Efficiently measuring a quantum device using machine learning

Lennon, Dominic T.; Moon, Hyun-Joo; Camenzind, Leon C.; Yu, Liuqi; Zumbühl, Dominik M.; Briggs, G. Andrew D.; Osborne, Michael A.; Laird, E.; Ares, Natalia

doi:10.1038/s41534-019-0193-4

Cited by 68 publications

(52 citation statements)

References 27 publications

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“…In addition, stable and nonhysteretic navigation in parameter space is a prerequisite for employing automatic tuning and operation procedures that will be essential for operation of complex quantum devices in the future. [ 50 ]…”

Section: Figurementioning

confidence: 99%

Shadow Epitaxy for In Situ Growth of Generic Semiconductor/Superconductor Hybrids

et al. 2020

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The quality of interfaces and surfaces is crucial for the performance of nanoscale devices. A pertinent example is the close tie between current progress in gate-tunable and topological superconductivity using semiconductor/superconductor electronic devices and the hard proximity-induced superconducting gap obtained from epitaxial indium arsenide/aluminium heterostructures. Fabrication of devices requires selective etch processes; these only exist for InAs/Al hybrids, which precludes the use of other, potentially better material combinations in functional devices. We present a crystal growth platform based on three-dimensional structuring of growth substrates for synthesising semiconductor nanowires with in-situ patterned superconductor shells, which enables independent choice of material by eliminating etching. We realise and characterise all the most frequently used architectures in superconducting hybrid devices, finding increased yield and electrostatic stability compared to etched devices, along with evidence of ballistic superconductivity. In addition to aluminium, we present hybrid devices based on tantalum, niobium and vanadium.One dimensional semiconductor (SE) nanowires (NWs) proximity coupled to superconductors (SU) have attracted considerable attention from the condensed matter community since the prediction 1,2 and observation of Majorana zero-modes 3-5 , which have been proposed as a basis for topologically protected quantum information processors 6,7 . To ensure topological protection, methods for growing disorder-free 'hard-gap' SE/SU epitaxial hybrids were developed 8-10 . These materials utilise bottom-up crystal growth of InAs nanowires with uniform epitaxial aluminium coatings, an approach which has been extended to high mobility two-dimensional systems 11,12 and selective area grown networks 13,14 . The success of epitaxial InAs/Al hybrids lies in the ability to realise important device classes such as normal metal spectroscopic devices, 5,9,11,12 Josephson Junctions 15-18 for gate-controlled transmon qubits 19,20 , and superconducting Majorana islands 21-23 , using top-down processing to selectively remove the Al. A limitation of this method is that relying on post-process etching inherently limits materials choice. For instance, despite strong incentives to utilise technologically important superconductors such as Nb 24 and NbTiN 25 -which exhibit higher transition temperatures, critical magnetic fields and superconducting energy gaps -selectively removing Nb from InAs remains an unsolved problem. Similarly, InSb is an attractive semiconductor due to its high mobility, g-factor and strong spin-orbit coupling 25-28 . Yet, selectively removing even aluminum from InSb without damage is impossible with known methods. Thus, most potential improvements in epitaxial SE/SU technology are predicated on developing a materials-independent method for device fabrication. An attractive approach to eliminate etching is to employ an in-situ 'shadow approach' to mask specific segments along the NW from supe...

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Section: Figurementioning

confidence: 99%

Shadow Epitaxy for In Situ Growth of Generic Semiconductor/Superconductor Hybrids

et al. 2020

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“…In this work we apply our adaptive technique on NV centres, although we emphasise that our scheme can be applied to various other physical systems, including other types of spins [39,[47][48][49], superconducting qubits [50,51] and trapped ions [52]. Our analysis starts with a discussion of the model employed to numerically simulate the central spin and the nuclear environment in section 2 and an introduction to the standard (non-adaptive) Ramsey measurement sequence in section 3.…”

Section: Introductionmentioning

confidence: 99%

Extending qubit coherence by adaptive quantum environment learning

2020

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Decoherence, resulting from unwanted interaction between a qubit and its environment, poses a serious challenge towards the development of quantum technologies. Recently, researchers have started analysing how real-time Hamiltonian learning approaches, based on estimating the qubit state faster than the environmental fluctuations, can be used to counteract decoherence. In this work, we investigate how the back-action of the quantum measurements used in the learning process can be harnessed to extend qubit coherence. We propose an adaptive protocol that, by learning the qubit environment, narrows down the distribution of possible environment states. While the outcomes of quantum measurements are random, we show that real-time adaptation of measurement settings (based on previous outcomes) allows a deterministic decrease of the width of the bath distribution, and hence an increase of the qubit coherence. We numerically simulate the performance of the protocol for the electronic spin of a nitrogen-vacancy centre in diamond subject to a dilute bath of 13 C nuclear spin, finding a considerable improvement over the performance of non-adaptive strategies.

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“…As (imperfect) quantum tomography is a data-driven technique, recent proposals suggest a natural benefit offered by machine learning methods. Bayesian models were used to optimise the data collection process by adaptive measurements in state reconstruction [8,9,23], process tomography [24], Hamiltonian learning [25] and other problems in experimental characterisation of quantum devices [26]. Neural networks were proposed to facilitate quantum tomography in high-dimensions.…”

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confidence: 99%

Experimental neural network enhanced quantum tomography

et al. 2020

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Quantum tomography is currently ubiquitous for testing any implementation of a quantum information processing device. Various sophisticated procedures for state and process reconstruction from measured data are well developed and benefit from precise knowledge of the model describing state preparation and the measurement apparatus. However, physical models suffer from intrinsic limitations as actual measurement operators and trial states cannot be known precisely. This scenario inevitably leads to state-preparation-and-measurement (SPAM) errors degrading reconstruction performance. Here we develop and experimentally implement a machine learning based protocol reducing SPAM errors. We trained a supervised neural network to filter the experimental data and hence uncovered salient patterns that characterize the measurement probabilities for the original state and the ideal experimental apparatus free from SPAM errors. We compared the neural network state reconstruction protocol with a protocol treating SPAM errors by process tomography, as well as to a SPAM-agnostic protocol with idealized measurements. The average reconstruction fidelity is shown to be enhanced by 10% and 27%, respectively. The presented methods apply to the vast range of quantum experiments which rely on tomography.arXiv:1904.05902v2 [quant-ph]

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Efficiently measuring a quantum device using machine learning

Cited by 68 publications

References 27 publications

Shadow Epitaxy for In Situ Growth of Generic Semiconductor/Superconductor Hybrids

Shadow Epitaxy for In Situ Growth of Generic Semiconductor/Superconductor Hybrids

Extending qubit coherence by adaptive quantum environment learning

Experimental neural network enhanced quantum tomography

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