EPJ Quantum Technology最新文献

Privacy Amplification (PA) is indispensable in Quantum Key Distribution (QKD), ensuring security against eavesdropping by eliminating information leakage. For Discrete-Variable QKD (DV-QKD) protocols, a large input block size exceeding 10⁸ bits is preferred to achieve the secure key rate approaching the asymptotic limit. However, in state-of-the-art quantum key distribution systems operating at multi-GHz pulse rates, PA becomes a critical bottleneck due to the conflicting requirements of large input block sizes and high throughput. To address this challenge, we propose a novel PA algorithm utilizing a newly constructed universal hash family DM3H and prove its cryptographic security rigorously. Based on the PA algorithm, we design and implement an efficient PA scheme which is capable of processing input block sizes up to 10¹⁰ bits while achieving high throughput performance. For an input block size of 10¹⁰ bits, the implementation on the i9-14900 platform demonstrates a throughput of 112 Mbps with a retention ratio of 0.33. This breakthrough significantly enhances the secure key rate and maximum transmission distance of DV-QKD systems.

The development of quantum technologies has been labelled the next revolution in human scientific and industrial endeavour. Because quantum technologies have potential military, defence, intelligence and law enforcement applications, there has been a great deal written about quantum as a dual-use technology; however, most of the research on quantum technologies is performed in higher education environments that lack robust security cultures. This theoretical paper generates a basic overview of the impact that quantum technologies are having, and could have, on how technologies are secured in university and higher education settings (“research security”). This paper then analyses the implications of quantum technology from the perspective of research security, arguing that a new paradigm is needed that moves beyond the dual-use binary. Specific applications of quantum technology are used as examples of challenges to the definitions and explanations of dual-use, and several alternatives are proposed and summarised.

We present the design of laser systems for the Bose-Einstein Condensate and Cold Atom Laboratory (BECCAL) payload, enabling numerous quantum technological experiments onboard the International Space Station (ISS), in particular dual species ⁸⁷Rb and ⁴¹K Bose-Einstein condensates. A flight model (FM) and a commercial off the shelf (COTS) based model are shown, both of which meet the BECCAL requirements in terms of functionality, but have differing size, weight and power (SWaP) and environmental requirements. The capabilities of both models are discussed and characteristics compared.

The flight model of BECCAL uses specifically developed and qualified custom components to create a compact and robust system suitable for long-term remote operation onboard the ISS. This system is based on ECDL-MOPA lasers and free-space optical benches made of Zerodur, as well as commercial fibre components. The COTS-based system utilizes entirely commercial parts to create a functionally equivalent system for operation in a standard laboratory, without the strict SWaP and environmental constraints of the flight model.

Large-scale operations in quantum networks require efficient sorting of desired paths between nodes. In this article, we consider entanglement routing, which involves establishing an entanglement link between specific nodes in a large network of bosonic nodes. The networks are continuous-variable graph states built from finite squeezing and passive linear optics, shaped by complex network structures that mimic real-world networks. We construct a bipartite routing protocol with the specific goal of establishing a teleportation channel between two clients via passive optics operations locally operated by two different providers sharing the network. We provide criteria for extracting the aforementioned channel and, through the use of a derandomised evolutionary algorithm, extend the existing framework to study complex graph topologies.

Integer factorization, a fundamental problem in computational mathematics, holds critical significance for modern cryptography, particularly in RSA encryption. Traditional approaches such as the number field sieve face exponential complexity limitations, while Shor’s quantum algorithm remains impractical due to hardware constraints. This study proposes a universal algorithm for integer factorization based on Ising machines by transforming the problem into a Quadratic Unconstrained Binary Optimization (QUBO) formulation. The algorithm introduces an optimal reduction formula to optimize the parameter ranges of local field coefficients (h) and coupling coefficients (J) in the Ising model. Additionally, a non-uniform column grouping method is employed to resolve the conflict between coefficient ranges and carry auxiliary quantum bits(qubits), minimizing the number of auxiliary qubits with minimal compromise on coefficient ranges. Using this approach, we successfully factorized the 22-bit integer 2,093,809 with only 118 qubits. Extrapolating to existing photonic Ising machines with 100,000 qubits, our method demonstrates the potential to factorize 631-bit integers, highlighting its promise for efficient large-scale integer factorization. All results presented in this paper are obtained from simulations on Fixstars Amplify and D-Wave simulators.

