Quantum computing is an information processing paradigm that uses quantum-mechanical properties to speedup computationally hard problems. Although promising, existing gate-based quantum computers consist of only a few dozen qubits and are not large enough for most applications. On the other hand, existing QAs with few thousand of qubits have the potential to solve some domain-specific optimization problems. QAs are single instruction machines and to execute a program, the problem is cast to a Hamiltonian, embedded on the hardware, and a single quantum machine instruction (QMI) is run. Unfortunately, noise and imperfections in hardware result in sub-optimal solutions on QAs even if the QMI is run for thousands of trials. The limited programmability of QAs mean that the user executes the same QMI for all trials. This subjects all trials to a similar noise profile throughout the execution, resulting in a systematic bias. We observe that systematic bias leads to sub-optimal solutions and cannot be alleviated by executing more trials or using existing error-mitigation schemes. To address this challenge, we propose EQUAL (Ensemble Quantum Annealing). EQUAL generates an ensemble of QMIs by adding controlled perturbations to the program QMI. When executed on the QA, the ensemble of QMIs steers the program away from encountering the same bias during all trials and thus, improves the quality of solutions. Our evaluations using the 2041-qubit D-Wave QA show that EQUAL bridges the difference between the baseline and the ideal by an average of 14% (and up to 26%), without requiring any additional trials. EQUAL can be combined with existing error mitigation schemes to further bridge the difference between the baseline and ideal by an average of 55% (and up to 68%).

中文翻译：

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EQUAL：通过注入受控扰动提高量子退火器的保真度

量子计算是一种信息处理范式，它使用量子力学特性来加速计算难题。尽管很有前景，但现有的基于门的量子计算机仅由几十个量子位组成，对于大多数应用来说不够大。另一方面，具有几千个量子位的现有 QA 有可能解决一些特定领域的优化问题。QA 是单指令机器，为了执行程序，问题被转换为哈密顿量，嵌入硬件，并运行单个量子机器指令 (QMI)。不幸的是，即使 QMI 运行了数千次试验，硬件中的噪声和缺陷也会导致 QA 的次优解决方案。QA 的有限可编程性意味着用户对所有试验执行相同的 QMI。这使所有试验在整个执行过程中都受到类似的噪声分布，从而导致系统偏差。我们观察到系统偏差会导致次优解决方案，并且无法通过执行更多试验或使用现有的错误缓解方案来缓解。为了应对这一挑战，我们提出了 EQUAL（集成量子退火）。EQUAL 通过向程序 QMI 添加受控扰动来生成 QMI 集合。在 QA 上执行时，QMI 的集合引导程序避免在所有试验中遇到相同的偏差，从而提高解决方案的质量。我们使用 2041 量子位 D-Wave QA 进行的评估表明，EQUAL 将基线和理想之间的差异平均缩小了 14%（最多 26%），而无需任何额外的试验。