Quantum computers are expected to revolutionize our ability to process information. The advancement from classical to quantum computing is a product of our advancement from classical to quantum physics -- the more our understanding of the universe grows, so does our ability to use it for computation. A natural question that arises is, what will physics allow in the future? Can more advanced theories of physics increase our computational power, beyond quantum computing? An active field of research in physics studies theoretical phenomena outside the scope of explainable quantum mechanics, that form when attempting to combine Quantum Mechanics (QM) with General Relativity (GR) into a unified theory of Quantum Gravity (QG). QG is known to present the possibility of a quantum superposition of causal structure and event orderings. In the literature of quantum information theory, this translates to a superposition of unitary evolution orders. In this work we show a first example of a natural computational model based on QG, that provides an exponential speedup over standard quantum computation (under standard hardness assumptions). We define a model and complexity measure for a quantum computer that has the ability to generate a superposition of unitary evolution orders, and show that such computer is able to solve in polynomial time two of the fundamental problems in computer science: The Graph Isomorphism Problem ($\mathsf{GI}$) and the Gap Closest Vector Problem ($\mathsf{GapCVP}$), with gap $O\left( n^{2} \right)$. These problems are believed by experts to be hard to solve for a regular quantum computer. Interestingly, our model does not seem overpowered, and we found no obvious way to solve entire complexity classes that are considered hard in computer science, like the classes $\mathbf{NP}$ and $\mathbf{SZK}$.

翻译：量子计算机有望革命性地提升信息处理能力。从经典计算到量子计算的进步，源于从经典物理到量子物理的认知跃迁——我们对宇宙的理解越深入，利用它进行计算的能力就越强。由此自然引发一个问题：未来物理学将允许什么？更先进的物理理论能否超越量子计算，进一步提升我们的计算能力？物理学中一个活跃的研究领域是探索在试图将量子力学与广义相对论统一为量子引力理论时产生的、超出可解释量子力学范畴的理论现象。已知量子引力可能呈现因果结构和事件顺序的量子叠加。在量子信息论文献中，这对应演化顺序的量子叠加。本文首次展示了一个基于量子引力的自然计算模型实例，该模型在标准硬度假设下相比标准量子计算提供了指数级加速。我们定义了一种能够生成演化顺序量子叠加的量子计算机的模型与复杂度度量，并证明此类计算机可在多项式时间内解决计算机科学中的两个基本问题：图同构问题（$\mathsf{GI}$）和间隙最近向量问题（$\mathsf{GapCVP}$），间隙为$O\left( n^{2} \right)$。专家普遍认为这些问题对常规量子计算机难以求解。有趣的是，我们的模型并未显得过于强大，且我们未发现明显方法能解决计算机科学中公认困难的完整复杂度类（如$\mathbf{NP}$和$\mathbf{SZK}$）。