Quantum Information Processing最新文献

In this paper, we introduce the variational quantum Kolmogorov–Arnold network (VQKAN), a novel framework for quantum machine learning that uses measurement results of qubits as neurons and quantum gates as synapses. VQKAN is applied to multi-dimensional function fitting and binary classification tasks, where it shows reduced overfitting and competitive accuracy compared to a baseline quantum neural network (QNN) under the conditions tested. We also evaluate VQKAN on a Fourier partial differential equation; on that task, VQKAN does not surpass QNN, QCNN, TTN, or LSTM baselines, and we report this negative result and discuss its implications. Empirically, the per-iteration computation time grows sub-cubically with the number of qubits over the tested range (N_qin [2,8]) (best fit (Tpropto N_q^{2.5}), (R^2{=}0.99)); a pure logarithmic fit is rejected ((R^2{=}0.78)). Within this practical regime, VQKAN offers a favourable trade-off between accuracy and gate count. A key empirical strength of VQKAN is reduced overfitting on the tested fitting tasks compared to QNN, allowing for better generalization even with limited data. Additionally, we present a modified compact ansatz that further enhances accuracy on the regression benchmark; on the classification benchmark, this ansatz is more expressive but more expensive and is not competitive with the canonical ansatz under MAE, a negative result we report alongside the positive one. All numerical experiments use noiseless statevector simulation; we further provide a finite-shot study (256–4096 shots), showing that the test-MAE remains within ∼ 0.06 of the infinite-shot value down to 4096 shots on the regression task, with no systematic degradation at lower shot counts, indicating moderate shot-noise robustness. Hardware (depolarizing / decoherence) noise was not modelled and is left to future work. These contributions establish VQKAN as a promising tool for advancing quantum machine learning and optimization techniques on tasks where edge-wise function approximations are natural.

Reference-frame-independent quantum key distribution (RFI-QKD) has the advantage of tolerating the slow varied reference frame. Two remote communication parties can share secret keys in the case of unknown and drifted reference frames. However, in the practical QKD system, the drift of reference frames will inevitably compromise the performance of the QKD system, resulting in the reduction of the secret key rate and transmission distance. In this paper, we attempt to improve the performance of RFI-QKD and RFI measurement-device-independent QKD (RFI MDI-QKD) based on the two-way local operations and classical communications (TW-LOCC) classical post-processing method. We focus on the iterative B-step-optimized TW-LOCC due to its immediate experimental feasibility. The simulation results show that the TW-LOCC method can significantly improve the transmission distance and secret key rate of RFI-QKD and RFI MDI-QKD by optimizing the number of executions B steps, and the maximum safe transmission distance is increased by 92 and 60 km, respectively. Our work provides a new idea for the post-processing of RFI-QKD protocols and further enhances the practicability and robustness of RFI-QKD.

Preserving quantum correlations in noisy environments is fundamental for advancing quantum technologies. We investigate the interplay between channel memory and indefinite causal order in protecting bipartite correlations—coherence, entanglement, and discord—between two qutrits. Our analysis covers amplitude damping channels with V-type and (Lambda )-type transitions, as well as trit flip, phase flip, and depolarizing channels. We show that memory effects in correlated channels not only delay entanglement sudden death but also enhance the resilience of quantum correlations against decoherence. Strikingly, the quantum switch of amplitude damping and bit-flip channels offers a significant advantage over their memoryless counterparts, demonstrating that indefinite causal order can amplify the benefits of channel memory. Fidelity analysis further confirms that correlations in two-qutrit states remain substantially more robust in correlated channels across all scenarios studied. These findings highlight the potential of combining channel memory and causal indefiniteness as powerful resources for reliable quantum communication and information processing.

In this paper, necessary and sufficient conditions for the self-orthogonality of t-generator quasi-cyclic (QC) codes are established with respect to the Euclidean, Hermitian, and symplectic inner products, respectively. In particular, by investigating the dual structure of a new family of 2-generator QC codes, we derive necessary and sufficient conditions for such codes to be dual-containing under the three aforementioned inner products. In terms of construction, this new family of 2-generator QC codes admits a more general form than several classes of codes in the existing literature. Furthermore, our results on the necessary and sufficient conditions for the self-orthogonality and dual-containing properties of such codes also extend those in related literature. Based on the derived self-orthogonality and dual-containing criteria, we further construct various quantum stabilizer codes and quantum synchronizable codes with favorable parameters.

We investigate the information transmission capabilities of a dissipative three-mode bosonic quantum system by analyzing its associated quantum channel. The study focuses on the evaluation of coherent information and entanglement-assisted classical capacity under both pure and mixed input states. The system dynamics are modeled using a Lindblad master equation, allowing us to capture the effects of environmental decoherence and dissipation. For pure states, the two capacity measures exhibit close agreement, while for mixed states a clear separation emerges due to the contribution of input entropy. The results further reveal a nontrivial dependence on evolution time and Hilbert space dimension, highlighting the competing roles of coherent interactions and noise. These findings provide insight into the fundamental limits of information transmission in realistic open quantum systems.

