Nonlinear induction detection of electron spin resonance

Bachar, Gil; Suchoi, Oren; Shtempluck, Oleg; Blank, Aharon; Buks, Eyal

doi:10.1063/1.4734500

Cited by 3 publications

(3 citation statements)

References 25 publications

(27 reference statements)

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“…Under the condition that δω ≈ Ω R , this requires that g √ N s κ Ω R , δω ω c , ω s (see A 3) [37]. For example, assuming an implementation using Xband pulsed electron spin resonance (ESR) (ω c /2π ≈ ω s /2π = 10 GHz), with samples that typically contain from roughly N s = 10 6 spins to N s = 10 17 spins [44,45], experimentally reasonable values are Ω R /2π = 100 MHz, Q = 10 4 (κ/2π = 1 MHz) [46][47][48], and g/2π = 1 Hz [47].…”

Section: Simulationsmentioning

confidence: 99%

Cavity Cooling of an Ensemble Spin System

2014

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We describe how sideband cooling techniques may be applied to large spin ensembles in magnetic resonance. Using the Tavis-Cummings model in the presence of a Rabi drive, we solve a Markovian master equation describing the joint spin-cavity dynamics to derive cooling rates as a function of ensemble size. Our calculations indicate that the coupled angular momentum subspaces of a spin ensemble containing roughly 10(11) electron spins may be polarized in a time many orders of magnitude shorter than the typical thermal relaxation time. The described techniques should permit efficient removal of entropy for spin-based quantum information processors and fast polarization of spin samples. The proposed application of a standard technique in quantum optics to magnetic resonance also serves to reinforce the connection between the two fields, which has recently begun to be explored in further detail due to the development of hybrid designs for manufacturing noise-resilient quantum devices.

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Section: Simulationsmentioning

confidence: 99%

Cavity Cooling of an Ensemble Spin System

2014

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“…Nonlinear resonators are used in a variety of applications, including superconducting qubits for quantum information processing [35], microwave kinetic inductance detectors for astronomy [36], and increasing inductive detection sensitivity in magnetic resonance [37]. Often, however, electronics controlling quantum systems are operated in their linear regime to avoid complications resulting from nonlinearity [25].…”

Section: Pacs Numbersmentioning

confidence: 99%

Controlling Quantum Devices with Nonlinear Hardware

et al. 2015

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High fidelity coherent control of quantum systems is critical to building quantum devices and quantum computers. We provide a general optimal control framework for designing control sequences that account for hardware control distortions while maintaining robustness to environmental noise. We demonstrate the utility of our algorithm by presenting examples of robust quantum gates optimized in the presence of nonlinear distortions. We show that nonlinear classical controllers do not necessarily incur additional computational cost to pulse optimization, enabling more powerful quantum devices. PACS numbers:The ability to coherently control the dynamics of quantum systems with high fidelity is a critical component of the development of modern quantum devices, including quantum computers [1], actuators [2,3], and sensors [4][5][6] that push beyond the capabilities of classical computation and metrology. In recent years, quantum computation has presented a compelling application for quantum control, as high-fidelity control is essential to implement quantum information processors that achieve fault-tolerance [7][8][9].The performance of numerically optimized quantum gates in laboratory applications strongly depends on the accuracy of the system model used to approximate the response of the experimental system to the applied control sequence. Here we develop a general framework whereby classical control hardware components are modelled explicitly, such that their effect on a quantum system can be computed and compensated for using numerical optimal control theory (OCT) [10] algorithms to optimize control sequences. Control sequences designed using OCT algorithms, such as the GRadient Ascent Pulse Engineering (GRAPE) [11] algorithm, can be made robust to a wide variety of inhomogenities, pulse errors and noise processes [12][13][14]. These methods are also easily extended [15][16][17][18] to other applications and may be integrated into other protocols [19]. Recently, it was demonstrated how a model of linear distortions of the control sequence, such as those arising from finite bandwidth of the classical control hardware, may also be integrated into OCT algorithms [20][21][22].Here, we improve and generalize those results beyond linear kernels to any operation that smoothly maps a list of control steps to a classical field seen by the quantum system. Importantly, our framework naturally allows for robustness against uncertainties and errors due to classical control hardware. We begin developing our method generally, without making assumptions about the device of interest, so that our results may be broadly applicable to a wide range of quantum devices. We briefly discuss how our theory is easily applied to any linear distortion, and then in more detail, demonstrate with numerics how nonlinearities in control hardware, such as those found in strongly-driven superconducting resonators used for pulsed electron spin resonance (PESR) [23][24][25], may be included in OCT algorithms.With this goal in mind, we briefly review th...

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“…The Jacobian matrix is simply given by J p (g) = φ which is independent of the pulse p. As a more involved example we consider a quantum system being controlled by a tuned and matched resonator circuit [34] with nonlinear circuit elements (Figure 2). Nonlinear resonators are used in a variety of applications, including superconducting qubits for quantum information processing [35], microwave kinetic inductance detectors for astronomy [36], and increasing inductive detection sensitivity in magnetic resonance [37]. Often, however, electronics controlling quantum systems are operated in their linear regime to avoid complications resulting from nonlinearity [25].…”

mentioning

confidence: 99%

Accounting for Classical Hardware in the Control of Quantum Devices

Hincks,

Granade,

Borneman

et al. 2014

Preprint

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Nonlinear induction detection of electron spin resonance

Cited by 3 publications

References 25 publications

Cavity Cooling of an Ensemble Spin System

Cavity Cooling of an Ensemble Spin System

Controlling Quantum Devices with Nonlinear Hardware

Accounting for Classical Hardware in the Control of Quantum Devices

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