Toffoli gates are an important primitive in reversible Boolean logic. In quantum computation, these Toffoli gates are composed using other elementary gates, most notably the Clifford+T basis. However, in fault-tolerant implementations of quantum circuits, the T-gate incurs extra cost relative to Clifford gates like the S-gate and CNOT gate. Relative-phase Toffoli Gates (RTOFs) have been proposed as a way to reduce this T-count at the cost of incurring a relative phase that could skew the final quantum states. In this paper, we utilize an observation that the relative phase which RTOFs introduce can be canceled by the appropriate application of less expensive S-gates instead of T-gates. It leverages alternate forms of the RTOF including incorporating S-gates into it or moving around its input bits in order to simplify the logic to erase the relative phase. We find experimentally that our method has a clear advantage in most cases, and identify several types of circuits that it could be synergistic with.

The Sparkle permutation family is used as an underlying building block of the authenticated encryption scheme Schwaemm, and the hash function Esch which are a part of one of finalists in the National Institute of Standards and Technology (NIST) lightweight cryptography standardization process. In this paper, we present distinguishing attacks on 6-round Sparkle384 and 7-round Sparkle512. We used divide-and-conquer approach and the fact that Sparkle permutations are keyless, as a different approach from designers’ long trail strategy. Our attack on Sparkle384 requires much lower time complexity than existing best one; our attack on Sparkle512 is best in terms of the number of attacked rounds, as far as we know. However, our results do not controvert the security claim of Sparkle designers.

In this paper, we present a framework for composing discrete-event simulation models from a large amount of airspace traffic data without using any specific waypoints. The framework consists of two parts. In the first part, abstracted route graphs that indicate representative routes in the airspace are composed. We propose two methods for extracting important routes in the form of graphs based on combination of various technologies such as space partition, trajectory clustering, and skeleton extraction. In the second part, discrete-event simulation models are composed based on statistical information on flight time along each edge of the abstracted route graph. The composed simulation models have intermediate granularity between micro models, such as multi-agent simulation, and macro models, such as queuing models, and therefore they should be classified as mesoscopic models. Finally, we show numerical results to evaluate the accuracy of the simulation model.

Radio Frequency Identification (RFID) is one of the key technologies of the Internet of Things. However, during its application, it faces a huge challenge of co-frequency interference cancellation, that is, the tag collision problem. The multi-tag anti-collision problem is modeled as a Blind Source Separation (BSS) problem from the perspective of system communication transmission layer signal processing. In order to reduce the cost of the reader antenna, this paper uses the boundedness of the tag communication signal to propose an underdetermined RFID tag anti-collision method based on Bounded Component Analysis (BCA). This algorithm converts the underdetermined tag into the signal collision model is combined with the BCA mechanism. Verification analysis was conducted using simulation data. The experimental results show that compared with the nonnegative matrix factorization (NMF) algorithm based on minimum correlation and minimum volume constraints, the bounded component analysis method proposed in this article can perform better. Solving the underdetermined collision problem greatly improves the effect of eliminating co-channel interference of tag signals, improves the system bit error rate performance, and reduces the complexity of the underdetermined model system.

In 2004, Ryoh Fuji-Hara et al. (IEEE Trans. Inf. Theory. 50(10):2408-2420, 2004) proposed an open problem of finding a maximum multiplicative subgroup G in ℤn satisfying two conditions: (1) the sum of any two distinct elements in G is nonzero; (2) any difference from G is still a unit in ℤn. The subgroups satisfying Condition (2) is called difference unit group. Difference unit group is related to difference packing, zero-difference balanced function and partitioned difference family, and thus have many applications in coding and communication. Suppose the canonical factorization of n is ∏ki=1 peii. In this letter, we mainly answer the open problem with the result that the maximum cardinality of such a subgroup G is $\frac{d}{2^m}$, where d = gcd(p1 － 1, p2 － 1, ･･･, pk － 1) and m = ν2(d). Also an explicit construction of such a subgroup is introduced.

To super-resolve low-resolution (LR) face image suffering from strong noise and fuzzy interference, we present a novel approach for noisy face super-resolution (SR) that is based on three-level information representation constraints. To begin with, we develop a feature distillation network that focuses on extracting pertinent face information, which incorporates both statistical anti-interference models and latent contrast algorithms. Subsequently, we incorporate a face identity embedding model and a discrete wavelet transform model, which serve as additional supervision mechanisms for the reconstruction process. The face identity embedding model ensures the reconstruction of identity information in hypersphere identity metric space, while the discrete wavelet transform model operates in the wavelet domain to supervise the restoration of spatial structures. The experimental results clearly demonstrate the efficacy of our proposed method, which is evident through the lower Learned Perceptual Image Patch Similarity (LPIPS) score and Fréchet Inception Distances (FID), and overall practicability of the reconstructed images.