Scalable Spatial Super-Resolution using Entangled Photons

N00N states -- maximally path-entangled states of N photons -- exhibit spatial interference patterns sharper than any classical interference pattern. This is known as super-resolution. However, even with perfectly efficient number-resolving detectors, the detection efficiency of all previously demonstrated methods to measure such interference decreases exponentially with the number of photons in the N00N state, often leading to the conclusion that N00N states are unsuitable for spatial measurements. Here, we create spatial super-resolution fringes with two-, three-, and four-photon N00N states, and demonstrate a scalable implementation of the so-called ``optical centroid measurement'' which provides an in-principle perfect detection efficiency. Moreover, we compare the N00N-state interference to the corresponding classical super-resolution interference. Although both provide the same increase in spatial frequency, the visibility of the classical fringes decreases exponentially with the number of detected photons, while the visibility of our experimentally measured N00N-state super-resolution fringes remains approximately constant with N. Our implementation of the optical centroid measurement is a scalable method to measure high photon-number quantum interference, an essential step forward for quantum-enhanced measurements, overcoming what was believed to be a fundamental challenge to quantum metrology

Ultrahigh and persistent optical depths of caesium in Kagom\'e-type hollow-core photonic crystal fibres

Alkali-filled hollow-core fibres are a promising medium for investigating light-matter interactions, especially at the single-photon level, due to the tight confinement of light and high optical depths achievable by light-induced atomic desorption. However, until now these large optical depths could only be generated for seconds at most once per day, severely limiting the practicality of the technology. Here we report the generation of highest observed transient ($>10^5$ for up to a minute) and highest observed persistent ($>2000$ for hours) optical depths of alkali vapours in a light-guiding geometry to date, using a caesium-filled Kagom\'e-type hollow-core photonic crystal fibre. Our results pave the way to light-matter interaction experiments in confined geometries requiring long operation times and large atomic number densities, such as generation of single-photon-level nonlinearities and development of single photon quantum memories.Comment: Author Accepted versio

Theory of noise suppression in {\Lambda}-type quantum memories by means of a cavity

Quantum memories, capable of storing single photons or other quantum states of light, to be retrieved on-demand, offer a route to large-scale quantum information processing with light. A promising class of memories is based on far-off-resonant Raman absorption in ensembles of $\Lambda$-type atoms. However at room temperature these systems exhibit unwanted four-wave mixing, which is prohibitive for applications at the single-photon level. Here we show how this noise can be suppressed by placing the storage medium inside a moderate-finesse optical cavity, thereby removing the main roadblock hindering this approach to quantum memory.Comment: 10 pages, 3 figures. This paper provides the theoretical background to our recent experimental demonstration of noise suppression in a cavity-enhanced Raman-type memory ( arXiv:1510.04625 ). See also the related paper arXiv:1511.05448, which describes numerical modelling of an atom-filled cavity. Comments welcom

Large scale quantum walks by means of optical fiber cavities

We demonstrate a platform for implementing quantum walks that overcomes many of the barriers associated with photonic implementations. We use coupled fiber-optic cavities to implement time-bin encoded walks in an integrated system. We show that this platform can achieve very low losses combined with high-fidelity operations, enabling an unprecedented large number of steps in a passive system, as required for scenarios with multiple walkers. Furthermore the platform is reconfigurable, enabling variation of the coin, and readily extends to multidimensional lattices. We demonstrate variation of the coin bias experimentally for three different values

High-speed noise-free optical quantum memory

Quantum networks promise to revolutionise computing, simulation, and communication. Light is the ideal information carrier for quantum networks, as its properties are not degraded by noise in ambient conditions, and it can support large bandwidths enabling fast operations and a large information capacity. Quantum memories, devices that store, manipulate, and release on demand quantum light, have been identified as critical components of photonic quantum networks, because they facilitate scalability. However, any noise introduced by the memory can render the device classical by destroying the quantum character of the light. Here we introduce an intrinsically noise-free memory protocol based on two-photon off-resonant cascaded absorption (ORCA). We consequently demonstrate for the first time successful storage of GHz-bandwidth heralded single photons in a warm atomic vapour with no added noise; confirmed by the unaltered photon statistics upon recall. Our ORCA memory platform meets the stringent noise-requirements for quantum memories whilst offering technical simplicity and high-speed operation, and therefore is immediately applicable to low-latency quantum networks

