Deep Learning Based Uplink Multi-User SIMO Beamforming Design

The advancement of fifth generation (5G) wireless communication networks has created a greater demand for wireless resource management solutions that offer high data rates, extensive coverage, minimal latency and energy-efficient performance. Nonetheless, traditional approaches have shortcomings when it comes to computational complexity and their ability to adapt to dynamic conditions, creating a gap between theoretical analysis and the practical execution of algorithmic solutions for managing wireless resources. Deep learning-based techniques offer promising solutions for bridging this gap with their substantial representation capabilities. We propose a novel unsupervised deep learning framework, which is called NNBF, for the design of uplink receive multi-user single input multiple output (MU-SIMO) beamforming. The primary objective is to enhance the throughput by focusing on maximizing the sum-rate while also offering computationally efficient solution, in contrast to established conventional methods. We conduct experiments for several antenna configurations. Our experimental results demonstrate that NNBF exhibits superior performance compared to our baseline methods, namely, zero-forcing beamforming (ZFBF) and minimum mean square error (MMSE) equalizer. Additionally, NNBF is scalable to the number of single-antenna user equipments (UEs) while baseline methods have significant computational burden due to matrix pseudo-inverse operation

Coherent control of electron spin qubits in silicon using a global field

Silicon spin qubits promise to leverage the extraordinary progress in silicon nanoelectronic device fabrication over the past half century to deliver large-scale quantum processors. Despite the scalability advantage of using silicon technology, realising a quantum computer with the millions of qubits required to run some of the most demanding quantum algorithms poses several outstanding challenges, including how to control many qubits simultaneously. Recently, compact 3D microwave dielectric resonators were proposed as a way to deliver the magnetic fields for spin qubit control across an entire quantum chip using only a single microwave source. Although spin resonance of individual electrons in the globally applied microwave field was demonstrated, the spins were controlled incoherently. Here we report coherent Rabi oscillations of single electron spin qubits in a planar SiMOS quantum dot device using a global magnetic field generated off-chip. The observation of coherent qubit control driven by a dielectric resonator establishes a credible pathway to achieving large-scale control in a spin-based quantum computer

Coherent control of electron spin qubits in silicon using a global field

Silicon spin qubits promise to leverage the extraordinary progress in silicon nanoelectronic device fabrication over the past half century to deliver large-scale quantum processors. Despite the scalability advantage of using silicon technology, realising a quantum computer with the millions of qubits required to run some of the most demanding quantum algorithms poses several outstanding challenges, including how to control so many qubits simultaneously. Recently, compact 3D microwave dielectric resonators were proposed as a way to deliver the magnetic fields for spin qubit control across an entire quantum chip using only a single microwave source. Although spin resonance of individual electrons in the globally applied microwave field was demonstrated, the spins were controlled incoherently. Here we report coherent Rabi oscillations of single electron spin qubits in a planar SiMOS quantum dot device using a global magnetic field generated off-chip. The observation of coherent qubit control driven by a dielectric resonator establishes a credible pathway to achieving large-scale control in a spin-based quantum computer

Bounds to electron spin qubit variability for scalable CMOS architectures

Spins of electrons in CMOS quantum dots combine exquisite quantum properties and scalable fabrication. In the age of quantum technology, however, the metrics that crowned Si/SiO2 as the microelectronics standard need to be reassessed with respect to their impact upon qubit performance. We chart the spin qubit variability due to the unavoidable atomic-scale roughness of the Si/SiO$_2$ interface, compiling experiments in 12 devices, and developing theoretical tools to analyse these results. Atomistic tight binding and path integral Monte Carlo methods are adapted for describing fluctuations in devices with millions of atoms by directly analysing their wavefunctions and electron paths instead of their energy spectra. We correlate the effect of roughness with the variability in qubit position, deformation, valley splitting, valley phase, spin-orbit coupling and exchange coupling. These variabilities are found to be bounded and lie within the tolerances for scalable architectures for quantum computing as long as robust control methods are incorporated.Comment: 20 pages, 8 figure

Impact of electrostatic crosstalk on spin qubits in dense CMOS quantum dot arrays

Quantum processors based on integrated nanoscale silicon spin qubits are a promising platform for highly scalable quantum computation. Current CMOS spin qubit processors consist of dense gate arrays to define the quantum dots, making them susceptible to crosstalk from capacitive coupling between a dot and its neighbouring gates. Small but sizeable spin-orbit interactions can transfer this electrostatic crosstalk to the spin g-factors, creating a dependence of the Larmor frequency on the electric field created by gate electrodes positioned even tens of nanometers apart. By studying the Stark shift from tens of spin qubits measured in nine different CMOS devices, we developed a theoretical frawework that explains how electric fields couple to the spin of the electrons in increasingly complex arrays, including those electric fluctuations that limit qubit dephasing times $T_2^*$. The results will aid in the design of robust strategies to scale CMOS quantum technology.Comment: 9 pages, 4 figure

