The intersection between quantum computing and optimisation has been an area
of interest in recent years. There have been numerous studies exploring the
application of quantum and quantum-hybrid solvers to various optimisation
problems. This work explores scalarisation methods within the context of
solving the bi-objective quadratic assignment problem using a quantum-hybrid
solver. We show results that are consistent with previous research on a
different Ising machine.

本研究探讨了在使用混合量子求解器解决双目标二次分配问题的情境下的标量化方法，并展示出与先前研究在不同的 Ising 机器上一致的结果。

利用量子混合求解器解决双目的二次分配问题

Utilising a Quantum Hybrid Solver for Bi-objective Quadratic Assignment  Problems

Real-world recommender systems often need to balance multiple objectives when
deciding which recommendations to present to users. These include behavioural
signals (e.g. clicks, shares, dwell time), as well as broader objectives (e.g.
diversity, fairness). Scalarisation methods are commonly used to handle this
balancing task, where a weighted average of per-objective reward signals
determines the final score used for ranking. Naturally, how these weights are
computed exactly, is key to success for any online platform. We frame this as a
decision-making task, where the scalarisation weights are actions taken to
maximise an overall North Star reward (e.g. long-term user retention or
growth). We extend existing policy learning methods to the continuous
multivariate action domain, proposing to maximise a pessimistic lower bound on
the North Star reward that the learnt policy will yield. Typical lower bounds
based on normal approximations suffer from insufficient coverage, and we
propose an efficient and effective policy-dependent correction for this. We
provide guidance to design stochastic data collection policies, as well as
highly sensitive reward signals. Empirical observations from simulations,
offline and online experiments highlight the efficacy of our deployed approach.

多目标权衡时，推荐系统中的标量化方法决定最终用于排序的加权平均奖励信号，本研究对该方法进行了扩展以优化长期用户保留或增长的综合奖励，使用连续多元动作空间的策略学习方法，并提出了修正措施，通过模拟、离线和在线实验验证了方法的有效性。