量子科技

Digital certificate offers an efficient way to realize the access authentication which safeguards the first line of secure communication. In this paper, we propose an access authentication scheme with digital certificates based on post-quantum standards published by the National Institute of Standards and Technology for quantum communication networks. Specifically, we experimentally demonstrate the secure access authentication of the quantum channel by verifying the signatures encoded in quantum states in a quantum secure direct communication network. Only the post-quantum digital signature algorithm is needed in the experiment. Together with secure relays, we envision the establishment of a large-scale quantum-secure communication network in the future.

Device-to-device (D2D) communication is expected to become a key component of 6G and IoT systems, enabling low-latency and infrastructure-independent connectivity. A major challenge is to establish secure session keys between previously unknown devices without relying on an online trusted third party, while also ensuring resilience against future quantum adversaries. This paper proposes a lightweight hybrid authentication and key agreement protocol for decentralized D2D communication. The approach combines IPFS-assisted distributed key discovery with a two-message protocol that uses post-quantum key encapsulation for long-term confidentiality, while retaining elliptic curve cryptography (ECC) for efficient real-time authentication under classical security assumptions.This design reflects the different temporal security requirements of confidentiality and authentication and provides a practical trade-off between quantum resilience and computational efficiency. The proposed scheme achieves mutual authentication under classical ECC assumptions, secure session key establishment, and resistance against common attacks, while providing post-quantum confidentiality protection against future quantum adversaries and removing the need for an online trusted third party (TTP) during protocol execution. The results demonstrate that the protocol offers a competitive and practical solution for secure decentralized D2D communication in IoT and future 6G environments.

Federated learning enables decentralized, privacy-preserving training but remains vulnerable to privacy leakage in the quantum era. Quantum federated learning (QFL) offers a promising path toward enhanced security and efficiency. However, a practical and experimentally validated QFL protocol utilizing near-term quantum techniques to address data privacy has been lacking. Here, we present QuNetQFL, a QFL protocol implemented on quantum networks, in which local model updates are masked with distributed quantum secret keys, offering information-theoretic security during aggregation. We experimentally validate the protocol on a 4-client quantum network and benchmark its performance using the generated keys on quantum and real-world datasets. Adding a single quantum client substantially improves global accuracy for classifying multipartite entangled and nonstabilizer quantum datasets. For language tasks, we apply QuNetQFL to sentiment analysis by federated fine-tuning of a hybrid classical-quantum language model, achieving comparable and robust performance in simulation and on real quantum hardware. Large-scale simulations further demonstrate scalability to 200 clients for handwritten-digit recognition, with rapid convergence and a 75% reduction in communication cost via model compression. Our work establishes a practical and scalable route to quantum-secure federated learning for the emerging quantum internet.

Quantum cloning machines are essential in quantum information processing, finding applications in areas such as quantum communication and cryptographic protocols. However, the fidelity of universal quantum cloning machines diminishes as the dimension of the Hilbert space increases, resulting in significantly lower efficiency when cloning high-dimensional quantum states compared to qubits. In this study, we introduce a Hybrid Quantum Autocloning Machine (HQAM) that combines quantum autoencoding with universal quantum cloning. The core concept involves compressing a high-dimensional quantum state into a lower-dimensional effective subspace through a quantum autoencoder, conducting the cloning process within this reduced subspace, and then reconstructing the state in the original Hilbert space. Our results show that, for input states with a strong overlap with the effective qubit subspace, the HQAM achieves cloning fidelities exceeding the benchmark fidelity of direct qutrit universal cloning and approaching the optimal qubit cloning limit, while maintaining robustness under noise. These findings demonstrate that compression-assisted cloning provides a practical strategy for improving cloning performance in high-dimensional quantum systems and may enable more efficient quantum information processing protocols.

Recent advances in quantum computing offer opportunities to explore alternative methods for the solution of classification problems commonly found in biomedical research. This study investigates the feasibility of using quantum kernel-based Support Vector Machines (QSVMs) to classify Autism Spectrum Disorder (ASD) using metabolomic measurements on real quantum hardware. This work evaluates the capabilities of current quantum computers for biomedical classification and establishes practical baselines for future studies. A quantum classification pipeline was developed using a variety of angle encoding schemes. An exhaustive search was performed to identify an optimal subset of four metabolomic features via simulation. These features were used to benchmark multiple encoding strategies via simulation, followed by validation on IBM Quantum hardware. A baseline using Support Vector Machine (SVM) with the same features was established for comparison. The best-performing QSVM achieved an average classification accuracy of 0.9434 on real quantum hardware, which is comparable to the accuracy using classical SVM of 0.9371 on the same feature set. These results highlight the potential of quantum kernels to capture meaningful feature interactions in biomedical data, despite the levels of noise and overhead of quantum computing. This study demonstrates that quantum kernel SVMs can achieve classification performance comparable to classical methods on biomedical data. However, current limitations in quantum hardware, such as qubit communication overhead and noise, pose challenges for practical deployment. Continued improvements in hardware acceleration and error correction are needed to realize the potential of quantum machine learning for biomedical classification tasks.

