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Squeezed states are essential for continuous variable (CV) quantum information processing, with wide-ranging applications in computing, sensing and communications. Integrated photonic circuits provide a scalable, convenient platform for building large CV circuits. Thin-film Lithium Niobate (TFLN) is particularly promising due to its low propagation loss, efficient parametric down conversion, and fast electro-optical modulation. In this work, we demonstrate a squeezed light source on an integrated TFLN platform, achieving a measured shot noise reduction of 0.46 dB using modal phase matching and grating couplers with an efficiency of up to -2.2 dB. The achieved squeezing is comparable to what has been observed using more complex circuitry based on periodic poling. The simpler design allows for compact, efficient and reproducible sources of squeezed light., Comment: 6 pages, 6 figures

In human sleep staging models, augmenting the temporal context of the input to the range of tens of minutes has recently demonstrated performance improvement. In contrast, the temporal context of mouse sleep staging models is typically in the order of tens of seconds. While long-term time patterns are less clear in mouse sleep, increasing the temporal context further than that of the current mouse sleep staging models might still result in a performance increase, given that the current methods only model very short term patterns. In this study, we examine the influence of increasing the temporal context in mouse sleep staging up to 15 minutes in three mouse cohorts using two recent and high-performing human sleep staging models that account for long-term dependencies. These are compared to two prominent mouse sleep staging models that use a local context of 12 s and 20 s, respectively. An increase in context up to 28 s is observed to have a positive impact on sleep stage classification performance, especially in REM sleep. However, the impact is limited for longer context windows. One of the human sleep scoring models, L-SeqSleepNet, outperforms both mouse models in all cohorts. This suggests that mouse sleep staging can benefit from more temporal context than currently used., Comment: Accepted for publication in the 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (2024)

Certified randomness guaranteed to be unpredictable by adversaries is central to information security. The fundamental randomness inherent in quantum physics makes certification possible from devices that are only weakly characterised, i.e. requiring little trust in their implementation. It was recently shown that the amount of certifiable randomness can be greatly improved using the so-called Entropy Accumulation Theorem generalised to prepare-and-measure settings. Furthermore, this approach allows a finite-size analysis which avoids assuming that all rounds are independent and identically distributed. Here, we demonstrate this improvement in semi-device-independent randomness certification from untrusted measurements., Comment: 18 pages, 5 figures, comments are welcome

We show that quantum entanglement can provide an exponential advantage in learning properties of a bosonic continuous-variable (CV) system. The task we consider is estimating a probabilistic mixture of displacement operators acting on $n$ bosonic modes, called a random displacement channel. We prove that if the $n$ modes are not entangled with an ancillary quantum memory, then the channel must be sampled a number of times exponential in $n$ in order to estimate its characteristic function to reasonable precision; this lower bound on sample complexity applies even if the channel inputs and measurements performed on channel outputs are chosen adaptively. On the other hand, we present a simple entanglement-assisted scheme that only requires a number of samples independent of $n$, given a sufficient amount of squeezing. This establishes an exponential separation in sample complexity. We then analyze the effect of photon loss and show that the entanglement-assisted scheme is still significantly more efficient than any lossless entanglement-free scheme under mild experimental conditions. Our work illuminates the role of entanglement in learning continuous-variable systems and points toward experimentally feasible demonstrations of provable entanglement-enabled advantage using CV quantum platforms., Comment: 7+28 pages, 3+6 figures

Variational quantum algorithms (VQAs) are hybrid quantum-classical approaches used for tackling a wide range of problems on noisy intermediate-scale quantum (NISQ) devices. Testing these algorithms on relevant hardware is crucial to investigate the effect of noise and imperfections and to assess their practical value. Here, we implement a variational algorithm designed for optimized parameter estimation on a continuous variable platform based on squeezed light, a key component for high-precision optical phase estimation. We investigate the ability of the algorithm to identify the optimal metrology process, including the optimization of the probe state and measurement strategy for small-angle optical phase sensing. Two different optimization strategies are employed, the first being a gradient descent optimizer using Gaussian parameter shift rules to estimate the gradient of the cost function directly from the measurements. The second strategy involves a gradient-free Bayesian optimizer, fine-tuning the system using the same cost function and trained on the data acquired through the gradient-dependent algorithm. We find that both algorithms can steer the experiment towards the optimal metrology process. However, they find minima not predicted by our theoretical model, demonstrating the strength of variational algorithms in modelling complex noise environments, a non-trivial task., Comment: Main document: 7 pages, 3 figures, Supplementary Material: 14 page, 10 figures

