Long + short talks accepted by AQIS 2023!

Papers on distillable entanglement & quantum capacity and quantum signal processing are selected for talks in AQIS 2023 🎉

Our papers Estimate Distillable Entanglement and Quantum Capacity by Squeezing Useless Entanglement by Chengkai Zhu, Chenghong Zhu and Xin Wang was selected for a long talk, and Quantum Phase Processing and its Applications in Estimating Phase and Entropies by Youle Wang, Lei Zhang, Zhan Yu and Xin Wang was selected for a short talk at AQIS 2023!

For AQIS 2023: The AQIS'23 is an international academic conference for quantum science. The AQIS'23 focuses on quantum information processing, communication and cryptography, an interdisciplinary field bridging quantum physics, computer science, mathematics, and information technologies.

For the paper Estimate Distillable Entanglement and Quantum Capacity by Squeezing Useless Entanglement: We propose methods for evaluating the distillable entanglement and the quantum capacity by squeezing out useless entanglement within a state or a quantum channel. We first consider a general resource measure called the reverse divergence of resources to quantify the minimum divergence between a target state and the set of free states. We then introduce the reverse max-relative entropy of entanglement and apply it to establish efficiently computable upper bounds on the distillable entanglement. We also extend the reverse divergence of resources to quantum channels and derive upper bounds on the quantum capacity. Our method has practical applications for purifying maximally entangled states under practical noises, such as depolarizing and amplitude damping noises, leading to improvements in estimating the one-way distillable entanglement. Our bounds also offer useful benchmarks for evaluating the quantum capacities of qubit quantum channels of interest, including the Pauli channels and the random mixed unitary channels.

For the paper Quantum Phase Processing and its Applications in Estimating Phase and Entropies: We develop a new algorithmic toolbox “Quantum phase processing” that can directly apply arbitrary trigonometric transformations to eigenphases of a unitary operator. The quantum phase processing circuit is constructed simply, consisting of single-qubit rotations and controlled-unitaries, typically using only one ancilla qubit. Besides the capability of phase transformation, quantum phase processing in particular can extract the eigen-information of quantum systems by simply measuring the ancilla qubit, making it naturally compatible with indirect measurement. Quantum phase processing complements a powerful framework known as quantum singular value transformation and leads to more intuitive and efficient quantum algorithms for solving problems that are particularly phase-related. As a notable application, we propose a new quantum phase estimation algorithm without quantum Fourier transform, which requires the fewest ancilla qubits and matches the best performance so far. We further exploit the power of our method by investigating a plethora of applications in Hamiltonian simulation, entanglement spectroscopy and quantum entropies estimation, demonstrating improvements or optimality for almost all cases.

Lei Zhang
Lei Zhang
PhD Student

I obtained my BMath in AMath, CO & joint PMath from the University of Waterloo. My research interests include quantum information theory and quantum machine learning.