High-fidelity operation and algorithmic initialisation of spin qubits above one kelvin

The encoding of qubits in semiconductor spin carriers has been recognised as a promising approach to a commercial quantum computer that can be lithographically produced and integrated at scale. However, the operation of the large number of qubits required for advantageous quantum applications will produce a thermal load exceeding the available cooling power of cryostats at millikelvin temperatures. As the scale-up accelerates, it becomes imperative to establish fault-tolerant operation above 1 kelvin, where the cooling power is orders of magnitude higher. Here, we tune up and operate spin qubits in silicon above 1 kelvin, with fidelities in the range required for fault-tolerant operation at such temperatures. We design an algorithmic initialisation protocol to prepare a pure two-qubit state even when the thermal energy is substantially above the qubit energies, and incorporate high-fidelity radio-frequency readout to achieve an initialisation fidelity of 99.34 per cent. Importantly, we demonstrate a single-qubit Clifford gate fidelity of 99.85 per cent, and a two-qubit gate fidelity of 98.92 per cent. These advances overcome the fundamental limitation that the thermal energy must be well below the qubit energies for high-fidelity operation to be possible, surmounting a major obstacle in the pathway to scalable and fault-tolerant quantum computation

On the Scalability of SiMOS Spin Based Qubit Devices

Quantum information technology promises to build outstanding computers that can solve computationally-demanding problems, deemed practically impossible to solve with the best modern supercomputers. The range of such problems extends to various areas such as drug development, material discovery, cybersecurity, financial modelling and climate change, indicating that quantum computers may transform the world we live in once they are successfully built. There are many approaches to realize a quantum computer. This dissertation is about one approach, namely silicon metal-oxide-semiconductor (SiMOS) spin-based qubit devices, and how to construct a large-scale quantum computer out of these devices. SiMOS spin-based qubits introduce a propitious quantum computing platform since they can operate at relatively elevated cryogenic temperatures, preserve the quantum information they store for a long time, and leverage from their compatibility to CMOS technology in order to scale-up. Currently, one- and two-qubit devices of sufficiently high quality have been demonstrated with this platform. Nevertheless, we need at least millions of qubits to have quantum computers that can be used in practical applications. Even with the scalability advantage of these devices, the objective is quite challenging and requires a very dedicated multi-disciplinary effort. Motivated by the size and importance of the problem, in this dissertation we attempt to address issues related to the scalability of SiMOS spin-based qubit devices. We first report our experimental results on devices fabricated by a CMOS foundry process, which will eventually have to be adopted as the devices scale-up and get too complex to fabricate in non-industrialized processes. We then propose a novel technique to control qubits, global control via microwave fields generated by off-chip dielectric resonators, which can potentially manipulate millions of qubits altogether in a device. Using our proposed technique we successfully demonstrate the coherent control of two qubits, confirming that it can replace the currently mainstream but non-scalable techniques. The dissertation concludes with an overall discussion about the potential challenges on the scalability, and presents a framework that is able to overcome those challenges. Consequently, despite the challenges along the way, SiMOS spin-based qubits have a growing prospect to be the platform on which large-scale quantum computers will be built and hence the technology that will shape our future

Service shop Register Application

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Personalized Federated Multi-Task Learning over Wireless Fading Channels

Multi-task learning (MTL) is a paradigm to learn multiple tasks simultaneously by utilizing a shared network, in which a distinct header network is further tailored for fine-tuning for each distinct task. Personalized federated learning (PFL) can be achieved through MTL in the context of federated learning (FL) where tasks are distributed across clients, referred to as personalized federated MTL (PF-MTL). Statistical heterogeneity caused by differences in the task complexities across clients and the non-identically independently distributed (non-i.i.d.) characteristics of local datasets degrades the system performance. To overcome this degradation, we propose FedGradNorm, a distributed dynamic weighting algorithm that balances learning speeds across tasks by normalizing the corresponding gradient norms in PF-MTL. We prove an exponential convergence rate for FedGradNorm. Further, we propose HOTA-FedGradNorm by utilizing over-the-air aggregation (OTA) with FedGradNorm in a hierarchical FL (HFL) setting. HOTA-FedGradNorm is designed to have efficient communication between the parameter server (PS) and clients in the power- and bandwidth-limited regime. We conduct experiments with both FedGradNorm and HOTA-FedGradNorm using MT facial landmark (MTFL) and wireless communication system (RadComDynamic) datasets. The results indicate that both frameworks are capable of achieving a faster training performance compared to equal-weighting strategies. In addition, FedGradNorm and HOTA-FedGradNorm compensate for imbalanced datasets across clients and adverse channel effects.https://doi.org/10.3390/a1511042