Electroencephalography (EEG) is a prominent non-invasive neuroimaging technique providing insights into brain function. Unfortunately, EEG data exhibit a high degree of noise and variability across subjects hampering generalizable signal extraction. Therefore, a key aim in EEG analysis is to extract the underlying neural activation (content) as well as to account for the individual subject variability (style). We hypothesize that the ability to convert EEG signals between tasks and subjects requires the extraction of latent representations accounting for content and style. Inspired by recent advancements in voice conversion technologies, we propose a novel contrastive split-latent permutation autoencoder (CSLP-AE) framework that directly optimizes for EEG conversion. Importantly, the latent representations are guided using contrastive learning to promote the latent splits to explicitly represent subject (style) and task (content). We contrast CSLP-AE to conventional supervised, unsupervised (AE), and self-supervised (contrastive learning) training and find that the proposed approach provides favorable generalizable characterizations of subject and task. Importantly, the procedure also enables zero-shot conversion between unseen subjects. While the present work only considers conversion of EEG, the proposed CSLP-AE provides a general framework for signal conversion and extraction of content (task activation) and style (subject variability) components of general interest for the modeling and analysis of biological signals., Comment: Accepted for publication at the 37th Conference on Neural Information Processing Systems (NeurIPS 2023)

This work reports a fully guided setup for single-mode squeezing generation on integrated titanium-indiffused periodically poled nonlinear resonators. A continuous wave laser beam is delivered and the squeezed field is collected by single-mode fibers, where up to -3.17(9) dB of useful squeezing is available in fibers. To showcase the usefulness of such a fiber-coupled device, we applied the generated squeezed light in a fiber-based phase sensing experiment, showing a quantum enhancement in the signal-to-noise ratio of 0.35 dB. Moreover, our investigation of photorefraction's impact on the cavity resonance condition suggests that it causes system instabilities at high powers.

We analyze an all-optical setup which enables Bell-inequality violation over long distances by exploiting probabilistic entanglement swapping. The setup involves only two-mode squeezers, displacements, beamsplitters, and on/off detectors. We analyze a scenario with dichotomic inputs and outputs, and check the robustness of the Bell inequality violation for up to 6 parties, with respect to phase-, amplitude-, and dark-count noise, as well as loss.

The EdResearch for Action "Design Principles Series" focuses on a single program or practice that has been proven to have positive effects on student outcomes. The series helps practitioners adapt and successfully implement an evidence-based program to meet the needs of their target population. This brief provides the evidence base to make the case for instructional coaching and discusses who the best candidates to coach and to be coached are; what effective coaches do; and what supports coaches need to be successful.

This brief is one in a series aimed at providing K-12 education decision-makers and advocates with an evidence base to ground discussions about how to best serve students during and following the novel coronavirus pandemic. It offers design principles to improve practice with instructional coaching based on a review of research. Links to research are included, as well as key considerations for designing an evidence-based coaching program. [This report was produced in collaboration with the University of Pittsburgh, Learning Research and Development Center.]

We perform a theoretical investigation into how a two-mode squeezed vacuum state, that has undergone photon loss, can be stored and purified using noiseless amplification with a collection of solid-state qubits. The proposed method may be used to probabilistically increase the entanglement between the two parties sharing the state. The proposed amplification step is similar in structure to a set of quantum scissors. However, in this work the amplification step is realized by a state transfer from an optical mode to a set of solid-state qubits, which act as a quantum memory. We explore two different applications, the generation of entangled many-qubit registers, and the construction of quantum repeaters for long-distance quantum key distribution.

We demonstrate the generation, 40 km fiber transmission, and homodyne detection of single-mode squeezed states of light at 1550 nm using real-time phase control of a locally generated local oscillator, often called a "real local oscillator" or "local local oscillator". The system was able to stably measure up to around 3.7 dB of noise suppression with a phase noise uncertainty of around 2.5$^\circ$, using only standard telecom-compatible components and a field-programmable gate array (FPGA). The compactness, low degree of complexity and efficacy of the implemented scheme makes it a relevant candidate for long distance quantum communication in future photonic quantum networks.

