Kwek, Leong Chuan

Quantum autoencoders serve as efficient means for quantum data compression. Here, we propose and demonstrate their use to reduce resource costs for quantum teleportation of subspaces in high-dimensional systems. We use a quantum autoencoder in a compress-teleport-decompress manner and report the first demonstration with qutrits using an integrated photonic platform for future scalability. The key strategy is to compress the dimensionality of input states by erasing redundant information and recover the initial states after chip-to-chip teleportation. Unsupervised machine learning is applied to train the on-chip autoencoder, enabling the compression and teleportation of any state from a high-dimensional subspace. Unknown states are decompressed at a high fidelity (~0.971), obtaining a total teleportation fidelity of ~0.894. Subspace encodings hold great potential as they support enhanced noise robustness and increased coherence. Laying the groundwork for machine learning techniques in quantum systems, our scheme opens previously unidentified paths toward high-dimensional quantum computing and networking.

Reducing noise in quantum systems is a significant challenge in advancing quantum technologies. We propose and demonstrate a noise reduction scheme utilizing a quantum autoencoder, which offers rigorous performance guarantees. The quantum autoencoder is trained to compress noisy quantum states into a latent subspace and eliminate noise through projective measurements. We identify various noise models in which the noiseless state can be perfectly reconstructed, even at high noise levels. We apply the autoencoder to cool thermal states to the ground state and reduce the cost of magic state distillation by several orders of magnitude. Our autoencoder can be implemented using only unitary transformations without the need for ancillas, making it immediately compatible with state-of-the-art quantum technologies. We experimentally validate our noise reduction methods in a photonic integrated circuit. Our results have direct applications in enhancing the robustness of quantum technologies against noise.

In the modern financial industry system, the structure of products has become more and more complex, and the bottleneck constraint of classical computing power has already restricted the development of the financial industry. Here, we present a photonic chip that implements the unary approach to European option pricing, in combination with the quantum amplitude estimation algorithm, to achieve quadratic speedup compared to classical Monte Carlo methods. The circuit consists of three modules: one loading the distribution of asset prices, one computing the expected payoff, and a third performing the quantum amplitude estimation algorithm to introduce speedups. In the distribution module, a generative adversarial network is embedded for efficient learning and loading of asset distributions, which precisely captures market trends. This work is a step forward in the development of specialized photonic processors for applications in finance, with the potential to improve the efficiency and quality of financial services.