arXiv subjects

Graham E. Rowlands

Publications and source records attributed to Graham E. Rowlands.

16 recordsLinked to original sources

arXiv2023

Overcoming the Coherence Time Barrier in Quantum Machine Learning on Temporal Data

Practical implementation of many quantum algorithms known today is limited by the coherence time of the executing quantum hardware and quantum sampling noise. Here we present a machine learning algorithm, NISQRC, for qubit-based quantum systems that enables inference on temporal data over durations unconstrained by decoherence. NISQRC leverages mid-circuit measurements and deterministic reset operations to reduce circuit executions, while still maintaining an appropriate length persistent temporal memory in quantum system, confirmed through the proposed Volterra Series analysis. This enables NISQRC to overcome not only limitations imposed by finite coherence, but also information scrambling in monitored circuits and sampling noise, problems that persist even in hypothetical fault-tolerant quantum computers that have yet to be realized. To validate our approach, we consider the channel equalization task to recover test signal symbols that are subject to a distorting channel. Through simulations and experiments on a 7-qubit quantum processor we demonstrate that NISQRC can recover arbitrarily long test signals, not limited by coherence time.

quant-ph↗

arXiv2023

Tackling Sampling Noise in Physical Systems for Machine Learning Applications: Fundamental Limits and Eigentasks

The expressive capacity of physical systems employed for learning is limited by the unavoidable presence of noise in their extracted outputs. Though present in physical systems across both the classical and quantum regimes, the precise impact of noise on learning remains poorly understood. Focusing on supervised learning, we present a mathematical framework for evaluating the resolvable expressive capacity (REC) of general physical systems under finite sampling noise, and provide a methodology for extracting its extrema, the eigentasks. Eigentasks are a native set of functions that a given physical system can approximate with minimal error. We show that the REC of a quantum system is limited by the fundamental theory of quantum measurement, and obtain a tight upper bound for the REC of any finitely-sampled physical system. We then provide empirical evidence that extracting low-noise eigentasks can lead to improved performance for machine learning tasks such as classification, displaying robustness to overfitting. We present analyses suggesting that correlations in the measured quantum system enhance learning capacity by reducing noise in eigentasks. The applicability of these results in practice is demonstrated with experiments on superconducting quantum processors. Our findings have broad implications for quantum machine learning and sensing applications.

quant-ph↗

arXiv2022

Quantifying the Expressive Capacity of Quantum Systems: Fundamental Limits and Eigentasks

The expressive capacity of quantum systems for machine learning is limited by quantum sampling noise incurred during measurement. Although it is generally believed that noise limits the resolvable capacity of quantum systems, the precise impact of noise on learning is not yet fully understood. We present a mathematical framework for evaluating the available expressive capacity of general quantum systems from a finite number of measurements, and provide a methodology for extracting the extrema of this capacity, its eigentasks. Eigentasks are a native set of functions that a given quantum system can approximate with minimal error. We show that extracting low-noise eigentasks leads to improved performance for machine learning tasks such as classification, displaying robustness to overfitting. We obtain a tight bound on the expressive capacity, and present analyses suggesting that correlations in the measured quantum system enhance learning capacity by reducing noise in eigentasks. These results are supported by experiments on superconducting quantum processors. Our findings have broad implications for quantum machine learning and sensing applications.