Continuous-variable quantum secure communication encoded by Gaussian mapping offers high-capacity and high transmission rates. For the theoretically secure encryption scheme of a 1-time pad, a highly reliable coherent-state quantum secure communication system has been established, and its security has been quantitatively evaluated using Wyner's wiretap channel theory. We also propose an information reconstruction scheme based on multidimensional rotation to extract secret messages at low-to-medium signal-to-noise ratios. Meanwhile, to address the unbalanced optical path, we design a self-balanced homodyne detector based on a programmable gain amplifier, achieving an electronic noise variance of 1.624 × 10 - 7 V 2 and a bandwidth of 715 MHz. In 10-km optical fiber transmission, the system successfully achieved secure transmission of the dichroic image, processed 1,536 information blocks, each containing 2 10 continuous variables, with a block error rate of approximately 8.78 × 10 - 5 , and ultimately achieved the secrecy capacity of 2.44 × 10 5 bits per second.

Distributed quantum information processing seeks to overcome the scalability limitations of monolithic quantum devices by interconnecting multiple quantum processing nodes via classical and quantum communication. This approach extends the capabilities of individual devices, enabling access to larger problem instances and novel algorithmic techniques. Beyond increasing qubit counts, it also enables qualitatively new capabilities, such as joint measurements on multiple copies of high-dimensional quantum states. The distinction between single-copy and multi-copy access reveals important differences in task complexity and helps identify which computational problems stand to benefit from distributed quantum resources. At the same time, it highlights trade-offs between classical and quantum communication models and the practical challenges involved in realizing them experimentally. In this review, we contextualize recent developments by surveying the theoretical foundations of distributed quantum protocols and examining the experimental platforms and algorithmic applications that realize them in practice.

Quantum networks connecting quantum processing nodes via photonic links enable distributed and modular quantum computation. In this framework, quantum gates between remote qubits can be realized using quantum teleportation protocols. The essential requirements for such non-local gates are remote entanglement, local quantum logic within each processor, and classical communication between nodes to perform operations based on measurement outcomes. Here, we demonstrate an unconditional Controlled-NOT quantum gate between remote diamond-based qubit devices. The control and target qubits are Carbon-13 nuclear spins, while NV electron spins enable local logic, readout, and remote entanglement generation. We benchmark the system by creating a Greenberger-Horne-Zeilinger state, showing genuine 4-partite entanglement shared between nodes. Using deterministic logic, single-shot readout, and real-time feed-forward, we implement non-local gates without post-selection. These results demonstrate a key capability for solid-state quantum networks, enabling exploration of distributed quantum computing and testing of complex network protocols on full-stack systems.

We introduce a quantum multiplexer (GHZ MUX) architecture that enables deterministic routing of an unknown qubit from a single sender to one of 2n receivers using only local tripartite Greenberger-Horne-Zeilinger (GHZ) states arranged in a binary tree. At each level of the hierarchy, a Bell-basis measurement and classical feed-forward propagate the encoded quantum information along a selected branch while maintaining the appropriate Pauli correction frame. Unlike quantum routing architectures that rely on globally entangled multipartite states, the proposed design composes small GHZ clusters into a modular teleportation hierarchy that requires only local entanglement generation and coherence. This structure achieves full input-output connectivity while preserving deterministic routing control and experimental feasibility for near-term small-scale quantum networks. Beyond routing functionality, we show that the same GHZ-tree structure naturally supports hidden-destination communication. We formalize this extension as the Hidden-Secret GHZ-Tree Routing (HS-GTR) protocol, in which the final receiver remains unknown to external observers and the transmitted quantum state may optionally be protected by a quantum one-time pad. This construction demonstrates that hierarchical GHZ routing can serve not only as a quantum switching architecture but also as a building block for privacy-preserving communication and multi-receiver key establishment in distributed quantum networks.

