arXiv subjects

Yaozu Wu

Publications and source records attributed to Yaozu Wu.

40 records · Page 3Linked to original sources

arXiv2021

Quantify the Non-Markovian Process with Intervening Projections in a Superconducting Processor

A Markov assumption considers a physical system memoryless to simplify its dynamics. Whereas memory effect or the non-Markovian phenomenon is more general in nature. In the quantum regime, it is challenging to define or quantify the non-Markovianity because the measurement of a quantum system often interferes with it. We simulate the open quantum dynamics in a superconducting processor, then characterize and quantify the non-Markovian process. With the complete set of intervening projections and the final measurement of the qubit, a restricted process tensor can be determined to account for the qubit-environment interaction. We apply the process tensor to predict the quantum state with memory effect, yielding an average fidelity of $99.86\%\pm 1.1\unicode{x2030}$. We further derive the Choi state of the rest process conditioned on history operations and quantify the non-Markovianity with a clear operational interpretation.

quant-ph↗

arXiv2021

Experimental Determination of Electronic States via Digitized Shortcut-to-Adiabaticity and Sequential Digitized Adiabaticity

A combination of the digitized shortcut-to-adiabaticity (STA) and the sequential digitized adiabaticity is implemented in a superconducting quantum device to determine electronic states in two example systems, the H2 molecule and the topological Bernevig-Hughes-Zhang (BHZ) model. For H2, a short internuclear distance is chosen as a starting point, at which the ground and excited states are obtained via the digitized STA. From this starting point, a sequence of internuclear distances is built. The eigenstates at each distance are sequentially determined from those at the previous distance via the digitized adiabaticity, leading to the potential energy landscapes of H2. The same approach is applied to the BHZ model, and the valence and conduction bands are excellently obtained along the X-Γ-X linecut of the first Brillouin zone. Furthermore, a numerical simulation of this method is performed to successfully extract the ground states of hydrogen chains with the lengths of 3 to 6 atoms.

quant-ph↗

arXiv2021

Optimization of Controlled-Z Gate with Data-Driven Gradient Ascent Pulse Engineering in a Superconducting Qubit System

The experimental optimization of a two-qubit controlled-Z (CZ) gate is realized following two different data-driven gradient ascent pulse engineering (GRAPE) protocols in the aim of optimizing the gate operator and the output quantum state, respectively. For both GRAPE protocols, the key computation of gradients utilizes mixed information of the input Z-control pulse and the experimental measurement. With an imperfect initial pulse in a flattop waveform, our experimental implementation shows that the CZ gate is quickly improved and the gate fidelities subject to the two optimized pulses are around 99%. Our experimental study confirms the applicability of the data-driven GRAPE protocols in the problem of the gate optimization.

quant-ph↗

arXiv2021

A Literature Review of Recent Graph Embedding Techniques for Biomedical Data

With the rapid development of biomedical software and hardware, a large amount of relational data interlinking genes, proteins, chemical components, drugs, diseases, and symptoms has been collected for modern biomedical research. Many graph-based learning methods have been proposed to analyze such type of data, giving a deeper insight into the topology and knowledge behind the biomedical data, which greatly benefit to both academic research and industrial application for human healthcare. However, the main difficulty is how to handle high dimensionality and sparsity of the biomedical graphs. Recently, graph embedding methods provide an effective and efficient way to address the above issues. It converts graph-based data into a low dimensional vector space where the graph structural properties and knowledge information are well preserved. In this survey, we conduct a literature review of recent developments and trends in applying graph embedding methods for biomedical data. We also introduce important applications and tasks in the biomedical domain as well as associated public biomedical datasets.

cs.AI↗