quant-ph↗

arXiv2021

Symmetry-Aware Reservoir Computing

We demonstrate that matching the symmetry properties of a reservoir computer (RC) to the data being processed dramatically increases its processing power. We apply our method to the parity task, a challenging benchmark problem that highlights inversion and permutation symmetries, and to a chaotic system inference task that presents an inversion symmetry rule. For the parity task, our symmetry-aware RC obtains zero error using an exponentially reduced neural network and training data, greatly speeding up the time to result and outperforming hand crafted artificial neural networks. When both symmetries are respected, we find that the network size $N$ necessary to obtain zero error for 50 different RC instances scales linearly with the parity-order $n$. Moreover, some symmetry-aware RC instances perform a zero error classification with only $N=1$ for $n\leq7$. Furthermore, we show that a symmetry-aware RC only needs a training data set with size on the order of $(n+n/2)$ to obtain such performance, an exponential reduction in comparison to a regular RC which requires a training data set with size on the order of $n2^n$ to contain all $2^n$ possible $n-$bit-long sequences. For the inference task, we show that a symmetry-aware RC presents a normalized root-mean-square error three orders-of-magnitude smaller than regular RCs. For both tasks, our RC approach respects the symmetries by adjusting only the input and the output layers, and not by problem-based modifications to the neural network. We anticipate that generalizations of our procedure can be applied in information processing for problems with known symmetries.

cs.NE↗

arXiv2021

Reservoir Computing with Superconducting Electronics

The rapidity and low power consumption of superconducting electronics makes them an ideal substrate for physical reservoir computing, which commandeers the computational power inherent to the evolution of a dynamical system for the purposes of performing machine learning tasks. We focus on a subset of superconducting circuits that exhibit soliton-like dynamics in simple transmission line geometries. With numerical simulations we demonstrate the effectiveness of these circuits in performing higher-order parity calculations and channel equalization at rates approaching 100 Gb/s. The availability of a proven superconducting logic scheme considerably simplifies the path to a fully integrated reservoir computing platform and makes superconducting reservoirs an enticing substrate for high rate signal processing applications.

cond-mat.supr-con↗

arXiv2019

Nanosecond Reversal of Three-Terminal Spin Hall Effect Memories Sustained at Cryogenic Temperatures

We characterize the nanosecond pulse switching performance of the three-terminal magnetic tunnel junctions (MTJs), driven by the spin Hall effect (SHE) in the channel, at a cryogenic temperature of 3 K. The SHE-MTJ devices exhibit reasonable magnetic switching and reliable current switching by as short pulses as 1 ns of $<10^{12}$ A/m$^{2}$ magnitude, exceeding the expectation from conventional macrospin model. The pulse switching bit error rates reach below $10^{-6}$ for < 10 ns pulses. Similar performance is achieved with exponentially decaying pulses expected to be delivered to the SHE-MTJ device by a nanocryotron device in parallel configuration of a realistic memory cell structure. These results suggest the viability of the SHE-MTJ structure as a cryogenic memory element for exascale superconducting computing systems.

physics.app-ph↗

arXiv2019

Cryogenic Memory Architecture Integrating Spin Hall Effect based Magnetic Memory and Superconductive Cryotron Devices

One of the most challenging obstacles to realizing exascale computing is minimizing the energy consumption of L2 cache, main memory, and interconnects to that memory. For promising cryogenic computing schemes utilizing Josephson junction superconducting logic, this obstacle is exacerbated by the cryogenic system requirements that expose the technology's lack of high-density, high-speed and power-efficient memory. Here we demonstrate an array of cryogenic memory cells consisting of a non-volatile three-terminal magnetic tunnel junction element driven by the spin Hall effect, combined with a superconducting heater-cryotron bit-select element. The write energy of these memory elements is roughly 8 pJ with a bit-select element, designed to achieve a minimum overhead power consumption of about 30%. Individual magnetic memory cells measured at 4 K show reliable switching with write error rates below $10^{-6}$, and a 4x4 array can be fully addressed with bit select error rates of $10^{-6}$. This demonstration is a first step towards a full cryogenic memory architecture targeting energy and performance specifications appropriate for applications in superconducting high performance and quantum computing control systems, which require significant memory resources operating at 4 K.

physics.app-ph↗

arXiv2019

Sub-nanosecond switching in a cryogenic spin-torque spin-valve memory element with a dilute permalloy free layer