Quantum computing provides alternative encoding and sampling paradigms for protein structure prediction (PSP), but existing quantum-PSP methods are often limited by resource-scaling issues and by discrete or inefficient encodings for continuous coordinates. To address these limitations, we propose QSyncFold, a hybrid quantum-classical neural network framework that combines quantum superposition with differentiable learning. QSyncFold employs ProtaQode to simultaneously achieve reversible continuous-space encoding of residue coordinates and parameterized interaction modeling. This is realized by encoding residue-pair interactions in superposition via a decomposable Any-State RY (ASRY) operator that is efficient for a limited qubit budget. Algorithmically, QSyncFold trades register size for iteration count, reducing the qubit requirement for each iteration from $O(N)$ to $3+\lceil \log _{2} N \rceil $, where $N$ is the number of residues. This design ensures the framework is experimentally viable under NISQ constraints. On short peptide structure prediction, QSyncFold achieved a 5.25-fold improvement in the lDDT metric compared with the Variational Quantum Eigensolver baseline and demonstrated a clear trade-off between qubit budget and convergence speed. While using quantum baselines as the primary comparison, the method performance approaches AlphaFold2 in the short peptide domain, with classical methods serving as background reference. This study advances the precision and methodology of quantum computing in PSP, illustrating a viable pathway for quantum algorithms in biomolecular modeling.

Quantum secret sharing (QSS) has been previously demonstrated with conceivability in optical-fiber channels. However, extending this framework to the microwave frequency band presents challenges in achieving secure quantum communications over turbulent channels, as intricate turbulence can induce amplitude and phase jitter in quantum signals, leading to decoherence or even interruptions in the communication link. In this work, we propose a microwave-enabled continuous-variable quantum secret sharing (CVQSS) scheme operating over turbulent free-space channels. The protocol explicitly addresses the extreme sensitivity of microwave quantum states to environmental turbulence, which manifests as severe amplitude and phase fluctuations. It incorporates the Shamir threshold scheme to facilitate multi-user secret sharing. We suggest a flexible approach to solving problems of adaptive phase compensation and multi-aperture reception techniques when characterizing an equivalent noise channel based on the Kolmogorov turbulence model. The proposed measurement-device-independent (MDI) architecture renders the protocol immune to all detector-side attacks, provided that the state preparation at the users' side is trusted. Numerical simulations ascertain the performance of the microwave continuous-variable measurement-device-independent quantum secret sharing (CV-MDI-QSS) system and demonstrate the feasibility of practical deployment in complicated turbulent channels. This approach offers a turbulence-resistant solution for dynamic quantum networks through harsh free-space channels implemented in microwave-propagated environments.

Quantum key distribution (QKD) is a cryptographic technique that uses quantum mechanical principles to enable secure key distribution, offering information-theoretic security guaranteed by physical laws. Practical deployment of QKD requires robust, cost-effective systems that can operate in challenging field environments. A major challenge is achieving reliable clock synchronization without adding hardware complexity. Conventional approaches often use separate classical light signals, which increase costs and introduce noise that degrades quantum channel performance. To address this limitation, we demonstrate a QKD system incorporating a recently proposed qubit-based distributed frame synchronization method, deployed over a metropolitan fiber network in Nanning, China. Using the polarization-encoded one-decoy-state BB84 protocol and the recently proposed qubit-based distributed frame synchronization method, our system achieves synchronization directly from the quantum signal, eliminating the need for dedicated synchronization hardware. Furthermore, to counteract dynamic polarization disturbances in urban fibers, the system integrates qubit-based polarization feedback control, enabling real-time polarization compensation through an automated polarization controller using data recovered from the qubit-based synchronization signals. During 12 hours of continuous operation, the system maintained a low average quantum bit error rate of 1.12 ± 0.48%, achieving a secure key rate of 26.6 kbit/s under 18 dB channel loss. Even under a high channel loss of 40 dB, a finite-key secure rate of 115 bit/s was achieved. This study represents a successful long-term validation of a frame-synchronization-based QKD scheme in a real urban environment, demonstrating exceptional stability and high-loss tolerance, and offering an alternative for building practical, scalable, and cost-efficient quantum-secure communication networks.