The measurement of physical parameters is one of the main pillars of science. A classic example is the measurement of the optical phase enabled by optical interferometry where the best sensitivity achievable with N photons scales as 1/N - known as the Heisenberg limit . To achieve phase estimation at the Heisenberg limit, it has been common to consider protocols based on highly complex NOON states of light. However, despite decades of research and several experimental explorations, there has been no demonstration of deterministic phase estimation with NOON states reaching the Heisenberg limit or even surpassing the shot noise limit. Here we use a phase estimation scheme based on a deterministic source of Gaussian squeezed vacuum states and high-efficiency homodyne detection to obtain phase estimates with an extreme sensitivity that significantly surpasses the shot noise limit and even beats the performance of an ideal, and thus unrealistic, NOON state protocol. Using a high-efficiency setup with a total loss of about 11% we achieve a Fisher Information of 15.8(6) rad^2 per photon unparalleled by any other optical phase estimation technology. The work represents a fundamental achievement in quantum metrology, and it opens the door to future quantum sensing technologies for the interrogation of light-sensitive biological systems.

We present a scheme for quantum randomness certification based on quantum steering. The protocol is one-sided device independent, providing high security, but requires only states and measurements that are simple to realise on quantum optics platforms - entangled squeezed vacuum states and homodyne detection. This ease of implementation is demonstrated by certifying randomness in existing experimental data and implies that giga-hertz random bit rates should be attainable with current technology. Furthermore, the steering-based setting represents the closest to full device independence that can be achieved using purely Gaussian states and measurements., Comment: 8 pages, 6 figures

Quantum information encoded into superposition of coherent states is an illustrative representative of practical applications of macroscopic quantum coherence possessing. However, these states are very sensitive to energy loss, losing their non-classical aspects of coherence very rapidly. An available deterministic strategy to slow down this decoherence process is to apply a Gaussian squeezing transformation prior to the loss as a protective step. Here, we propose a deterministic hybrid protection scheme utilizing strong but feasible interactions with two-level ancillas immune to spontaneous emission. We verify robustness of the scheme against dephasing of qubit ancilla. Our scheme is applicable to complex superpositions of coherent states in many oscillators, and remarkably, the robustness to loss is enhanced with the amplitude of the coherent states. This scheme can be realized in experiments with atoms, solid-state systems and superconducting circuits.

Continuous variable measurement-based quantum computation on cluster states has in recent years shown great potential for scalable, universal, and fault-tolerant quantum computation when combined with the Gottesman-Kitaev-Preskill (GKP) code and quantum error correction. However, no complete fault-tolerant architecture exists that includes everything from cluster state generation with finite squeezing to gate implementations with realistic noise and error correction. In this work, we propose a simple architecture for the preparation of a cluster state in three dimensions in which gates by gate teleportation can be efficiently implemented. To accommodate scalability, we propose architectures that allow for both spatial and temporal multiplexing, with the temporal encoded version requiring as little as two squeezed light sources. Due to its three-dimensional structure, the architecture supports topological qubit error correction, while GKP error correction is efficiently realized within the architecture by teleportation. To validate fault-tolerance, the architecture is simulated using surface-GKP codes, including noise from GKP-states as well as gate noise caused by finite squeezing in the cluster state. We find a fault-tolerant squeezing threshold of 12.7 dB with room for further improvement.

Clinical sleep analysis require manual analysis of sleep patterns for correct diagnosis of sleep disorders. However, several studies have shown significant variability in manual scoring of clinically relevant discrete sleep events, such as arousals, leg movements, and sleep disordered breathing (apneas and hypopneas). We investigated whether an automatic method could be used for event detection and if a model trained on all events (joint model) performed better than corresponding event-specific models (single-event models). We trained a deep neural network event detection model on 1653 individual recordings and tested the optimized model on 1000 separate hold-out recordings. F1 scores for the optimized joint detection model were 0.70, 0.63, and 0.62 for arousals, leg movements, and sleep disordered breathing, respectively, compared to 0.65, 0.61, and 0.60 for the optimized single-event models. Index values computed from detected events correlated positively with manual annotations ($r^2$ = 0.73, $r^2$ = 0.77, $r^2$ = 0.78, respectively). We furthermore quantified model accuracy based on temporal difference metrics, which improved overall by using the joint model compared to single-event models. Our automatic model jointly detects arousals, leg movements and sleep disordered breathing events with high correlation with human annotations. Finally, we benchmark against previous state-of-the-art multi-event detection models and found an overall increase in F1 score with our proposed model despite a 97.5% reduction in model size. Source code for training and inference is available at https://github.com/neergaard/msed.git., Comment: 10 pages, 4 figures. Accepted for publication in IEEE Transactions on Biomedical Engineering