We present a study of the pulsed current switching characteristics of spin-valve nanopillars with in-plane magnetized dilute permalloy and undiluted permalloy free layers in the ballistic regime at low temperature. The dilute permalloy free layer device switches much faster: the characteristic switching time for a permalloy free (Ni0.83Fe0.17) layer device is 1.18 ns, while that for a dilute permalloy ([Ni0.83Fe0.17]0.6Cu0.4) free layer device is 0.475 ns. A ballistic macrospin model can capture the data trends with a reduced spin torque asymmetry parameter, reduced spin polarization and increased Gilbert damping for the dilute permalloy free layer relative to the permalloy devices. Our study demonstrates that reducing the magnetization of the free layer increases the switching speed while greatly reducing the switching energy and shows a promising route toward even lower power magnetic memory devices compatible with superconducting electronics.

physics.app-ph↗

arXiv2018

Efficient Switching of 3-Terminal Magnetic Tunnel Junctions by the Giant Spin Hall Effect of $\rm{Pt}_{85}\rm{Hf}_{15}$ Alloy

Recent research has indicated that introducing impurities that increase the resistivity of Pt can enhance the efficiency of the spin Hall torque it generates. Here we directly demonstrate the usefulness of this strategy by fabricating prototype 3-terminal in-plane-magnetized magnetic tunnel junctions that utilize the spin Hall torque from a $\rm{Pt}_{85}\rm{Hf}_{15}$ alloy, and measuring the critical currents for switching. We find that $\rm{Pt}_{85}\rm{Hf}_{15}$ reduces the switching current densities compared to pure Pt by approximately a factor of 2 for both quasi-static ramped current biases and nanosecond-scale current pulses, thereby proving the feasibility of this approach to assist in the development of efficient embedded magnetic memory technologies.

cond-mat.mtrl-sci↗

arXiv2017

Coherent spin-transfer precession switching in orthogonal spin-torque devices

We present experimental results and macrospin simulations of the switching characteristics of orthogonal spin-transfer devices incorporating an out-of-plane magnetized polarizing layer and an in-plane magnetized spin valve device at cryogenic temperatures. At $T=4$ K we demonstrate: high speed deterministic switching at short pulse lengths - down to 100 ps - with sufficient measurement statistics to establish a switching error rate of $10^{-5}$; coherent precessional switching at longer times; and ensemble decoherence effects at even longer times. Finite temperature macrospin models capture the precessional switching well but fail to fully reproduce all the decoherence and switching error behavior.

cond-mat.mes-hall↗

arXiv2017

Nanosecond magnetization dynamics during spin Hall switching of in-plane magnetic tunnel junctions

We present a study of the magnetic dynamics associated with nanosecond scale magnetic switching driven by the spin Hall effect in 3-terminal nanoscale magnetic tunnel junctions (3T-MTJs) with in-plane magnetization. Utilizing fast pulse measurements in a variety of material stacks and detailed micromagnetic simulations, we demonstrate that this unexpectedly fast and reliable magnetic reversal is facilitated by the self-generated Oersted field, and the short-pulse energy efficiency can be substantially enhanced by micromagnetic curvature in the magnetic free layer. The sign of the Oersted field is essential for this enhancement --- in simulations in which we artificially impose a field-like torque with a sign opposite to the effect of the Oersted field, the result is a much slower and stochastic switching process that is reminiscent of the so-called incubation delay in conventional 2-terminal spin-torque-switched MTJs.

cond-mat.mes-hall↗

arXiv2016

Experimental Demonstration of Efficient Spin-Orbit Torque Switching of an MTJ with sub-100 ns Pulses

Efficient generation of spin currents from charge currents is of high importance for memory and logic applications of spintronics. In particular, generation of spin currents from charge currents in high spin-orbit coupling metals has the potential to provide a scalable solution for embedded memory. We demonstrate a net reduction in critical charge current for spin torque driven magnetization reversal via using spin-orbit mediated spin current generation. We scaled the dimensions of the spin-orbit electrode to 400 nm and the nanomagnet to 270 nm x 68 nm in a three terminal spin-orbit torque, magnetic tunnel junction (SOT-MTJ) geometry. Our estimated effective spin Hall angle is 0.15-0.20 using the ratio of zero temperature critical current from spin Hall switching and estimated spin current density for switching the magnet. We show bidirectional transient switching using spin-orbit generated spin torque at 100 ns switching pulses reliably followed by transient read operations. We finally compare the static and dynamic response of the SOT-MTJ with transient spin circuit modeling showing the performance of scaled SOT-MTJs to enable nanosecond class non-volatile MTJs.

