge to MDM for free-space QKD. Here, we show that the digital generation of time-reversed wavefronts for multiple probe beams is an effective method for mitigating atmospheric turbulence. We experimentally characterize seven OAM modes after propagation through a 340-m outdoor free-space link and observe an average modal crosstalk as low as 13.2% by implementing real-time time reversal. The crosstalk can be further reduced to 3.4% when adopting a mode spacing $Δ\ell$ of 2. We implement a classical MDM system as a proof-of-principle demonstration, and the bit error rate is reduced from $3.6\times 10^{-3}$ to be less than $1.3\times 10^{-7}$ through the use of time reversal. We also propose a practical and scalable scheme for high-speed, mode-multiplexed QKD through a turbulent link. The modal crosstalk can be further reduced by using faster equipment. Our method can be useful to various free-space applications that require crosstalk suppression.

physics.optics↗

arXiv2020

Performance of real-time adaptive optics compensation in a turbulent channel with high-dimensional spatial-mode encoding

The orbital angular momentum (OAM) of photons is a promising degree of freedom for high-dimensional quantum key distribution (QKD). However, effectively mitigating the adverse effects of atmospheric turbulence is a persistent challenge in OAM QKD systems operating over free-space communication channels. In contrast to previous works focusing on correcting static simulated turbulence, we investigate the performance of OAM QKD in real atmospheric turbulence with real-time adaptive optics (AO) correction. We show that, even our AO system provides a limited correction, it is possible to mitigate the errors induced by weak turbulence and establish a secure channel. The crosstalk induced by turbulence and the performance of AO systems is investigated in two configurations: a lab-scale link with controllable turbulence, and a 340 m long cross-campus link with dynamic atmospheric turbulence. Our experimental results suggest that an advanced AO system with fine beam tracking, reliable beam stabilization, precise wavefront sensing, and accurate wavefront correction is necessary to adequately correct turbulence-induced error. We also propose and demonstrate different solutions to improve the performance of OAM QKD with turbulence, which could enable the possibility of OAM encoding in strong turbulence.

eess.SP↗

arXiv2019

Using all transverse degrees of freedom in quantum communications based on a generic mode sorter

The dimension of the state space for information encoding offered by the transverse structure of light is usually limited by the finite size of apertures. The widely used orbital angular momentum (OAM) number of Laguerre-Gaussian (LG) modes in free-space communications cannot achieve the theoretical maximum transmission capacity unless the radial degree of freedom is multiplexed into the protocol. While the methodology to sort the radial quantum number has been developed, the application of radial modes in quantum communications requires an additional ability to efficiently measure the superposition of LG modes in the mutually unbiased basis. Here we develop and implement a generic mode sorter that is capable of sorting the superposition of LG modes through the use of a mode converter. As a consequence, we demonstrate an 8-dimensional quantum key distribution experiment involving all three transverse degrees of freedom: spin, azimuthal, and radial quantum numbers of photons. Our protocol presents an important step towards the goal of reaching the capacity limit of a free-space link and can be useful to other applications that involve spatial modes of photons.

physics.optics↗