In this research paper, a quantum-inspired design and optimization technique for extremely efficient photonic wavelength division multiplexers (WDMs) and bandpass filters (BPFs) developed for advanced integrated photonic communication platforms is presented. Tunneling-inspired resonance and evanescent coupling effects in nanoscale photonic structures that are essential to advanced photonic circuits are not sufficiently captured by classical electromagnetic models when the device dimensions approach closer to the nanoscale. In order to overcome this constraint, we propose a tunneling-based model using quantum-inspired analogies that provides for precise control of resonance, phase coherence, and energy transfer by interpreting evanescent field coupling as a quantum-inspired tunneling analogy over potential barriers. An enhanced iterative random grouping with adaptive differential evolution (IRRG-ADE) algorithm, designed to tackle large-scale, non-separable photonic design domains with quantum-inspired constraints, is implemented to tackle the resulting multi-objective optimization problem. The numerical simulations demonstrate significant advantages over classical electromagnetic systems, with an insertion loss of 0.45 dB, crosstalk suppression of 29.1 dB, and a passband flatness of 0.96. The proposed structure provides a scalable approach for creating spectrally stable, CMOS-compatible wavelength-selective components for next-generation advanced photonic communication and integrated photonic platforms by combining quantum tunneling analogies via computational photonic optimization. The proposed framework is quantum-inspired in the sense that it utilizes mathematical analogies between optical wave propagation and quantum tunneling phenomena for photonic structure optimization. The presented devices remain classical passive dielectric photonic structures and do not directly manipulate quantum states, entanglement, or single-photon dynamics.

The Quantum Fisher Information Matrix (QFIM) quantifies how sensitive parameterized quantum states are to changes in their parameters. Recently, it has been used to improve variational quantum algorithm optimization through geometry-aware techniques. However, estimating the QFIM—particularly its off-block-diagonal elements—requires substantial resources. To address this, we introduce a novel protocol that efficiently computes these elements for commuting-block variational circuits. Our approach reduces the number of quantum state preparations from (O(m^2)) to (O(L^2)), where m is the number of parameters and L the number of circuit layers. This also lowers classical measurement and post-processing requirements, improving computational efficiency.

Applying post-selection in each step of an iterated protocol leads to sensitive quantum dynamics that may be utilized to test and benchmark current quantum computers. An example of this type of protocols was originally proposed for the task of matching an unknown quantum state to a reference state. We propose to employ the quantum state matching protocol for the purpose of testing and benchmarking quantum computers. In particular, we implement this scheme on freely available IBM superconducting quantum computers. By comparing measured values with the theoretical conditional probability of the single, final post-selected qubit, which is easy to calculate classically, we define a benchmark metric. Additionally, the standard deviation of the experimental results from their average serves as a secondary benchmark metric, characterizing fluctuations in the given device. A peculiar feature of the considered protocol is that there is a phase parameter of the initially prepared state, on which the resulting conditional probability should not depend. A careful analysis of the measured values indicates that its dependence on the initial phase can reveal useful information about coherent gate errors of the quantum device.

Quantum computing offers novel paradigms for data representation and signal processing, yet traditional Quantum Image Representation (QIP) models often face significant challenges regarding qubit resources and state preparation complexity. This paper introduces a hybrid quantum-classical framework that utilizes Discrete-Time Quantum Walks (DTQW) as a generative mechanism for construction of classical dictionary learning. The core contribution of this work lies in the spectral and informational characterization of the quantum-generated basis. A comparative analysis against industry-standard benchmarks, including the Discrete Cosine Transform (DCT) and Haar Wavelet Transform (WAV), reveals that the DTQW basis operates in a high-entropy representational regime, achieving a maximum normalized Shannon entropy ((H/H_{max} approx 1.0)). This property facilitates a delocalized or “holographic” distribution of signal energy across the entire coefficient spectrum. Furthermore, we show that applying unitary quantum operator to the position register enables visually interpretable image transformations through a computationally efficient coefficient reuse mechanism. This research shifts the focus from traditional compression-centric imaging toward a robustness-centric quantum-assisted paradigm, offering promising applications in secure communication and resilient image representation.

We present three new algorithms for efficient in-place estimation of average fidelity of a d-dimensional quantum logic gate, without using ancilla qubits. The main advantage of our algorithms is the much smaller usage of truly random bits compared to what was known so far. Reducing the requirement of classical seed randomness increases the reliability of estimation, as high-quality random bits are an expensive computational resource and commonly used sources of random bits are either pseudorandom or have a non-trivial bias. We use cutting edge tools from classical and quantum derandomisation theory, viz. k-wise independent generators and k-fold quantum tensor product expanders in order to obtain our new randomness efficient algorithms. Our algorithms are suitable for NISQ and early fault-tolerant quantum computers where one wants to reduce the number of control pulses that can potentially be used, in order to minimise the complexity of the control circuitry and also due to coherence time limitations.