Abstract:Quantum reservoir computing provides a framework for exploiting the natural dynamics of quantum systems as a computational resource. It can implement real-time signal processing and solve temporal machine learning problems in general, which requires memory and nonlinear mapping of the recent input stream using the quantum dynamics in computational supremacy region, where the classical simulation of the system is intractable. A nuclear magnetic resonance spin-ensemble system is one of the realistic candidates f… Show more
“…In contrary, the random sampled and low k setups are able to encode the information to the other spins fast and the decoding is efficient, but the memory does not extend as far. We can further synthesize desired memory properties by spatially multiplexing distinct quantum reservoirs 24 . Figure 4 The OTOC between the input spin (site ) and the other spins for different couplings .…”
Quantum computing and neural networks show great promise for the future of information processing. In this paper we study a quantum reservoir computer (QRC), a framework harnessing quantum dynamics and designed for fast and efficient solving of temporal machine learning tasks such as speech recognition, time series prediction and natural language processing. Specifically, we study memory capacity and accuracy of a quantum reservoir computer based on the fully connected transverse field Ising model by investigating different forms of inter-spin interactions and computing timescales. We show that variation in inter-spin interactions leads to a better memory capacity in general, by engineering the type of interactions the capacity can be greatly enhanced and there exists an optimal timescale at which the capacity is maximized. To connect computational capabilities to physical properties of the underlaying system, we also study the out-of-time-ordered correlator and find that its faster decay implies a more accurate memory. Furthermore, as an example application on real world data, we use QRC to predict stock values.
“…In contrary, the random sampled and low k setups are able to encode the information to the other spins fast and the decoding is efficient, but the memory does not extend as far. We can further synthesize desired memory properties by spatially multiplexing distinct quantum reservoirs 24 . Figure 4 The OTOC between the input spin (site ) and the other spins for different couplings .…”
Quantum computing and neural networks show great promise for the future of information processing. In this paper we study a quantum reservoir computer (QRC), a framework harnessing quantum dynamics and designed for fast and efficient solving of temporal machine learning tasks such as speech recognition, time series prediction and natural language processing. Specifically, we study memory capacity and accuracy of a quantum reservoir computer based on the fully connected transverse field Ising model by investigating different forms of inter-spin interactions and computing timescales. We show that variation in inter-spin interactions leads to a better memory capacity in general, by engineering the type of interactions the capacity can be greatly enhanced and there exists an optimal timescale at which the capacity is maximized. To connect computational capabilities to physical properties of the underlaying system, we also study the out-of-time-ordered correlator and find that its faster decay implies a more accurate memory. Furthermore, as an example application on real world data, we use QRC to predict stock values.
“…Multiple MTJs are placed in parallel, and an identical pulse voltage is applied to all the MTJs. To construct nodes for RC, spatial multiplexing [37] is employed. The node vector x(T), is defined as a vector with MĂN elements, where M is the number of MTJs and N is the number of virtual nodes in an MTJ.…”
Section: B Figures-of-merit For Reservoir Computing Using Multiple Mtjsmentioning
The figures-of-merit for reservoir computing (RC), using spintronics devices called magnetic tunnel junctions (MTJs), are evaluated. RC is a type of recurrent neural network. The input information is stored in certain parts of the reservoir, and computation can be performed by optimizing a linear transform matrix for the output. While all the network characteristics should be controlled in a general recurrent neural network, such optimization is not necessary for RC. The reservoir only has to possess a non-linear response with memory effect. In this paper, macromagnetic simulation is conducted for the spin-dynamics in MTJs, for reservoir computing. It is determined that the MTJ-system possesses the memory effect and non-linearity required for RC. With RC using 5-7MTJs, high performance can be obtained, similar to an echo-state network with 20-30 nodes, even if there are no magnetic and/or electrical interactions between the magnetizations.
I. INTRODUCTIONThe magnetization direction of ferromagnetic metallic film is determined by the magnetic anisotropy energy, which causes non-volatility. This property can be used for magnetic random-access memory devices [1]. In magnetic tunnel junction (MTJ) devices consisting of ferromagnetic and dielectric thin films, the magnetization direction in the ferromagnet can be detected by the change in device resistance originating from the tunneling magnetoresistance (TMR) effect [2][3][4][5]. Moreover, the magnetization direction can be electrically controlled by the spin-torque [6-9]. Therefore, MTJ devices are suitable for constructing non-volatile high-density memory devices. In addition to such long-term memory effect, the magnetization precessional dynamics appear to possess short-term memory effect with non-linear behavior. Such additional magnetization dynamics properties may be suitable for computation using MTJ devices.The recurrent neural network (RNN) [10, 11] is a machine learning method. It is a mathematical model, which emulates the nerve system in human brain. The RNN concept is depicted in Fig. 1(a).The model consists of three layers, input, middle (node), and output. In the RNN, the information of the middle layer recursively propagates in itself. The middle-layer state is determined by the present input and past middle-layer state, i.e., the middle layer in the RNN possesses memory effect. All the weight matrices for the input (Win), middle (W) and output (Wout) should be precisely trained to obtain the desired output. However, when the middle layer has sufficient memory effect and non-linearity, it is feasible to perform computation by optimizing only the output matrix (Wout). This type of simple RNN is called reservoir computing (RC) [12][13][14]. In RC, as system training is simple, it
“…Previous works have focused on the link between performance and the Lyapunov Spectrum [10] and comparison of different node types [11]. Extensions of the reservoir computing have also been proposed: both the use of plasticity [12] of links in the artificial neural network, as well as deterministically constructing networks [13] or spatial multiplexing [14] try to boost the performance. However, most of these theoretical investigations have focused on the more machine-learning inspired time-discrete artificial neural networks, as opposed to photonic and time-continuous systems.…”
The reservoir computing scheme is a machine learning mechanism which utilizes the naturally occurring computational capabilities of dynamical systems. One important subset of systems that has proven powerful both in experiments and theory are delay-systems. In this work, we investigate the reservoir computing performance of hybrid network-delay systems systematically by evaluating the NARMA10 and the Sante Fe task for varying system parameters. We construct 'multiplexed networks' that can be seen as intermediate steps on the scale from classical networks to the 'virtual networks' of delay systems. We find that the delay approach can be extended to the network case without loss of computational power, enabling the construction of faster reservoir computing systems.
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