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In this article, we propose new formulations of max-sum and max-min dispersion problems that enable solutions via the Grover adaptive search (GAS) quantum algorithm, offering quadratic speedup. Dispersion problems are combinatorial optimization problems classified as NP-hard, which appear often in coding theory and wireless communications applications involving optimal codebook design. In turn, GAS is a quantum exhaustive search algorithm that can be used to implement full-fledged maximum-likelihood optimal solutions. In conventional naive formulations, however, it is typical to rely on a binary vector spaces, resulting in search space sizes prohibitive even for GAS. To circumvent this challenge, we instead formulate the search of optimal dispersion problem over Dicke states, an equal superposition of binary vectors with equal Hamming weights, which significantly reduces the search space leading to a simplification of the quantum circuit via the elimination of penalty terms. In addition, we propose a method to replace distance coefficients with their ranks, contributing to the reduction of the number of qubits. Our analysis demonstrates that as a result of the proposed techniques, a reduction in query complexity compared to the conventional GAS using the Hadamard transform is achieved, enhancing the feasibility of the quantum-based solution of the dispersion problem.

Grover adaptive search (GAS) is a quantum exhaustive search algorithm designed to solve binary optimization problems. In this article, we propose higher order binary formulations that can simultaneously reduce the numbers of qubits and gates required for GAS. Specifically, we consider two novel strategies: one that reduces the number of gates through polynomial factorization, and the other that halves the order of the objective function, subsequently decreasing circuit runtime and implementation cost. Our analysis demonstrates that the proposed higher order formulations improve the convergence performance of GAS by reducing both the search space size and the number of quantum gates. Our strategies are also beneficial for general combinatorial optimization problems using one-hot encoding.

Facial skin blood flow (SkBF) has attracted attention as an autonomic indicator because it influences facial colour, which informs others of emotional states, and facial temperature related to social anxiety. Previous studies have examined the facial SkBF in people experiencing emotions; however, facial SkBF changes in the observers of emotions are poorly understood. Our study clarified facial SkBF changes related to observing others’ emotions by comparing the changes with other physiological indices. Thirty healthy participants (24 females; mean age: 22.17) observed six types of facial expressions (neutral, angry, and embarrassed expressions with and without facial blushing) and rated the emotional intensity of the other person. We measured their facial SkBF, finger SkBF, and cardiac RR interval as they made their observations. Facial SkBF generally decreased in relation to observing emotional faces (angry and embarrassed faces) and significantly decreased for angry expressions with blushing. None of the participants noticed blushing of facial stimuli. For the RR interval and finger SkBF, there was no variation depending on the observed facial expressions, although there was a general increase related to observation. These results indicated that facial SkBF is sensitive and reactive to emotional faces—especially angry faces with blushing— compared with other autonomic indices. The facial SkBF changes were not related to either RR interval changes or the intensity rating, suggesting that facial SkBF changes may be caused by vasoconstriction and have potential functions for our emotions. The decrease in facial SkBF may have a role in calming observers by preventing them from adopting the same emotional state as a person with intense anger. These findings clarify daily facial SkBF fluctuations and their relationship with our emotional processing in interpersonal situations.

In this article, we propose a novel method of formulating an NP-hard wireless channel assignment problem as a higher-order unconstrained binary optimization (HUBO), where the Grover adaptive search (GAS) is used to provide a quadratic speedup for solving the problem. The conventional method relies on a one-hot encoding of the channel indices, resulting in a quadratic formulation. By contrast, we conceive ascending and descending binary encodings of the channel indices, construct a specific quantum circuit, and derive the exact numbers of qubits and gates required by GAS. Our analysis clarifies that the proposed HUBO formulation significantly reduces the number of qubits and the query complexity compared with the conventional quadratic formulation. This advantage is achieved at the cost of an increased number of quantum gates, which we demonstrate can be reduced by our proposed descending binary encoding.

Abstract In the IEEE 802.11 specifications, a wireless fidelity (Wi‐Fi) access point (AP) broadcasts a beacon frame every 0.1024 s, and the received signal strength indicator (RSSI) is measured at a user terminal. RSSI is used as a metric for AP selection, but it does unlikely correlate with the actual quality of service, as has been pointed out in previous literature. In this letter, a simple metric is proposed that correlates with the effective throughput offered by a public Wi‐Fi AP. Specifically, the number of beacon frames within a time window is counted instead of using RSSI. The proposed metric can be easily calculated as it does not rely on advanced techniques such as machine learning. In a realistic IEEE 802.11a/n/ac network, the correlation coefficient between the proposed metric and the effective total throughput is 0.874 in the time window of 1 s, which is higher than a conventional metric, indicating the potential for low‐latency identification of whether a public Wi‐Fi AP can provide good throughput.