Recent Results in Photonic Quantum Computations, Simulations and Quantum Networks

Debarka Mukhopadhyay, Siddhartha Bhattacharyya, Jan Platoš +3 more

Jian-Wei Pan

Quantum information technology has been developing rapidly in last two decades. Very recently, quite a few optical quantum key distribution networks have been reported, enabling preliminary practical applications in secure information transfer. Meanwhile, a number of significant progresses based on quantum network have also been made in the research of linear optical quantum computing, simulation and metrology. However, on the way towards large-scale optical quantum network serious problems occur. On the one hand, the distance of fiber-based quantum communication is limited, due to intrinsic fiber loss and decreasing of entanglement quality caused by the noisy environment. On the other hand, the probabilistic feature of single and entangled photon sources would also cause an exponentially increasing overhead for large-scale quantum network. To solve the above problems, quantum repeaters and/or transmission of optical quantum bits over free space channel can be efficiently exploited for future wide-area realization of quantum communication. In addition, memory built-in quantum repeaters would also enable scalable linear optical quantum computing, simulation and high precision measurement. In this talk I will present some recent experiments from our group, including eight-photon entanglement, topological quantum error-correction, quantum repeater and efficient and long-lived quantum memory, and entanglement distribution and quantum teleportation over 100km-scale free-space quantum channels. These experiments show the promising future possibility towards scalable quantum network with photons and atoms.

Ryutaro Nagai, Takao Tomono, Yuichiro Minato

Tensor network is known as a powerful method to simulate quantum computation on a classical computer. Recently, the random quantum circuit used in the demonstration of quantum supremacy by Sycamore quantum computer have been simulated with tensor network method. Compared to the application of tensor networks to Discrete-Variable (DV) quantum computation, there are very few examples of application of tensor network to Continuous-Variable (CV) quantum computation. Therefore, we applied a tensor network structure called Matrix Product States (MPS) to CV quantum computation. Tensor network is expected to be useful for simulating Gaussian Boson Sampling (GBS), which is a promised near-term application of photonic quantum system. We outline the performance of tensor network-based simulation of CV quantum computation in GBS.

Jacob F. F. Bulmer

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.

Rafael Pereira da Silva