Quantum computing threatens classical cryptography and the convergence of quantum key distribution (QKD), and steganography offers an unconditionally secure hybrid approach for postquantum communication. QKD ensures provable secrecy via quantum mechanics, while steganography conceals the existence of communication providing stealth and plausible deniability. This systematic review explores integrated frameworks that combine QKD with classical and quantum steganographic techniques to enhance confidentiality, integrity, and undetectability. The study categorizes existing hybrid models and evaluates their performance using metrics such as quantum bit error rate (QBER), entropy, and imperceptibility. It examines quantum steganographic protocols including quantum LSB, entangled‐state hiding, and covert key transmission, analyzing their resilience against quantum noise and steganalysis. Despite promising simulation tools and real‐time QKD implementations, challenges remain in scalability, interoperability, and formal security guarantees. This review identifies key limitations in current systems and highlights future directions for developing robust, scalable, and stealth‐aware quantum–classical security frameworks. To the best of current knowledge, this is the first systematic synthesis of hybrid QKD–steganography frameworks offering a novel taxonomy and critical analysis to advance secure communication infrastructures in the postquantum era.
Semiquantum secure direct communication (SQSDC) allows a quantum user and a classical user to exchange confidential messages without preshared keys. However, existing SQSDC protocols are predominantly unidirectional, and those supporting bidirectional transmission often lack rigorous security checking in both directions. Here, we propose a bidirectional SQSDC protocol utilizing four‐photon states composed of two Bell states. The first Bell state facilitates forward transmission via Z‐basis encoding and decoding, while the second Bell state enables reverse transmission through bit‐flip operations. Security is ensured by Bell‐state measurements on checking photon pairs prior to each decoding phase, with photon reordering eliminating the need for preshared keys. Security analysis confirms robustness against intercept‐resend, entanglement‐measurement, and Trojan horse attacks. The protocol achieves a transmission efficiency of 30% and requires only currently available technologies, making it experimentally feasible for practical semiquantum communication networks.
We propose the models of Riemannian manifold optimization techniques to enhance the performance of the quantum approximate optimization algorithm (QAOA) for combinatorial optimization problems on near‐term quantum devices. The approach leverages the intrinsic geometric structure of the problem domain, addressing the nonconvexity of the QAOA objective function and overcoming challenges posed by traditional gradient descent optimizers. We introduce three Riemannian optimization methods, including Riemannian steepest descent, Riemannian conjugate gradient, and Riemannian trust regions for QAOA, and demonstrate their superiority over other techniques, such as Bayesian optimization, particle swarms, Nelder–Mead, and Adam. Riemannian trust regions generally achieve a convergence speed that is three times faster than other methods such as Adan, TuRBO, and Adam.
This study presents Q2SV, an FPGA-based quantum state vector simulator implemented using high-level synthesis (HLS), capable of simulating quantum circuits with up to 29 qubits. Using FPGA parallelism, efficient memory allocation, and hardware-optimized execution pipelines, Q2SV achieves scalable quantum simulation without requiring iterative resynthesis. The system’s workflow processes OpenQASM circuits by providing a flexible and general-purpose quantum state vector simulation approach. Experimental evaluations demonstrate significant reductions in execution time and storage requirements, positioning FPGAs as a possible alternative to GPUs and CPUs for large-scale quantum circuit emulation. While not yet matching the raw computational power of high-end CPUs and GPUs in all cases, this work establishes a foundational framework for future optimizations in hardware-accelerated quantum simulation. This work provides an adaptable HLS-based code that serves as a template that paves the way for enhanced memory management, parallel processing, and architecture-specific optimizations, enabling more efficient FPGA-based quantum simulations in the future.
Quantum artificial intelligence (QAI) has made significant progress in recent years. However, the lack of platforms for large-scale QAI models training has constrained research on related algorithms, such as quantum large language models. In this paper, we introduce LFQAP2, a supercomputing-based platform designed for training large-scale QAI models. The platform employs a hybrid parallelization strategy that integrates data parallelism and model parallelism. For data parallelism, different threads compute the loss for distinct sample batches. For model parallelism, different processes compute the gradients for separate parameter batches. This parallelization strategy integrates the strengths of both data parallelism and model parallelism. It has high node-internal computing efficiency and excellent scalability, and can reduce internode communication overhead, limiting it to the exchange of only a small number of floating-point values. LFQAP2 is implemented using the MPI + OpenMP parallel programming model and deployed on a 16-node cluster, where each node is equipped with 32 computing cores. To assess the platform’s efficiency, we evaluate its performance on two types of tasks: image recognition (MNIST dataset classification) and natural language processing (word embedding). For the 16-qubit MNIST classification task with 128 trainable parameters, the 32-thread acceleration efficiency reaches 0.852, while the 16-node multiprocess acceleration efficiency is 0.357. For the 9-qubit word embedding task with 288 trainable parameters, the 32-thread acceleration efficiency also reaches 0.843, and the 16-node multiprocess acceleration efficiency is 0.598. We successfully completed the training of a 288-parameter model within 23 min, providing a robust computational platform to support research on large-scale quantum models.
