Yanglin Hu

Chen-Xun Weng, Ming-Yang Li, Nai-Rui Xu et al.

Zero-knowledge proofs (ZKPs) are widely applied in digital economies, such as cryptocurrencies and smart contracts, for establishing trust and privacy between untrusted parties. Classical ZKPs rely on computational assumptions and are vulnerable to quantum attacks. While a recent advance suggests quantum-sound symmetric relativistic ZKPs for the graph three-coloring problem without computational assumptions, the high round complexity, which leads to unachievable runtime and overall randomness cost, renders them impractical for real-life deployment. To overcome this, we develop an efficient asymmetric relativistic ZKP protocol using relativistic bit commitments, and prove its quantum soundness by relating it to the nonlocal Clauser-Horne-Shimony-Holt (CHSH) game. Our protocol achieves a linear relationship between the round complexity and the number of edges, and thus significantly improves practical feasibility. In addition, we implement a proof-of-principle experiment which completes all interactive rounds in about 0.22 seconds and requires an overall randomness cost of 430.81 MB. Our work illustrates the powerful potential of integrating special relativity with quantum theory in trustless cryptography, paving the way for robust applications against quantum attacks in distrustful Internet environments.

Josep Lumbreras, Ruo Cheng Huang, Yanglin Hu et al.

In reinforcement learning, an agent interacts sequentially with an environment to maximize a reward, receiving only partial, probabilistic feedback. This creates a fundamental exploration-exploitation trade-off: the agent must explore to learn the hidden dynamics while exploiting this knowledge to maximize its target objective. While extensively studied classically, applying this framework to quantum systems requires dealing with hidden quantum states that evolve via unknown dynamics. We formalize this problem via a framework where the environment maintains a hidden quantum memory evolving via unknown quantum channels, and the agent intervenes sequentially using quantum instruments. For this setting, we adapt an optimistic maximum-likelihood estimation algorithm. We extend the analysis to continuous action spaces, allowing us to model general positive operator-valued measures (POVMs). By controlling the propagation of estimation errors through quantum channels and instruments, we prove that the cumulative regret of our strategy scales as $\widetilde{\mathcal{O}}(\sqrt{K})$ over $K$ episodes. Furthermore, via a reduction to the multi-armed quantum bandit problem, we establish information-theoretic lower bounds demonstrating that this sublinear scaling is strictly optimal up to polylogarithmic factors. As a physical application, we consider state-agnostic work extraction. When extracting free energy from a sequence of non-i.i.d. quantum states correlated by a hidden memory, any lack of knowledge about the source leads to thermodynamic dissipation. In our setting, the mathematical regret exactly quantifies this cumulative dissipation. Using our adaptive algorithm, the agent uses past energy outcomes to improve its extraction protocol on the fly, achieving sublinear cumulative dissipation, and, consequently, an asymptotically zero dissipation rate.