cond-mat.mes-hall↗

arXiv2016

Nanosecond-timescale low error switching of in-plane magnetic tunnel junctions through dynamic Oersted-field assisted spin-Hall effect

We investigate fast-pulse switching of in-plane-magnetized magnetic tunnel junctions (MTJs) within 3-terminal devices in which spin-transfer torque is applied to the MTJ by the giant spin Hall effect. We measure reliable switching, with write error rates down to $10^{-5}$, using current pulses as short as just 2 ns in duration. This represents the fastest reliable switching reported to date for any spin-torque-driven magnetic memory geometry, and corresponds to a characteristic time scale that is significantly shorter than predicted possible within a macrospin model for in-plane MTJs subject to thermal fluctuations at room temperature. Using micromagnetic simulations, we show that in the 3-terminal spin-Hall devices the Oersted magnetic field generated by the pulse current strongly modifies the magnetic dynamics excited by the spin-Hall torque, enabling this unanticipated performance improvement. Our results suggest that in-plane MTJs controlled by Oersted-field-assisted spin-Hall torque are a promising candidate for both cache memory applications requiring high speed and for cryogenic memories requiring low write energies.

cond-mat.mes-hall↗

arXiv2015

A Critical Analysis of the Feasibility of Pure Strain-Actuated Giant Magnetostrictive Nanoscale Memories

Concepts for memories based on the manipulation of giant magnetostrictive nanomagnets by stress pulses have garnered recent attention due to their potential for ultra-low energy operation in the high storage density limit. Here we discuss the feasibility of making such memories in light of the fact that the Gilbert damping of such materials is typically quite high. We report the results of numerical simulations for several classes of toggle precessional and non-toggle dissipative magnetoelastic switching modes. Material candidates for each of the several classes are analyzed and forms for the anisotropy energy density and ranges of material parameters appropriate for each material class are employed. Our study indicates that the Gilbert damping as well as the anisotropy and demagnetization energies are all crucial for determining the feasibility of magnetoelastic toggle-mode precessional switching schemes. The roles of thermal stability and thermal fluctuations for stress-pulse switching of giant magnetostrictive nanomagnets are also discussed in detail and are shown to be important in the viability, design, and footprint of magnetostrictive switching schemes.

cond-mat.mtrl-sci↗

arXiv2013

Time Domain Mapping of Spin Torque Oscillator Effective Energy

Stochastic dynamics of spin torque oscillators (STOs) can be described in terms of magnetization drift and diffusion over a current-dependent effective energy surface given by the Fokker-Planck equation. Here we present a method that directly probes this effective energy surface via time-resolved measurements of the microwave voltage generated by a STO. We show that the effective energy approach provides a simple recipe for predicting spectral line widths and line shapes near the generation threshold. Our time domain technique also accurately measures the field-like component of spin torque in a wide range of the voltage bias values.

cond-mat.mes-hall↗

arXiv2012

Voltage-Induced Ferromagnetic Resonance in Magnetic Tunnel Junctions

We demonstrate excitation of ferromagnetic resonance in CoFeB/MgO/CoFeB magnetic tunnel junctions (MTJs) by the combined action of voltage-controlled magnetic anisotropy (VCMA) and spin transfer torque (ST). Our measurements reveal that GHz-frequency VCMA torque and ST in low-resistance MTJs have similar magnitudes, and thus that both torques are equally important for understanding high-frequency voltage-driven magnetization dynamics in MTJs. As an example, we show that VCMA can increase the sensitivity of an MTJ-based microwave signal detector to the sensitivity level of semiconductor Schottky diodes.

cond-mat.mes-hall↗