Quantum computing can be realized with numerous different hardware platforms and computational protocols. A highly promising approach to foster scalability is to apply a photonic platform combined with a measurement-induced quantum information processing protocol where gate operations are realized through optical measurements on a multipartite entangled quantum state -- a so-called cluster state. Heretofore, a few quantum gates on non-universal or non-scalable cluster states have been, but a full set of gates for universal scalable quantum computing has not been realized. We propose and demonstrate the deterministic implementation of a multi-mode set of measurement-induced quantum gates in a large two-dimensional (2D) optical cluster state using phase-controlled continuous variable quadrature measurements. Each gate is simply programmed into the phases of the high-efficiency quadrature measurements which execute the transformations by teleportation through the cluster state. Using these programmable gates, we demonstrate a small quantum circuit consisting of 10 single-mode gates and 2 two-mode gates on a three-mode input state. On this platform, fault-tolerant universal quantum computing is possible if the cluster state entanglement is improved and a supply of Gottesman-Kitaev-Preskill qubits is available. Moreover, it operates at the telecom wavelength and is therefore network connectable without quantum transducers.

With the Gottesman-Kitaev-Preskill (GKP) encoding, Clifford gates and error correction can be carried out using simple Gaussian operations. Still, non-Clifford gates, required for universality, require non-Gaussian elements. In their original proposal, GKP suggested a particularly simple method of using a single application of the cubic phase gate to perform the logical non-Clifford T-gate. Here we show that this cubic phase gate approach performs extraordinarily poorly, even for arbitrarily large amounts of squeezing in the GKP state. Thus, contrary to common belief, the cubic phase gate is not suitable for achieving universal fault-tolerant quantum computation with GKP states., Comment: 7 pages, 3 figures, corrected typos

Quantum enhanced receivers are endowed with resources to achieve higher sensitivities than conventional technologies. For application in optical communications, they provide improved discriminatory capabilities for multiple non-orthogonal quantum states. In this work, we propose and experimentally demonstrate a new decoding scheme for quadrature phase-shift encoded signals. Our receiver surpasses the standard quantum limit and outperforms all previously known non-adaptive detectors at low input powers. Unlike existing approaches, the receiver only exploits linear optical elements and on-off photo-detection. This circumvents the requirement for challenging feed-forward operations that limit communication transmission rates and can be readily implemented with current technology., Comment: 11 pages with 10 figures with appendices. Extended analysis. Accepted version

Generalized quantum measurements identifying non-orthogonal states without ambiguity often play an indispensable role in various quantum applications. For such unambiguous state discrimination scenario, we have a finite probability of obtaining inconclusive results and minimizing the probability of inconclusive results is of particular importance. In this paper, we experimentally demonstrate an adaptive generalized measurement that can unambiguously discriminate the quaternary phase-shift-keying coherent states with a near-optimal performance. Our scheme is composed of displacement operations, single photon detections and adaptive control of the displacements dependent on a history of photon detection outcomes. Our experimental results show a clear improvement of both a probability of conclusive results and a ratio of erroneous decision caused by unavoidable experimental imperfections over conventional static generalized measurements.

We determine the optimal method of discriminating and comparing quantum states from a certain class of multimode Gaussian states and their mixtures when arbitrary global Gaussian operations and general Gaussian measurements are allowed. We consider the so-called constant-$\hat{p}$ displaced states which include mixtures of multimode coherent states arbitrarily displaced along a common axis. We first show that no global or local Gaussian transformations or generalized Gaussian measurements can lead to a better discrimination method than simple homodyne measurements applied to each mode separately and classical postprocessing of the results. This result is applied to binary state comparison problems. We show that homodyne measurements, separately performed on each mode, are the best Gaussian measurement for binary state comparison. We further compare the performance of the optimal Gaussian strategy for binary coherent states comparison with these of non-Gaussian strategies using photon detections., Comment: 8 pages, 3 figures

