Demonstration of Adiabatic Variational Quantum Computing with a Superconducting Quantum Coprocessor

Adiabatic quantum computing enables the preparation of many-body ground states. This is key for applications in chemistry, materials science, and beyond. Realisation poses major experimental challenges: Direct analog implementation requires complex Hamiltonian engineering, while the digitised version needs deep quantum gate circuits. To bypass these obstacles, we suggest an adiabatic variational hybrid algorithm, which employs short quantum circuits and provides a systematic quantum adiabatic optimisation of the circuit parameters. The quantum adiabatic theorem promises not only the ground state but also that the excited eigenstates can be found. We report the first experimental demonstration that many-body eigenstates can be efficiently prepared by an adiabatic variational algorithm assisted with a multi-qubit superconducting coprocessor. We track the real-time evolution of the ground and exited states of transverse-field Ising spins with a fidelity up that can reach about 99%.Comment: 12 pages, 4 figure

THREE ESSAYS ON THE HIGH-SPEED RAIL NETWORK IN CHINA

My dissertation consists of three essays that study the economic consequences of China’s high-speed rail (HSR) expansion. In the first essay, I use the college admission cutoff scores to reveal students’ college preferences under the enrollment quota. By exploiting the quasi-experimental variation in whether or not college cities are connected by the HSR network, I document a two-point increase in the cutoff scores following a HSR station opening in the college city using difference-in-difference (DD) approach. Colleges in the megacities experience a larger increase in cutoff scores after the station opening. These findings suggest that the HSR network stimulates “brain drain” from unconnected cities to connected cities, especially connected megacities. The second essay examines the impact of better HSR accessibility on housing prices in Jiangsu Province. Using transaction data of new houses aggregated to the complex level, I compare the housing prices of properties close to the new HSR stations to those close to pre-existing HSR stations, before and after the new station openings. In a DD specification, I document that housing prices decrease by twenty percent in the areas where the station distance reduces due to the station opening outside the city. The third essay investigates the impacts on household income. Using DD approach, I document that urban households experience a significant increase in total household income following the opening of HSR station in their city. While labor earnings increase, the probability of having business income decreases. Moreover, labor income of the households whose heads work in the manufacturing sector increases little, but for households whose heads work in the transport or communications sectors increases much more than other households, suggesting that the HSR network facilitates urban industry specialization

近現代日本におけるアップサイクルの理念と実践

京都精華大学博士（芸術）2023年度doctoral thesi

When Do LLMs Need Retrieval Augmentation? Mitigating LLMs' Overconfidence Helps Retrieval Augmentation

Large Language Models (LLMs) have been found to have difficulty knowing they do not possess certain knowledge and tend to provide specious answers in such cases. Retrieval Augmentation (RA) has been extensively studied to mitigate LLMs' hallucinations. However, due to the extra overhead and unassured quality of retrieval, it may not be optimal to conduct RA all the time. A straightforward idea is to only conduct retrieval when LLMs are uncertain about a question. This motivates us to enhance the LLMs' ability to perceive their knowledge boundaries to help RA. In this paper, we first quantitatively measure LLMs' such ability and confirm their overconfidence. Then, we study how LLMs' certainty about a question correlates with their dependence on external retrieved information. We propose several methods to enhance LLMs' perception of knowledge boundaries and show that they are effective in reducing overconfidence. Additionally, equipped with these methods, LLMs can achieve comparable or even better performance of RA with much fewer retrieval calls