The 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 the 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.

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.

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.

The nascent computational paradigm of quantum reservoir computing presents an attractive use of near-term, noisy-intermediate-scale quantum processors. To understand the potential power and use cases of quantum reservoir computing, it is necessary to define a conceptual framework to separate its constituent components and determine their impacts on performance. In this manuscript, we utilize such a framework to isolate the input encoding component of contemporary quantum reservoir computing schemes. We find that across the majority of schemes the input encoding implements a nonlinear transformation on the input data. As nonlinearity is known to be a key computational resource in reservoir computing, this calls into question the necessity and function of further, post-input, processing. Our findings will impact the design of future quantum reservoirs, as well as the interpretation of results and fair comparison between proposed designs.

Neuromorphic computing is a broad field that uses biological inspiration to address computing design. It is being pursued in many hardware technologies, both novel and conventional. We discuss the use of superconductive electronics for neuromorphic computing and why they are a compelling technology for the design of neuromorphic computing systems. One example is the natural spiking behavior of Josephson junctions and the ability to transmit short voltage spikes without the resistive capacitive time constants that typically hinder spike-based computing. We review the work that has been done on biologically inspired superconductive devices, circuits, and architectures and discuss the scaling potential of these demonstrations.

Accelerating computation with quantum resources is limited by the challenges of high-fidelity control of quantum systems. Reservoir computing presents an attractive alternative, as precise control and full calibration of system dynamics are not required. Instead, complex internal trajectories in a large state space are leveraged as a computational resource. Quantum systems offer a unique venue for reservoir computing, given the presence of interactions unavailable in classical systems and a potentially exponentially-larger computational space. With a reservoir comprised of a single d-dimensional quantum system, we demonstrate clear performance improvement with Hilbert space dimension at two benchmark tasks and advantage over the physically analogous classical reservoir. Quantum reservoirs as realized by current-era quantum hardware offer immediate practical implementation and a promising outlook for increased performance in larger systems.

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.

Realizing the promise of quantum information processing remains a daunting task given the omnipresence of noise and error. Adapting noise-resilient classical computing modalities to quantum mechanics may be a viable path towards near-term applications in the noisy intermediate-scale quantum era. Here, we propose continuous variable quantum reservoir computing in a single nonlinear oscillator. Through numerical simulation of our model we demonstrate quantum-classical performance improvement and identify its likely source: the nonlinearity of quantum measurement. Beyond quantum reservoir computing, this result may impact the interpretation of results across quantum machine learning. We study how the performance of our quantum reservoir depends on Hilbert space dimension, how it is impacted by injected noise, and briefly comment on its experimental implementation. Our results show that quantum reservoir computing in a single nonlinear oscillator is an attractive modality for quantum computing on near-term hardware.

Accelerating computational tasks with quantum resources is a widely-pursued goal that is presently limited by the challenges associated with high-fidelity control of many-body quantum systems. The paradigm of reservoir computing presents an attractive alternative, especially in the noisy intermediate-scale quantum era, since control over the internal system state and knowledge of its dynamics are not required. Instead, complex, unsupervised internal trajectories through a large state space are leveraged as a computational resource. Quantum systems offer a unique venue for reservoir computing, given the presence of interactions unavailable in analogous classical systems, and the potential for a computational space that grows exponentially with physical system size. Here, we consider a reservoir comprised of a single qudit ($d$-dimensional quantum system). We demonstrate a robust performance advantage compared to an analogous classical system accompanied by a clear improvement with Hilbert space dimension for two benchmark tasks: signal processing and short-term memory capacity. Qudit reservoirs are directly realized by current-era quantum hardware, offering immediate practical implementation, and a promising outlook for increased performance in larger systems.

The high energy consumption of high-performance computers (HPCs) based on current CMOS technology has become a serious problem when scaling up to exascale performance [1] . Superconducting (SC) computers provide a viable solution for fast and energy-efficient computing: their estimated power consumption is about 2 orders of magnitude lower than CMOS-based supercomputers, even when including the cooling power (see Fig. 1 ). Furthermore, scalable SC quantum computers will eventually require (classical) cryogenic, and preferably SC, processors and memories for qubit control and fast signal processing. However, the development of SC computers has faced several challenges among which is the lack of fast, power-efficient and scalable cryogenic memories. These memories have to be integrated with SC circuits whose supply power is typically very small.

