We developed a tunnel diode oscillator and characterized its performance, demonstrating its potential applications in the quantum state readout of electrons in semiconductors and electrons on liquid helium. This cryogenic microwave source demonstrates significant scalability potential for large-scale qubit readout systems due to its compact design and low-power consumption of only 1 µW, making it suitable for integration on the 10 mK stage of a dilution refrigerator. The tunnel diode oscillator exhibits superior amplitude stability compared to commercial microwave sources. The output frequency is centered around 140 MHz, commonly used for qubit readout of electrons in semiconductors, with a frequency tunability of 10 MHz achieved using a varactor diode. Furthermore, the phase noise was significantly improved by replacing the commercially available voltage source with a lead-acid battery, achieving a measured phase noise of -115 dBc/Hz at a 1 MHz offset.

Cavity-enhanced diamond color center qubits can be initialized, manipulated, entangled, and read individually with high fidelity, which makes them ideal for large-scale, modular quantum computers, quantum networks, and distributed quantum sensing systems. However, diamond’s unique material properties pose significant challenges in manufacturing nanophotonic devices, leading to fabrication-induced structural imperfections and inaccuracies in defect implantation, which hinder reproducibility, degrade optical properties and compromise the spatial coupling of color centers to small mode-volume cavities. A cavity design tolerant to fabrication imperfections─such as surface roughness, sidewall slant, and nonoptimal emitter positioning─can improve coupling efficiency while simplifying fabrication. To address this challenge, a deep learning-based optimization methodology is developed to enhance the fabrication error tolerance of nanophotonic devices. Convolutional neural networks (CNNs) are applied to promising designs, such as L2 and fishbone nanobeam cavities, predicting Q-factors at least one-million times faster than traditional finite-difference time-domain (FDTD) simulations, enabling efficient optimization of complex, high-dimensional parameter spaces. The CNNs achieve prediction errors below 3.99% and correlation coefficients up to 0.988. Optimized structures demonstrate a 52% reduction in Q-factor degradation, achieving quality factors of 5 × 104 under real-world conditions and a 2-fold expansion in field distribution, enabling efficient coupling of nonoptimally positioned emitters. Compared to previous deep-learning optimization methods, this approach achieves twice the Q-factor performance in the presence of fabrication errors, significantly enhancing device robustness. Hence, this methodology enables scalable, high-yield manufacturing of robust nanophotonic devices, including the cavity-enhanced diamond quantum systems developed in this study.