Quantum hypergraph states extend the well-studied class of graph states by taking into account multi-qubit interactions through hyperedges. They provide a powerful framework to represent a family of quantum states with genuine multipartite entanglement. In this review, we provide a compact overview of the formal structure, entanglement characteristics, and operational relevance of hypergraph states in quantum information theory. We begin by introducing their mathematical foundations and generalizations of the stabilizer formalism. A central focus is placed on their entanglement properties, including the classification under local unitary (LU) and stochastic local operations with classical communication (SLOCC), the quantification of multipartite entanglement, and detection techniques via entanglement witnesses. We also explore other nonclassical features of hypergraph states, such as contextuality and genuine multipartite nonlocality, derived from stabilizer-based Bell-type inequalities. Additional attention is given to the role of hypergraph states in error correction, and as a computational resource in measurement-based quantum computation (MBQC), and to their non-stabilizer character -quantified via resource-theoretic measures of quantum magic. Finally we review their generalization to higher dimensions, i.e. to qudits and continuous variables.

Continuous-variable (CV) quantum key distribution (QKD) allows for quantum secure communication with the benefit of being close to classical coherent communication. In recent years, CV QKD protocols using a discrete number of displaced coherent states have been studied intensively as the modulation can be directly implemented with real devices with finite resolution. Until now, experiments only calculated key rates in the asymptotic regime. Here, we present a CV QKD system using discrete modulation that is especially designed for atmospheric channels. We use polarization encoding to exploit the nonbirefringent nature of the turbulent atmosphere. This allows to expand CV QKD networks beyond the existing fiber backbone. In a laboratory demonstration with a static 3-decibel loss channel, we implemented a recently developed security proof allowing to calculate composable finite-size key rates against independently and identically distributed collective attacks. We applied the full QKD protocol including a quantum random number generator, error correction, and privacy amplification to extract secret keys.

Random number generation is a fundamental task for modern cryptography, secure communications, and stochastic computing. Quantum random number generators (QRNGs) provide inherently unpredictable data derived from quantum physical processes and therefore offer stronger security guarantees than classical approaches. However, some optical QRNGs rely on blue light-emitting diodes (LEDs), which suffer from reduced spectral matching with commonly used silicon avalanche photodiodes (APDs). This mismatch restricts the achievable signal-to-noise ratio (SNR) and limits the extractable quantum entropy. Here, we demonstrate a spontaneous emission-based QRNG employing a green InGaN LED coupled to a silicon APD. The improved spectral overlap between the emitted light and the APD responsivity results in a significantly higher SNR than with previously reported blue LEDs operating at similar electrical power levels. The measured signal is first filtered with a high-pass filter to suppress low-frequency noise, then randomness is extracted using the SHAKE256 hash function. A detailed statistical and spectral analysis is performed to evaluate the extractable entropy of the generated data. A physically reasonable estimation yields a quantum entropy generation rate of 2.78 Gbit/s. These results establish longer-wavelength nitride-based LEDs as a more suitable entropy source for QRNG systems, using inexpensive and widely available silicon photodiodes.

The development of next-generation communication networks with integrated multifunctional capabilities across diverse environments-spanning space, air, land, and sea-requires efficient transduction between disparate information carriers. While substantial progress has been made in transducers for terrestrial applications, devices that unify sensing and communication in underwater environments remain in their infancy. We present an experimental demonstration of a dual-function acoustic-to-optical transducer, enabling simultaneous underwater encrypted communication and distance measurement. Specifically, the transducer efficiently converts underwater acoustic signals into single photons for transmission through optical fibers over distances of up to 50 kilometers. Concurrently, we achieve deep subwavelength precision in distance measurement, reaching an accuracy of 1/250 of the acoustic wavelength, while maintaining robust communication performance. Furthermore, we implement coincidence counting-based encryption to embed acoustic information within noisy optical fiber networks. This work represents a notable step toward next-generation network architectures that seamlessly integrate sensing and communication across heterogeneous media.