Two-year prognostic utility of plasma p217+tau across the Alzheimer’s continuum

Background: Plasma p217+tau has shown high concordance with cerebrospinal fluid (CSF) and positron emission tomography (PET) measures of amyloid- (A ) and tau in Alzheimer’s Disease (AD). However, its association with longitudinal cognition and comparative performance to PET A and tau in predicting cognitive decline are unknown. Objectives: To evaluate whether p217+tau can predict the rate of cognitive decline observed over two-year average follow-up and compare this to prediction based on A (18F-NAV4694) and tau (18F-MK6240) PET. We also explored the sample size required to detect a 30% slowing in cognitive decline in a 2-year trial and selection test cost using p217+tau (pT+) as compared to PET A (A+) and tau (T+) with and without p217+tau pre-screening. Design: A prospective observational cohort study. Setting: Participants of the Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL) and Australian Dementia Network (ADNeT). Participants: 153 cognitively unimpaired (CU) and 50 cognitively impaired (CI) individuals. Measurements: Baseline p217+tau Simoa assay 18F-MK6240 tau-PET and 18F-NAV4694 A -PET with neuropsychological follow-up (MMSE, CDR-SB, AIBL-PACC) over 2.4 ± 0.8 years. Results: In CI, p217+tau was a significant predictor of change in MMSE ( = −0.55, p \u3c 0.001) and CDR-SB ( =0.61, p \u3c 0.001) with an effect size similar to A Centiloid (MMSE = −0.48, p = 0.002; CDR-SB = 0.43, p = 0.004) and meta-temporal (MetaT) tau SUVR (MMSE: = −0.62, p \u3c 0.001; CDR-SB: = 0.65, p \u3c 0.001). In CU, only MetaT tau SUVR was significantly associated with change in AIBL-PACC ( = −0.22, p = 0.008). Screening pT+ CI participants into a trial could lead to 24% reduction in sample size compared to screening with PET for A+ and 6–13% compared to screening with PET for T+ (different regions). This would translate to an 81–83% biomarker test cost-saving assuming the p217+tau test cost one-fifth of a PET scan. In a trial requiring PET A+ or T+, p217+tau pre-screening followed by PET in those who were pT+ would cost more in the CI group, compared to 26–38% biomarker test cost-saving in the CU. Conclusions: Substantial cost reduction can be achieved using p217+tau alone to select participants with MCI or mild dementia for a clinical trial designed to slow cognitive decline over two years, compared to participant selection by PET. In pre-clinical AD trials, p217+tau provides significant cost-saving if used as a pre-screening measure for PET A+ or T+ but in MCI/mild dementia trials this may add to cost both in testing and in the increased number of participants needed for testing

Optimal Timing of Administration of Direct-Acting Antivirals for Patients with Hepatitis C-Associated Hepatocellular Carcinoma Undergoing Liver Transplantation

Objective: To investigate the optimal timing of direct acting antiviral (DAA) administration in patients with hepatitis C-associated hepatocellular carcinoma (HCC) undergoing liver transplantation (LT). Summary of Background Data: In patients with hepatitis C (HCV) associated HCC undergoing LT, the optimal timing of direct-acting antivirals (DAA) administration to achieve sustained virologic response (SVR) and improved oncologic outcomes remains a topic of much debate. Methods: The United States HCC LT Consortium (2015–2019) was reviewed for patients with primary HCV-associated HCC who underwent LT and received DAA therapy at 20 institutions. Primary outcomes were SVR and HCC recurrence-free survival (RFS). Results: Of 857 patients, 725 were within Milan criteria. SVR was associated with improved 5-year RFS (92% vs 77%, P < 0.01). Patients who received DAAs pre-LT, 0–3 months post-LT, and ≥3 months post-LT had SVR rates of 91%, 92%, and 82%, and 5-year RFS of 93%, 94%, and 87%, respectively. Among 427 HCV treatment-naïve patients (no previous interferon therapy), patients who achieved SVR with DAAs had improved 5-year RFS (93% vs 76%, P < 0.01). Patients who received DAAs pre-LT, 0–3 months post-LT, and ≥3 months post-LT had SVR rates of 91%, 93%, and 78% (P < 0.01) and 5-year RFS of 93%, 100%, and 83% (P = 0.01). Conclusions: The optimal timing of DAA therapy appears to be 0 to 3 months after LT for HCV-associated HCC, given increased rates of SVR and improved RFS. Delayed administration after transplant should be avoided. A prospective randomized controlled trial is warranted to validate these results

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License