Chip-based quantum computing with photons

Kai‐Mei C. Fu

huihui zhu, Tian Chen, Haoran Ma +15 more

Abstract Recent years have witnessed significant progress in quantum computing and its applications, driven by the emergence of integrated quantum photonic chips. These chips offer significant advantages of scalability, stability, and low-cost advantages, enabling new possibilities in miniaturized quantum systems. Here, we provide an overview of the advances in integrated photonic chips for quantum computing, beginning with a summary of the prevalent photonic integrated platforms and key components for quantum computing systems. We then discuss a range of quantum computing applications, which are divided into two main directions: one is the Noisy Intermediate Scale Quantum (NISQ) era, and the other is towards the future universal quantum computing. Finally, the review culminates in a perspective on the challenges of high-performance chip-based quantum computing, as well as a prospect for future opportunities in integrated quantum networks.

Christophe Couteau

Ahmed El-Halawany, Michael Leuenberger

Liang-Hui Du, Yong Hu, Zheng-Wei Zhou +2 more

We study a quantum computing system using microwave photons in transmission line resonators on a superconducting chip as qubits. We show that linear optics and other controls necessary for quantum computing can be implemented by coupling to Josephson devices on the same chip. By taking advantage of the strong nonlinearities in Josephson junctions, photonic qubit interactions can be realized. We analyze the gate error rate to demonstrate that our scheme is realistic even for Josephson devices with limited decoherence times. As a conceptually innovative solution based on existing technologies, our scheme provides an integrated and scalable approach to the next key milestone for photonic qubit quantum computing.

Hui Zhang

In recent years, optical computing becomes a powerful task-specific platform, under the increasing demand for computing power (especially for neural networks). This doctoral thesis is devoted to the design, simulation, and testing of integrated silicon photonic circuits for optical neural networks and machine-learning-assisted quantum computing tasks. In Chapter 3, we demonstrate an optical neural chip that implements complex-valued neural networks and benchmark its performance under practical settings, proving strong learning capabilities. In Chapter 4, we demonstrate an efficient, physics-agnostic, and closed-loop protocol for training optical neural networks on chip. In Chapter 5, we demonstrate a silicon photonic chip that achieves chip-to-chip teleportation of high-dimensional quantum states, using the trainable quantum autoencoders. High fidelities are achieved between the input qutrit and the qutrit decoded from the teleported state. Our results present a promising avenue towards realizing deep optical neural networks and challenging quantum computing tasks with dedicated integrated silicon photonic circuits.