Integer factorization using stochastic magnetic tunnel junctions
Integer factorization using stochastic magnetic tunnel junctions
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DOI：
10.1038/s41586-019-1557-9
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发表时间：
2019-09-19
期刊：
影响因子：
64.8
通讯作者：
Datta, Supriyo
中科院分区：
文献类型：
--
作者：
Borders, William A.;Pervaiz, Ahmed Z.;Datta, Supriyo
传统的计算机使用一串被称为“位”的0和1来确定地操作，以二进制代码表示信息。尽管传统计算机已发展成为复杂的机器，但仍有许多类型的问题无法有效地解决，包括推理、可逆逻辑、采样和优化，这导致了对替代计算方案的相当大的兴趣。量子计算使用量子位表示0和1的叠加，有望有效地执行这些任务（1-3）。然而，退相干和目前对低温操作的要求(4)，以及可以实现的有限的多体相互作用，构成了相当大的挑战。概率计算（1,5-7）是另一种非常规的计算方案，它与量子计算具有相似的概念，但不受上述挑战的限制。关键作用是由一个概率位（p位）扮演的——一个在0和1之间波动的鲁棒的经典实体，它使用受神经网络启发的原理与同一系统中的其他p位相互作用(8)。在这里，我们提出了一个使用自旋电子学技术进行概率计算的概念验证实验，并演示了整数分解，这是绝热(9)和门控(2)量子计算解决的优化类问题的一个说明性示例。显示随机行为的纳米级磁隧道结是通过修改市场上现成的磁阻随机存取存储器技术而开发的（10,11），并用于实现在室温下工作的三端p位。p位通过电连接形成一个功能异步网络，其中应用了一种改进的绝热量子计算算法，实现了三体和四体相互作用。使用这个基本的异步概率计算机，使用8个相关的p位，演示了高达945的整数的因数分解，结果显示与理论预测很好地一致，从而为优化和采样的难题提供了一个潜在的可扩展的硬件方法。
Conventional computers operate deterministically using strings of zeros and ones called bits to represent information in binary code. Despite the evolution of conventional computers into sophisticated machines, there are many classes of problems that they cannot efficiently address, including inference, invertible logic, sampling and optimization, leading to considerable interest in alternative computing schemes. Quantum computing, which uses qubits to represent a superposition of 0 and 1, is expected to perform these tasks efficiently(1-3). However, decoherence and the current requirement for cryogenic operation(4), as well as the limited many-body interactions that can be implemented, pose considerable challenges. Probabilistic computing(1,5-7) is another unconventional computation scheme that shares similar concepts with quantum computing but is not limited by the above challenges. The key role is played by a probabilistic bit (a p-bit)-a robust, classical entity fluctuating in time between 0 and 1, which interacts with other p-bits in the same system using principles inspired by neural networks(8). Here we present a proof-of-concept experiment for probabilistic computing using spintronics technology, and demonstrate integer factorization, an illustrative example of the optimization class of problems addressed by adiabatic(9) and gated(2) quantum computing. Nanoscale magnetic tunnel junctions showing stochastic behaviour are developed by modifying market-ready magnetoresistive random-access memory technology(10,11) and are used to implement three-terminal p-bits that operate at room temperature. The p-bits are electrically connected to form a functional asynchronous network, to which a modified adiabatic quantum computing algorithm that implements three- and four-body interactions is applied. Factorization of integers up to 945 is demonstrated with this rudimentary asynchronous probabilistic computer using eight correlated p-bits, and the results show good agreement with theoretical predictions, thus providing a potentially scalable hardware approach to the difficult problems of optimization and sampling.