基於多模態擴散模型的離散-連續量子電路合成

摘要

高效編譯量子操作仍是擴展量子計算能力的主要瓶頸。當今最先進的方法通過結合搜索算法與基於梯度的參數優化來實現低編譯誤差，但這些方法耗時長且需要多次調用量子硬件或昂貴的經典模擬，使其擴展性受到限制。近年來，機器學習模型作為替代方案出現，儘管目前它們僅限於離散門集。本文介紹了一種多模態去噪擴散模型，該模型能同時生成電路結構及其連續參數，以編譯目標酉矩陣。它利用兩個獨立的擴散過程，一個用於離散門選擇，另一個用於參數預測。我們在不同實驗中對該模型進行基準測試，分析該方法在不同量子比特數量、電路深度及參數化門比例下的準確性。最後，通過利用其快速生成電路的特性，我們為特定操作創建了大型電路數據集，並利用這些數據集提取有價值的啟發式信息，以幫助我們發現量子電路合成的新見解。

English

Efficiently compiling quantum operations remains a major bottleneck in scaling quantum computing. Today's state-of-the-art methods achieve low compilation error by combining search algorithms with gradient-based parameter optimization, but they incur long runtimes and require multiple calls to quantum hardware or expensive classical simulations, making their scaling prohibitive. Recently, machine-learning models have emerged as an alternative, though they are currently restricted to discrete gate sets. Here, we introduce a multimodal denoising diffusion model that simultaneously generates a circuit's structure and its continuous parameters for compiling a target unitary. It leverages two independent diffusion processes, one for discrete gate selection and one for parameter prediction. We benchmark the model over different experiments, analyzing the method's accuracy across varying qubit counts, circuit depths, and proportions of parameterized gates. Finally, by exploiting its rapid circuit generation, we create large datasets of circuits for particular operations and use these to extract valuable heuristics that can help us discover new insights into quantum circuit synthesis.

基於多模態擴散模型的離散-連續量子電路合成

Synthesis of discrete-continuous quantum circuits with multimodal diffusion models

摘要

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