Study Objectives: Sleep stage scoring is performed manually by sleep experts and is prone to subjective interpretation of scoring rules with low intra- and interscorer reliability. Many automatic systems rely on few small-scale databases for developing models, and generalizability to new datasets is thus unknown. We investigated a novel deep neural network to assess the generalizability of several large-scale cohorts. Methods: A deep neural network model was developed using 15684 polysomnography studies from five different cohorts. We applied four different scenarios: 1) impact of varying time-scales in the model; 2) performance of a single cohort on other cohorts of smaller, greater or equal size relative to the performance of other cohorts on a single cohort; 3) varying the fraction of mixed-cohort training data compared to using single-origin data; and 4) comparing models trained on combinations of data from 2, 3, and 4 cohorts. Results: Overall classification accuracy improved with increasing fractions of training data (0.25$\%$: 0.782 $\pm$ 0.097, 95$\%$ CI [0.777-0.787]; 100$\%$: 0.869 $\pm$ 0.064, 95$\%$ CI [0.864-0.872]), and with increasing number of data sources (2: 0.788 $\pm$ 0.102, 95$\%$ CI [0.787-0.790]; 3: 0.808 $\pm$ 0.092, 95$\%$ CI [0.807-0.810]; 4: 0.821 $\pm$ 0.085, 95$\%$ CI [0.819-0.823]). Different cohorts show varying levels of generalization to other cohorts. Conclusions: Automatic sleep stage scoring systems based on deep learning algorithms should consider as much data as possible from as many sources available to ensure proper generalization. Public datasets for benchmarking should be made available for future research., Comment: Author's original version. This article has been accepted for publication in SLEEP published by Oxford University Press

Global rice production systems face two opposing challenges: the need to increase production to accommodate the world's growing population while simultaneously reducing greenhouse gas (GHG) emissions. Adaptations to drainage regimes are one of the most promising options for methane mitigation in rice production. Whereas several studies have focused on mid-season drainage (MD) to mitigate GHG emissions, early-season drainage (ED) varying in timing and duration has not been extensively studied. However, such ED periods could potentially be very effective since initial available Carbon levels (and thereby the potential for methanogenesis) can be very high in paddy systems with rice straw incorporation. This study tested the effectiveness of seven drainage regimes varying in their timing and duration (combinations of ED and MD) to mitigate methane (CH4) and nitrous oxide (N2O) emissions in a 101-day growth chamber experiment. Emissions were considerably reduced by early-season drainage compared to both conventional continuous flooding (CF) and the MD drainage regime. The results suggest that ED + MD drainage may have the potential to reduce methane emissions and yield-scaled global warming potential by 85 to 90% compared to CF and by 75 to 77% compared to MD only. A combination of (short or long) ED drainage and one MD drainage episode was found to be the most effective in mitigating methane emissions without negatively affecting yield. In particular, compared with CF, the long early-season drainage treatments LE + SM and LE + LM significantly decreased yield scaled global warming potential by 85% and 87% respectively. This was associated with carbon being stabilised early in the season, thereby reducing available carbon for methanogenesis. Overall nitrous oxide emissions were small and not significantly affected by ED.

Flooded rice production is crucial to global food security, but there are associated environmental concerns. In particular, it is a significant source of methane and nitrous oxide emissions and a large consumer of water resources, while arsenic, cadmium and lead levels in the grain are a serious health concern. There is also a tendency to use more organic fertilisers to close nutrient cycles, posing a threat of even higher greenhouse gas emissions and grain arsenic levels. It has been shown that alternate wetting and drying (AWD) water management reduces both water use and greenhouse gas emissions, but success at maintaining yields varies. This study tested the effect of early AWD (e-AWD) versus continuous flooding (CF) water management practices on grain yields, greenhouse gas emissions and grain arsenic, cadmium and lead levels in a split plot field experiment with organic fertilisers under organic management. The e-AWD water regime showed no difference in yield for the organic treatments. Yields significantly increased by 5 to 16 percent in the combination treatments. Root biomass and root length increased in the e-AWD treatments up to 72 and 41 percent, respectively. The e-AWD water regime reduced seasonal methane emissions by 71 to 85 percent for organic treatments and by 51 to 76 percent for combination treatments; this was linked to a 15 to 47 percent reduction in dissolved organic carbon. Nitrous oxide emissions increased by 23 to 305 percent but accounted for less than 20 percent of global warming potential. Area and yield scaled global warming potentials were reduced by 67 to 83 percent. The e-AWD regime altered soil redox potentials, resulting in a reduction in grain arsenic and lead concentrations of up to 66 % and 73 % respectively. Grain cadmium levels were also reduced up to 33 % in organic treatments.

