‘‘Qubit routing” refers to the task of modifying quantum circuits so that they satisfy the connectivity constraints of a target quantum computer. This involves inserting SWAP gates into the circuit so that the logical gates only ever occur between adjacent physical qubits. The goal is to minimise the circuit depth added by the SWAP gates. In this article, we propose a qubit routing procedure that uses a modified version of the deep Q-learning paradigm. The system is able to outperform the qubit routing procedures from two of the most advanced quantum compilers currently available (Qiskit and t \( | \) ket \( \rangle \) ), on both random and realistic circuits, across a range of near-term architecture sizes (with up to 50 qubits).

This paper proposes a method for minimum mean squared error (MMSE) adaptive waveform design (AWD) in multiple-input-multiple-output (MIMO) active sensing systems which are used to track moving targets. The method proposed herein prompts two computational improvements compared to a related method for static targets. Consideration of moving targets also introduces the possibility of `model mismatch' between the actual motion of the targets, and the model available to the MMSE AWD system. Results show that the proposed method leads to an improvement in mean squared error performance of up to 29% compared to the non-adaptive case.

Real-world wireless communication channels are typically highly underspread: their coherence time is much greater than their delay spread. In such situations, it is common to assume that, with sufficiently high bandwidth, the capacity without channel state information (CSI) at the receiver (termed the noncoherent channel capacity) is approximately equal to the capacity with perfect CSI at the receiver (termed the coherent channel capacity). In this paper, we propose a lower bound on the noncoherent capacity of highly underspread fading channels, which assumes only that the delay spread and coherence time are known. Furthermore, our lower bound can be calculated recursively, with each increment corresponding to a step increase in bandwidth. These properties, we contend, make our lower bound an excellent candidate as a simple method to verify that the noncoherent capacity is indeed approximately equal to the coherent capacity for typical wireless communication applications. We precede the derivation of the aforementioned lower bound on the information capacity with a rigorous justification of the mathematical representation of the channel. Furthermore, we also provide a numerical example for an actual wireless communication channel and demonstrate that our lower bound does indeed approximately equal the coherent channel capacity.

To deploy effective communication systems in vehicle cavities, it is critical to understand the time variation of the in-vehicle channel. Initially, rapid channel variation is addressed, which is characterized in the frequency domain as a Doppler spread. It is then shown that, for typical Doppler spreads, the in-vehicle channel is underspread, and therefore, the information capacity approaches the capacity achieved with perfect receiver channel state information in the infinite bandwidth limit. Measurements are performed for a number of channel variation scenarios (e.g., absorptive motion, reflective motion, one antenna moving, and both antennas moving) at a number of carrier frequencies and for a number of cavity loading scenarios. It is found that the Doppler spread increases with carrier frequency; however, the type of channel variation and loading appear to have little effect. Channel variation over a longer time period is also measured to characterize the slower channel variation. Channel variation is a function of the cavity occupant motion, which is difficult to model theoretically; therefore, an empirical model for the slow channel variation is proposed, which leads to an improved estimate of the channel state.

Interest is increasing in the use of Wireless Sensor Networks (WSNs) for intra-vehicle network applications owing to the desire of manufacturers to deliver advanced in-car services. Operating WSNs in the unlicensed 2.4 GHz frequency band that is shared with other wireless technologies, e.g., Bluetooth and WiFi, brings additional challenges owing to interference and multipath fading. Therefore, a comprehensive understanding of the intra-vehicle wireless channel is essential for Radio Frequency (RF) resource allocation management, and to establish if various diversity techniques will assist in mitigating the effects of interference and fading in this environment. In this paper, we present our preliminary results in measuring the wireless channel for different scenarios within a car and also assessing the performance benefits that are potentially available from the use of Frequency Diversity (FD) for Line-of-Sight (LOS) and Non Line-of-Sight (NLOS) cases. A cross correlation based analysis of the results shows that FD offers a route to improve Intra-Vehicle Communication (IVC) quality by the use of smart frequency agile MAC protocols.