Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2112.11165

Scalable High-Rate Twin-Field Quantum Key Distribution Networks without Constraint of Probability and Intensity

Yuan-Mei Xie·Chen-Xun Weng·Yu-Shuo Lu·Yao Fu·Yang Wang·Hua-Lei Yin·Zeng-Bing Chen
View original source ↗Read source document ↗Export metadata ↓

Abstract

Implementation of a twin-field quantum key distribution network faces limitations, including the low tolerance of interference errors for phase-matching type protocols and the strict constraint regarding intensity and probability for sending-or-not-sending type protocols. Here, we propose a two-photon twin-field quantum key distribution protocol and achieve twin-field-type two-photon interference through post-matching phase-correlated single-photon interference events. We exploit the non-interference mode as the code mode to highly tolerate interference errors, and the two-photon interference naturally removes the intensity and probability constraint. Therefore, our protocol can transcend the abovementioned limitations while breaking the secret key capacity of repeaterless quantum key distribution. Simulations show that for a four-user networks, under which each node with fixed system parameters can dynamically switch different attenuation links, the key rates of our protocol for all six links can either exceed or approach the secret key capacity. However, the key rates of all links are lower than the key capacity when using phase-matching type protocols. Additionally, four of the links could not extract the key when using sending-or-not-sending type protocols. We anticipate that our protocol can facilitate the development of practical and efficient quantum networks.

Explore related subjects

quant-phcs.CRcs.NIphysics.optics

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Yuan-Mei Xie, Chen-Xun Weng, Yu-Shuo Lu, Yao Fu, Yang Wang, Hua-Lei Yin, Zeng-Bing Chen. 2023-04-10. Scalable High-Rate Twin-Field Quantum Key Distribution Networks without Constraint of Probability and Intensity. https://doi.org/10.1103/physreva.107.042603

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Probing the Error-Mitigation Threshold with Matrix Product States

Quantum error mitigation relies on accurate noise characterization, but mismatches between the actual and characterized noise can be amplified and drive a sharp threshold between successful and failed mitigation. In random circuits, this threshold maps onto a random-field Ising transition, but previous exact numerics were limited to small one-dimensional and all-to-all systems, leaving explicit two-dimensional architectures unresolved. We develop a fixed-bond-dimension matrix-product-state method for the replicated transfer dynamics that extends threshold calculations beyond exact propagation while retaining the finite-size signatures of the transition. At system sizes beyond previous exact studies, we recover the predicted absence of a threshold for quenched disorder in 1D, obtain a sharper annealed all-to-all critical point, and resolve architecture-dependent finite-depth thresholds in 2D square and heavy-hex circuits. These results establish replicated tensor-network dynamics as a practical tool for probing error-mitigation thresholds in large and higher-dimensional noisy circuits.

quant-ph↗
arXiv2026

Low-cost algorithm-to-execution framework for surface-code quantum computing

The execution of useful quantum algorithms on fault-tolerant processors requires more than a mapping from logical gates to encoded operations: the spatial organization, non-Clifford resource supply, and execution schedule must also be determined while keeping physical overhead within practical limits. Although the theoretical hierarchy from logical circuits to fault-tolerant operations is well established, these implementation choices are often specified and optimized separately. Here we develop a low-cost algorithm-to-execution framework for surface-code quantum computing. From hierarchical algorithm descriptions, it constructs dependency-preserving logical schedules and an executable workload capturing logical interactions, operation parallelism, and time-resolved non-Clifford demand, thereby linking logical computation to surface-code organization, resource-state preparation, and fault-tolerant execution in a traceable workflow. We apply the framework to twenty benchmark circuits across seven algorithm families and a hierarchically composed application-scale elliptic-curve discrete-logarithm workload. Physical costs vary substantially even for circuits with similar logical resource counts. Under our direct-rotation calibration, non-Clifford implementation selection reduces space-time volume by up to 241.5 times versus an all-synthesis baseline for the QAOA amplitude-amplification workload. Circuit-specific surface-code layouts reduce routed-latency estimates for all twenty benchmarks; thirteen also reduce space-time volume because communication savings outweigh added spatial overhead. These results show that low-cost fault-tolerant execution depends on computation scheduling and organization, not aggregate logical resource counts alone.

quant-ph↗
arXiv2026

Sample-optimal learning of stabilizer states

It is well-known that learning a pure $n$-qubit stabilizer state $|\psi\rangle$ both requires, and can be accomplished with, access to a number of copies of $|\psi\rangle$ linear in $n$. However, the precise constant coefficient of this scaling does not appear to have been determined. Here we prove that $L_\delta(n)$, the smallest number of copies from which a quantum procedure can identify any stabilizer state with failure probability at most $0<\delta<1/8$, satisfies $n+\lceil\log_2(1/\delta)\rceil-3\leq L_\delta(n)\leq n+\left\lceil\log_2(1/\delta)\right\rceil+4$. We present a polynomial-time quantum learning algorithm that saturates this bound, achieving a constant factor improvement in sample-complexity over previously known approaches. As an immediate corollary, we obtain via the Choi-Jamiolkowski isomorphism an algorithm for learning an unknown $n$-qubit Clifford unitary from $2n+\left\lceil\log_2(1/\delta)\right\rceil+4$ queries, the $n$-dependence of which we show to be optimal. Our proof technique, which involves Fourier analysis on the abelian group $\mathbb{Z}_4^n \times \mathbb{F}_2^{n(n-1)/2}$, seems to be qualitatively different to previous approaches to stabilizer state learning, and may be of some independent interest; in particular, it admits natural generalisations to further problems in quantum learning theory.

quant-ph↗