Precision aquaculture demands robust communication networks capable of coordinating thousands of distributed sensors across marine and freshwater facilities. Current aquaculture IoT networks face critical challenges including underwater signal attenuation reaching 98% loss at 100 m depth, dynamic topology changes from fish movement and water currents, and severe energy constraints on battery-powered sensor nodes. This paper introduces SQUID-COMM, a novel bio-inspired communication framework emulating the signaling mechanisms of the Colossal Squid (Mesonychoteuthis hamiltoni). The framework introduces seven innovative mechanisms: Bioluminescent Pulse-Coded Modulation (BPCM) achieving 34% higher spectral efficiency through adaptive signal encoding; Chromatophore-Inspired Channel Adaptation (CICA) enabling 15ms frequency hopping response time; Distributed Axon-Ganglia Routing Protocol (DAGRP) maintaining 99.7% packet delivery under 40% node mobility; Tentacle-Topology Self-Organization (TTSO) for dynamic mesh network formation; Giant Fiber Emergency Broadcast (GFEB) achieving sub-50ms critical alert propagation; Photophore Synchronization Protocol (PSP) for microsecond-accurate time coordination; and Ink-Cloud Congestion Control (ICCC) reducing packet loss by 82%. The Enhanced SQUID-COMM variant incorporates Neuromorphic Edge Processing reducing cloud communication by 78%, Federated Learning Coordination for distributed model updates, and Quantum-Resistant Encryption for future-proof security. Experimental evaluation across five aquaculture deployment scenarios demonstrates end-to-end latency of 12.3ms representing 78% reduction compared to LoRaWAN, throughput of 2.4 Mbps in turbid conditions spanning 5-150 NTU, energy efficiency of 0.23 mJ/bit constituting 67% improvement over Zigbee, and network lifetime extension of 340%. Real-world deployment at four commercial facilities across Norway, Egypt, Thailand, and Greece over 120 days processed 2.3 billion sensor readings with 99.94% reliability, enabling fish behavior detection at 94.7% accuracy and early disease detection with 4.2-day lead time. Statistical analysis confirms significant improvements with p-values below 0.001 and Cohen's d exceeding 1.2, while economic evaluation demonstrates annual savings of €89,000-€340,000 per facility.

The Theory of Psychic Quanta (TPQ) postulates the existence of a universal non-local psychic field whose quantized excitations-termed informational quanta (analogous to qubits)-anchor to coherent brain systems to generate individual consciousness as a phenomenological unit. According to this model, the brain does not produce consciousness in an emergentist sense; rather, it acts as a bidirectional biophysical interface that stabilizes the informational quantum without generating it. This reciprocal interaction maintains experiential coherence from biological birth until the cessation of brain activity, at which point the quantum disanchors and reintegrates into the diffuse psychic field. TPQ incorporates temporal dynamics where subjective time emerges from quanta superposition collapse, unifying linear neural processing with atemporal field states. This model integrates recent updates to contemporary informational panpsychism, the Orchestrated Objective Reduction (Orch-OR) theory and Integrated Information Theory (IIT), predicting specific neurophilosophical correlates such as gamma synchronization (40-100 Hz) and inter-individual affective resonance. Predictions include EEG protocols for NDE gamma surges and past-life memory imprints in children. The theoretical implications include reinterpretation of psychological wellbeing as maximum phase coherence between the individual quantum and the universal field, explanation of non-local emotional communication between minds, and empirically verifiable predictions regarding neural patterns during empathic interactions, NDEs, telepathic intuition, and post-mortem awareness. Finally, TPQ proposes a resolution to Chalmers' "hard problem" of consciousness (1995) through dual ontology integrating physical-material and psychic-informational domains.

Continuous-variable quantum key distribution (CVQKD) offers high key rates and compatibility with classical optical communications. Developing scalable and efficient CVQKD networks will facilitate the deployment of large-scale quantum communication networks. This paper proposes a CVQKD network based on the untrusted entanglement states of an optical frequency comb. The scheme generates Einstein-Podolsky-Rosen states with a frequency comb structure via a type-II optical parametric oscillator. By employing the entanglement-in-the-middle scheme, a source-untrusted fully connected CVQKD network capable of distributing secret keys simultaneously can be formed. We analyze the security of the system in the asymptotic and finite-size cases. Simulation results show that with careful control of system loss and noise, the proposed scheme is feasible for deploying a short-distance fully connected CVQKD network. Loss will be the main factor limiting system performance. The proposed scheme offers a new perspective for a multi-user fully connected CVQKD network.

This paper proposes a turbulence-induced perturbation (TIP) approach to address the security degradation of quantum noise stream cipher (QNSC) systems under high transmit power and introduces a variable-order quantum noise stream cipher (VO-QNSC) scheme to further enhance transmission performance. The TIP approach incorporates turbulence-induced perturbation to strengthen the physical-layer security of QNSC in high-power scenarios, while the VO-QNSC scheme significantly improves system performance without increasing algorithmic complexity or redundancy, making it suitable for deployment on satellite terminals with limited computational resources. Simulation results show that, after introducing TIP, the system detection failure probability (DFP) can exceed 99.97% and number of masked signals (NMS) is improved to the order of 103, which effectively enhances the anti-eavesdropping ability of the system. For poor channel conditions, VO-QNSC can improve the receiver sensitivity by up to approximately 0.5 dB, which can meet the requirements of communication security and transmission performance in complex environments.