提出可调控量子记忆容量的新机制，实现对量子储层网络记忆衰减的精准控制。
Controllable Quantum Memory Capacity in Quantum Reservoir Networks with Tunable partial-SWAPs
- 引入可调部分交换门，直接控制量子记忆的衰减速率。
- 在模拟与IBM QPU上验证，短时记忆容量与NARMA-5任务表现提升。
- 适合研究量子储层计算、可控量子记忆的科研人员参考。
在量子储层计算（QRC）领域，反馈模型与递归模型是两类主要架构。本文聚焦递归模型，其采用双寄存器结构赋予系统遗忘性记忆。尽管这类模型已在硬件上验证并展现良好性能，但记忆容量的形成机制尚不明确且不可控。为此，我们提出一种硬件可实现的可调部分交换（tunable partial-SWAP）机制，可在基于门模型的量子处理单元（QPU）上直接调控量子储层网络（QRN）的记忆衰减率。理论分析基于受控振幅阻尼通道，通过随机短时记忆容量（STMC）召回基准和NARMA-5数据集，在仿真与实际IBM QPUs上进行验证，结果表明该机制有效提升了记忆性能。
原文摘要 · Abstract (English)
In the field of quantum reservoir computing (QRC), many different computational models and architectures have been proposed. From these models, we identify feedback-based models -- which use a feedback mechanism to re-embed classical measurements from the QRC -- and recurrent models -- which use a multi-register approach with memory and readout qubits -- as the two major competing architectures that have been discussed and validated on hardware. In this paper, we advance upon the recurrent architectures, which employ a two register approach to endow the QRC with a fading memory. While these approaches have been validated on hardware and have demonstrated great real-world performance on noisy-intermediate-scale-quantum (NISQ) quantum processing units (QPUs), the exact mechanism through which the memory capacity arises is not completely understood or fully controllable. With this, we augment the recurrent approaches and present a hardware-realizable mechanism, which we call a tunable partial-SWAP, that allows for the direct control of the rate of memory dissipation from a QRN implemented on a gate-based QPU. The theory behind this mechanism is discussed in terms of a controlled amplitude-damping channel and validation experiments using a randomized short-term memory capacity (STMC) recall benchmark and the NARMA-5 dataset are conducted using simulation and IBM QPUs, respectively.
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