Quantum computational advantage with a programmable photonic processor.

A quantum computer attains computational advantage when outperforming the best classical computers running the best-known algorithms on well-defined tasks. No photonic machine offering programmability over all its quantum gates has demonstrated quantum computational advantage: previous machines1,2 were largely restricted to static gate sequences. Earlier photonic demonstrations were also vulnerable to spoofing3, in which classical heuristics produce samples, without direct simulation, lying closer to the ideal distribution than do samples from the quantum hardware. Here we report quantum computational advantage using Borealis, a photonic processor offering dynamic programmability on all gates implemented. We carry out Gaussian boson sampling4 (GBS) on 216 squeezed modes entangled with three-dimensional connectivity5, using a time-multiplexed and photon-number-resolving architecture. On average, it would take more than 9,000 years for the best available algorithms and supercomputers to produce, using exact methods, a single sample from the programmed distribution, whereas Borealis requires only 36 µs. This runtime advantage is over 50 million times as extreme as that reported from earlier photonic machines. Ours constitutes a very large GBS experiment, registering events with up to 219 photons and a mean photon number of 125. This work is a critical milestone on the path to a practical quantum computer, validating key technological features of photonics as a platform for this goal.

Assessing the accuracy of using oscillating gradient spin echo sequences with AxCaliber to infer micron-sized axon diameters.

OBJECTIVE: Current magnetic resonance imaging (MRI) axon diameter measurements rely on the pulsed gradient spin-echo sequence, which is unable to provide diffusion times short enough to measure small axon diameters. This study combines the AxCaliber axon diameter fitting method with data generated from Monte Carlo simulations of oscillating gradient spin-echo sequences (OGSE) to infer micron-sized axon diameters, in order to determine the feasibility of using MRI to infer smaller axon diameters in brain tissue. MATERIALS AND METHODS: Monte Carlo computer simulation data were synthesized from tissue geometries of cylinders of different diameters using a range of gradient frequencies in the cosine OGSE sequence . Data were fitted to the AxCaliber method modified to allow the new pulse sequence. Intra- and extra-axonal water were studied separately and together. RESULTS: The simulations revealed the extra-axonal model to be problematic. Rather than change the model, we found that restricting the range of gradient frequencies such that the measured apparent diffusion coefficient was constant over that range resulted in more accurate fitted diameters. Thus a careful selection of frequency ranges is needed for the AxCaliber method to correctly model extra-axonal water, or adaptations to the method are needed. This restriction helped reduce the necessary gradient strengths for measurements that could be performed with parameters feasible for a Bruker BG6 gradient set. For these experiments, the simulations inferred diameters as small as 0.5 µm on square-packed and randomly packed cylinders. The accuracy of the inferred diameters was found to be dependent on the signal-to-noise ratio (SNR), with smaller diameters more affected by noise, although all diameter distributions were distinguishable from one another for all SNRs tested. CONCLUSION: The results of this study indicate the feasibility of using MRI with OGSE on preclinical scanners to infer small axon diameters.

Quantum computational advantage via high-dimensional Gaussian boson sampling.

Photonics is a promising platform for demonstrating a quantum computational advantage (QCA) by outperforming the most powerful classical supercomputers on a well-defined computational task. Despite this promise, existing proposals and demonstrations face challenges. Experimentally, current implementations of Gaussian boson sampling (GBS) lack programmability or have prohibitive loss rates. Theoretically, there is a comparative lack of rigorous evidence for the classical hardness of GBS. In this work, we make progress in improving both the theoretical evidence and experimental prospects. We provide evidence for the hardness of GBS, comparable to the strongest theoretical proposals for QCA. We also propose a QCA architecture we call high-dimensional GBS, which is programmable and can be implemented with low loss using few optical components. We show that particular algorithms for simulating GBS are outperformed by high-dimensional GBS experiments at modest system sizes. This work thus opens the path to demonstrating QCA with programmable photonic processors.

Comparison of manual and semi-automated segmentation methods to evaluate hippocampus volume in APP and PS1 transgenic mice obtained via in vivo magnetic resonance imaging.

BACKGROUND: Magnetic resonance imaging (MRI) of transgenic mouse models of Alzheimer's disease is valuable to understand better the structural changes that occur in the brain and could provide a means to test drug treatments. A hallmark pathological feature of Alzheimer's disease is atrophy of the hippocampus, which is an early biomarker of the disease. MRI can be used to detect and monitor this biomarker. METHOD: Repeated measurements using in vivo 3D T2-weighted imaging of mice were used to assess the methods. Each mouse was imaged twice in one week and twice the following week and no changes in volume were expected. The hippocampus was segmented both manually and semi-automatically. Registration was done to gain information on shape changes. The volumes from each mouse were compared intra-mouse, between mice and to hippocampus volume values in the literature. RESULTS: A reliable method was developed which was able to detect difference in volumes of hippocampus between mice when performed by a single individual. The semi-automated segmentation was unable to detect the same level of differences. The semi-automated segmentation method gave larger hippocampus volumes, with 78-87% reliability between the manual and semi-automated segmentation. Although more accurate, the manual segmentation is laborious and suffers from inter- and intra-variability. CONCLUSION: These results suggest that manual segmentation is still considered the most reliable segmentation method for small structures. However, if performing longitudinal studies, where there is at least one year between imaging sessions, the segmentation should be done all at once at the end of all the imaging sessions. If segmentation is done after each imaging session, with at least a year passing between segmentations, very small variations in volumes can be missed. This method provides a means to quantify the volume of the hippocampus in a live mouse using manual segmentation, which is the first step toward studying hippocampus atrophy in a mouse model of Alzheimer's disease.