Since blockchain platforms still depend on classical cryptographic protocols, they become more and more vulnerable to the rapid advancement of quantum computing. However, the emergence of quantum attacks has placed the need to develop Quantum Key Distribution (QKD) protocols that can preserve security while eliminating the limitations of quantum information systems, such as noise and error mitigation. To address these needs, this study proposes a novel Hybrid Rainbow-Kyber QKD (HRK-QKD) Protocol, which uses the strength of multivariate quadratic equations in Rainbow to mask the classical keys and the efficiency of lattice-based encryption in Kyber for key encryption. An entanglement-assisted dynamic key synthesis protocol that combines matrix-based quantum noise filtering, lattice-based multi-dimensional transformations and adaptive private key rotations is utilized. The proposed methods provide real-time mitigation of quantum noise and minimal performance overhead for key generation. HRK-QKD achieves the highest scalability ratio ((Sc=2.7)), the best noise resilience (0.90-0.99), and the highest quantum security measure ((Q_{S}=0.064881)) with minimal information leakage probability (0.00001). This advancement also means blockchain remains a resilient technology against quantum threats, with an economical, scalable, and high-accuracy solution for next-generation secure communication systems.

Object tracking with radar has become a key part of many IoT applications, such as smart transportation, autonomous robotics, and ambient surveillance. Nevertheless, conventional machine learning techniques have fatal problems, such as privacy, noisy radar signals, latency, and generalization in the distributed IoT systems. This paper proposes a new model, Quantum-Assisted Federated Learning (QAFL), a combination of quantum machine learning (QML) and federated learning (FL) to solve these problems, which can effectively and privately identify radar objects and their routes. The suggested QAFL architecture has a new Hybrid Angle-Amplitude Encoding (HAAE) scheme with multi-layer Variational Quantum Circuits (VQCs) to support the effective extraction of features in the presence of noisy and non-homogeneous radar sensor data. We also present a Quantum-Enhanced Federated Averaging (Quantum-FedAvg) algorithm that can be used to improve the efficiency of the training, privacy, and scalability of distributed IoT nodes. Comprehensive experimental tests based on the CARRADA automotive radar dataset show that QAFL achieves large performance improvements compared to classical federated learning baselines, in terms of classification accuracy improvements of up to 7.2 percentage points with the low signal-to-noise ratio (SNR) regime, trajectory prediction error reduction of up to 23 percent, as well as considerable communication overhead and training latency reductions. Such results highlight the enormous potential of quantum-enhanced federated learning systems to radar-based internet-of-things tracking systems.

Belief propagation (BP) combined with ordered statistics decoding (OSD) provides a good balance between accuracy and complexity for many quantum error correction (QEC) codes, making it nearly universal. However, the complexity of OSD can limit real-time decoding, particularly for superconducting qubits, and the limits of classical hardware decoders have not been fully explored. Therefore, it is important to assess the architecture of OSD for different code families, such as surface codes and bicycle bivariate codes, under realistic assumptions like the detector error model. This paper introduces a BP + OSD parallel architecture implemented on FPGA and ASIC for surface codes (distances 3–21) and bicycle bivariate codes (distances 6–24). Results show that for surface codes up to distance 9, the OSD post-processor fits into a single VCU129 FPGA, achieving a frequency of 200 MHz with a worst-case latency of 134 μs. For bicycle bivariate codes, the limit is distance 12, with a frequency of 244 MHz and a worst-case latency of 84 μs. In ASIC, with 45 nm technology, latency improves by 31%, but area resources grow significantly, making parallel implementation beyond distance 12 impractical on a single chip. The designs were verified using a hardware emulator, ensuring that the decoder’s behavior matches software simulations and revealing interesting results like potential error floors at low logical error rates.

We investigate lattice scalar field theory in two-dimensional Euclidean space via a quantum annealer. To accommodate the quartic interaction terms, we introduce three schemes for rewriting them as quadratic polynomials through the use of auxiliary qubits. These methods are applied on D-Wave quantum annealer, and their effectiveness is assessed by examining the annealer-generated distributions. Using these distributions, we perform Monte Carlo sampling via the Metropolis-Hastings algorithm and compare the outcomes with those from classical local Metropolis simulations.