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 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≤7. 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 a 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 the generalizations of our procedure can be applied in information processing for problems with known symmetries.

Alzheimer’s disease (AD) is a disorder associated with progressive degeneration of memory and cognitive function. Galantamine is a licenced treatment for AD but supplies of the plant alkaloid that it is produced from, galanthamine, are limited. This three-year system study tested the potential to combine Narcissus-derived galanthamine production with grassland-based ruminant production. Replicate plots of permanent pasture were prepared with and without bulbs of Narcissus pseudonarcissus sown as lines into the sward. Two different fertiliser regimes were imposed. The above-ground green biomass of N. pseudonarcissus was harvested in early spring and the galanthamine yield determined. In the second harvest year a split-plot design was implemented with lines of N. pseudonarcissus cut annually and biennially. All plots were subsequently grazed by ewes and lambs and animal performance recorded. Incorporation of N. pseudonarcissus into grazed permanent pasture had no detrimental effects on the health or performance of the sheep which subsequently grazed the pasture. There was no consistency to the effects of fertiliser rates on galanthamine yields. There was no difference in overall galanthamine yield if N. pseudonarcissus was cut biennially (1.64 vs. 1.75 kg galanthamine/ha for annual combined vs biennial cuts respectively; s.e.d = 0.117 kg galanthamine/ha; ns). This study verified the feasibility of a dual cropping approach to producing plant-derived galanthamine.

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.

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.

Vector Hamiltonian formalism (VHF) for the description of a weakly nonlinear magnetization dynamics has been developed. Transformation from the traditional Landau-Lifshitz equation, describing dynamics of a magnetization vector m⃗(r⃗, t) on a sphere, to a vector Hamiltonian equation, describing dynamics of a spin excitation vector s⃗(r⃗, t) on a plane, is done using the azimuthal Lambert transformation that preserves both the phase-space area and vector structure of dynamical equations, and guarantees that the plane containing vector s⃗(r⃗, t) is at each value of the coordinate r⃗ perpendicular to the a stationary vector m⃗_0(r⃗) describing the magnetization ground state of the system. By expanding vector s⃗(r⃗, t) in a complete set of linear magnetic vector eigemodes s⃗_ν(r⃗) of the studied system, and using a weakly nonlinear approximation |s⃗(r⃗, t)| ≪ 1, it is possible to express the Hamiltonian function of the system in the form of integrals over the vector eigenmode profiles s⃗_ν(r⃗), and calculate all the coefficients of this Hamiltonian. The developed approach allows one to describe weakly nonlinear dynamics in micro- and nano-scale magnetic systems with complicated geometries and spatially non-uniform ground states by numerically calculating linear spectrum and eigenmode profiles, and semi-analytically evaluating amplitudes of multi-mode nonlinear interactions. Examples of applications of the developed formalism to the magnetic systems having spatially nonuniform ground state of magnetization are presented.

We present a study of precessional magnetization switching in orthogonal spin-torque spin-valve devices at low temperatures. The samples consist of a spin-polarizing layer that is magnetized out-of-the film plane and an in-plane magnetized free and reference magnetic layer separated by non-magnetic metallic layers. We find coherent oscillations in the switching probability, characterized by high speed switching (~200 ps), error rates as low as 10 −5 and decoherence effects at longer timescales (~1 ns). Our study, which is conducted over a wide range of parameter space (pulse amplitude and duration) with deep statistics, demonstrates that the switching dynamics are likely dominated by the action of the out-of-plane spin polarization, in contrast to in-plane spin-torque from the reference layer, as has been the case in most previous studies. Our results demonstrate that precessional spin-torque devices are well suited to a cryogenic environment, while at room temperature they have so far not exhibited coherent or reliable switching.

We present a study of 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 temperatures. The dilute permalloy free layer device switches much faster: the characteristic switching time for a permalloy (Ni0.83Fe0.17) free 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.