Benchmarking an 11-qubit quantum computer

The field of quantum computing has grown from concept to demonstration devices over the past 20 years. Universal quantum computing offers efficiency in approaching problems of scientific and commercial interest, such as factoring large numbers, searching databases, simulating intractable models from quantum physics, and optimizing complex cost functions. Here, we present an 11-qubit fully-connected, programmable quantum computer in a trapped ion system composed of 13 ¹⁷¹Yb⁺ ions. We demonstrate average single-qubit gate fidelities of 99.5\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document}%, average two-qubit-gate fidelities of 97.5\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document}%, and SPAM errors of 0.7\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document}%. To illustrate the capabilities of this universal platform and provide a basis for comparison with similarly-sized devices, we compile the Bernstein-Vazirani and Hidden Shift algorithms into our native gates and execute them on the hardware with average success rates of 78\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document}% and 35\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document}%, respectively. These algorithms serve as excellent benchmarks for any type of quantum hardware, and show that our system outperforms all other currently available hardware.

The growing complexity of quantum computing devices makes presents challenges for benchmarking their performance as previous, exhaustive approaches become infeasible. Here the authors characterise the quality of their 11-qubit device by successfully computing two quantum algorithms.

On the accuracy of optically tracked transducers for image-guided transcranial ultrasound

PURPOSE

Transcranial focused ultrasound (FUS) is increasingly being explored to modulate neuronal activity. To target neuromodulation, researchers often localize the FUS beam onto the brain region(s) of interest using spatially tracked tools overlaid on pre-acquired images. Here, we quantify the accuracy of optically tracked image-guided FUS with magnetic resonance imaging (MRI) thermometry, evaluate sources of error and demonstrate feasibility of these procedures to target the macaque somatosensory region.

METHODS

We developed an optically tracked FUS system capable of projecting the transducer focus onto a pre-acquired MRI volume. To measure the target registration error (TRE), we aimed the transducer focus at a desired target in a phantom under image guidance, heated the target while imaging with MR thermometry and then calculated the TRE as the difference between the targeted and heated locations. Multiple targets were measured using either an unbiased or bias-corrected calibration. We then targeted the macaque S1 brain region, where displacement induced by the acoustic radiation force was measured using MR acoustic radiation force imaging (MR-ARFI).

RESULTS

All calibration methods enabled registration with TRE on the order of 3 mm. Unbiased calibration resulted in an average TRE of 3.26 mm (min-max: 2.80-4.53 mm), which was not significantly changed by prospective bias correction (TRE of 3.05 mm; 2.06-3.81 mm, p = 0.55). Restricting motion between the transducer and target and increasing the distance between tracked markers reduced the TRE to 2.43 mm (min-max: 0.79-3.88 mm). MR-ARFI images showed qualitatively similar shape and extent as projected beam profiles in a living non-human primate.

CONCLUSIONS

Our study describes methods for image guidance of FUS neuromodulation and quantifies errors associated with this method in a large animal. The workflow is efficient enough for in vivo use, and we demonstrate transcranial MR-ARFI in vivo in macaques for the first time.