Quantum-enhanced reconfigurable in-memory stochastic computing
Hong-Zhe Yang, Jian-Peng Dou, Feng Lu, Xiao-Wen Shang, Chao-Ni Zhang, Heng Zhou, Hao Tang, Xian-Min Jin

TL;DR
This paper introduces a quantum-enhanced in-memory computing system that uses correlated photons for secure and efficient computation.
Contribution
The novel contribution is a reconfigurable quantum-enhanced in-memory computing system using room-temperature quantum memory and correlated photons.
Findings
Addition and multiplication operations are achieved by accumulating photon counts in the quantum memory.
Multiple computing tasks can be accelerated through parallel accumulation of photon counts.
The system ensures secure remote computation due to the stochastic nature of the process.
Abstract
In-memory computing, which enables computation directly within memory, represents an efficient approach to processing massively parallel computation tasks that are intractable for conventional computers. However, implementations of in-memory computing have been primarily limited to the classical regime, with its nonclassical counterpart yet to be fully explored. Quantum memory, with its unique capability to generate, preserve, and nontrivially operate on quantum states, offers spectacular quantum-enhanced advantages and is thus a promising candidate for in-memory computing. Here, leveraging a room-temperature quantum memory, we demonstrate a quantum-enhanced and reconfigurable in-memory stochastic computing system, where correlated photons, randomly produced in the quantum memory, serve as the computing resources. We show that addition and multiplication operations can be…
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Taxonomy
TopicsNeural Networks and Reservoir Computing · Quantum Computing Algorithms and Architecture · Advanced Memory and Neural Computing
