Paper accepted by TQC 2025 and AQIS 2025!

Paper on quantum computing was selected for a talk in TQC 2025 and AQIS 2025 🎉

Our paper Quantum Algorithm for Reversing Unknown Unitary Evolutions by Yu-Ao Chen, Yin Mo, Yingjian Liu, Lei Zhang, and Xin Wang was selected for a talk in TQC 2025 and a long talk in AQIS 2025!

For TQC 2025: The Theory of Quantum Computation, Communication and Cryptography (TQC) is a leading annual international conference for students and researchers working in the theoretical aspects of quantum information science. The scientific objective is to bring together the theoretical quantum information science community to present and discuss the latest advances in the field.

For AQIS 2025: The AQIS'25 is an international academic conference for quantum science. The AQIS'25 focuses on quantum information processing, communication and cryptography, an interdisciplinary field bridging quantum physics, computer science, mathematics, and information technologies.

For the paper Quantum Algorithm for Reversing Unknown Unitary Evolutions: Reversing an unknown quantum evolution is of central importance to quantum information processing and fundamental physics, yet it remains a formidable challenge as conventional methods necessitate an infinite number of queries to fully characterize the quantum process. Here we introduce the Quantum Unitary Reversal Algorithm (QURA), a deterministic and exact approach to universally reverse arbitrary unknown unitary transformations using $\mathcal{O}(d^2)$ calls of the unitary, where $d$ is the system dimension. Our quantum algorithm resolves a fundamental problem of time-reversal simulations for closed quantum systems by confirming the feasibility of reversing any unitary evolution without knowing the exact process. The algorithm also provides the construction of a key oracle for unitary inversion in many quantum algorithm frameworks, such as quantum singular value transformation. It notably reveals a sharp boundary between the quantum and classical computing realms and unveils a quadratic quantum advantage in computational complexity for this foundational task.