Yuan Liu

Min Chen, Yu Gan, Xin Jin et al.

Artificial intelligence (AI) and quantum information (QI) are rapidly co-evolving. AI is becoming a practical tool for learning, designing, controlling, and verifying quantum systems, while QI offers new computational models, representational structures, and learning-theoretic questions for AI. This survey reviews the interface from both directions. In the AI for QI direction, we organize recent progress around the central tasks of extracting information from limited measurements, training and discovering quantum algorithms, stabilizing noisy hardware, automating experimental and programming workflows, and extending learning-based methods to sensing and networking. In the QI for AI direction, we examine how quantum computation and quantum-inspired structures affect learning through algorithmic speedups, expressivity, trainability, generalization, neural-network design, and tensor-network representations. We close by identifying cross-cutting challenges in reproducibility, scalability, hardware realism, and co-design, arguing that progress will depend on tighter integration of theory, experiment, and hybrid quantum--classical systems.

Ria Patel, Masoud Hakimi Heris, Yuan Liu et al.

Standard gate-level transpilation introduces significant physical noise and overhead for high-precision quantum algorithms, such as the Quantum Singular Value Transformation (QSVT), on near-term trapped-ion hardware. Current compilers treat quantum operations as discrete units, forcing the physical control layer to execute highly fragmented laser pulses. To address this hardware-software disconnect, this work introduces a holistic pulse synthesis strategy that bypasses discrete gate-stitching to compile algorithms directly into continuous compound pulse gadgets. As a proof-of-concept, we target Hamiltonian simulation of the $H_2$ molecule, block-encoding the problem into a QSVT circuit to approximate the time-evolution operator $U = e^{-i H t}$ across 3 computational ions (2 system, 1 ancilla). We utilize the Gradient Ascent Pulse Engineering (GRAPE) algorithm to generate these compound gadgets and evaluate our methodology using noisy Lindblad master equation simulations. Preliminary observations indicate that the proposed strategy achieves significant temporal compression, reducing the total pulse schedule duration compared to standard compilers. Furthermore, synthesizing operations holistically eliminates the control-layer latency associated with discrete pulse lookup overhead. By streamlining the physical control schedule, this methodology offers a promising pathway to execute operations faster, highlighting the potential for compound gadgets to increase the computational depth achievable within fundamental $T_2$ decoherence limits.

Yuan Liu, Yakun Wang, Bin Wu

Direct reciprocity, based on the repeated interactions, is a fundamental mechanism to promote cooperation. Zero-determinant (ZD) strategies have opened an avenue for unilateral payoff control. However, previous studies neglect internal costs provided what agents do differ from what agents think, which is crucial for decision making of intelligent agents. Motivated by this, we establish a game theoretical framework by assuming that an individual pays the internal cost if the behavior is inconsistent with the internal thought. We prove that ZD strategy does not exist if the cost via behavior-value inconsistency is present. Instead, we find a new class of repeated strategies that enforce a unilateral payoff control, which is termed as positive/negative determinant strategy. The found strategy allows an individual to enforce an affine combination of two individuals' average payoffs above/below zero. Consequently, a focal individual is able to unilaterally control the opponent's payoff below a given value via negative determinant strategy, and a focal individual is able to get more payoff than the opponent via positive determinant strategy. We also find that the control ability of positive/negative determinant strategies is better off than that of ZD strategies. Our work highlights the importance of inconsistency between the behavior and value on payoff control, which is typically absent in classic ZD strategies.