Due to its unique scalability potential, continuous variable quantum optics is a promising platform for large scale quantum computing. In particular, very large cluster states with a two-dimensional topology that are suitable for universal quantum computing and quantum simulation can be readily generated in a deterministic manner, and routes towards fault-tolerance via bosonic quantum error-correction are known. In this article we propose a complete measurement-based quantum computing architecture for the implementation of a universal set of gates on the recently generated two-dimensional cluster states [1,2]. We analyze the performance of the various quantum gates that are executed in these cluster states as well as in other two-dimensional cluster states (the bilayer-square lattice and quad-rail lattice cluster states [3,4]) by estimating and minimizing the associated stochastic noise addition as well as the resulting gate error probability. We compare the four different states and find that, although they all allow for universal computation, the quad-rail lattice cluster state performs better than the other three states which all exhibit similar performance.

Datasets in sleep science present challenges for machine learning algorithms due to differences in recording setups across clinics. We investigate two deep transfer learning strategies for overcoming the channel mismatch problem for cases where two datasets do not contain exactly the same setup leading to degraded performance in single-EEG models. Specifically, we train a baseline model on multivariate polysomnography data and subsequently replace the first two layers to prepare the architecture for single-channel electroencephalography data. Using a fine-tuning strategy, our model yields similar performance to the baseline model (F1=0.682 and F1=0.694, respectively), and was significantly better than a comparable single-channel model. Our results are promising for researchers working with small databases who wish to use deep learning models pre-trained on larger databases., Comment: Accepted for presentation at EMBC2020

Attempts to scale up instructional interventions confront implementation challenges that mitigate their ultimate impact on teaching and learning. In this article, we argue that learning about adaptation during the design and implementation phases of reform is critical to the development of interventions that can be implemented with integrity at scale. Through analysis of data generated during a mathematics instructional coaching initiative, we examine the adaptations coaches made to diverse relational and organizational contexts. Findings from two studies of adaptation illustrate the need to attend to the extent to which adaptations are consistent with the core features of a reform. Based on our findings, we posit a generalizable model that supports evidence-based mutual adaptation. [This is the online first version of an article published in "American Educational Research Journal."]

Discrimination of coherent states beyond the standard quantum limit (SQL) is an important tasknot only for quantum information processing but also for optical coherent communication. In orderto optimize long distance optical fiber networks, it is of practical importance to develop a quantumreceiver beating the SQL and approaching the quantum bound at telecom wavelength. In this paper,we experimentally demonstrate a receiver beating the conventional SQL at telecom wavelength. Ourreceiver is composed of a displacement operation, a single photon counter and a real time adaptivefeedback operation. By using a high performance single photon detector operating at the telecomwavelength, we achieve a discrimination error beyond the SQL. The demonstration in the telecomband provides the first step important towards quantum and classical communication beyond theSQL using a coherent state alphabet, and we envision that the technology can be used for long-distance quantum key distribution, effective quantum state preparation and quantum estimation.

The four-component cat state represents a particularly useful quantum state for realizing fault-tolerant continuous variable quantum computing. While such encoding has been experimentally generated and employed in the microwave regime, the states have not yet been produced in the optical regime. Here we propose a simple linear optical circuit combined with photon counters for the generation of such optical four-component cat states. This work might pave the way for the first experimental generation of fault-tolerant optical continuous variable quantum codes., Comment: 5 pages, 5 figures

During the last 20 years, the advance of communication technologies has generated multiple exciting applications. However, classical cryptography, commonly adopted to secure current communication systems, can be jeopardized by the advent of quantum computers. Quantum key distribution (QKD) is a promising technology aiming to solve such a security problem. Unfortunately, current implementations of QKD systems show relatively low key rates, demand low channel noise and use ad hoc devices. In this work, we picture how to overcome the rate limitation by using a 37-core fibre to generate 2.86 Mbit/s per core that can be space multiplexed into the highest secret key rate of 105.7 Mbit/s to date. We also demonstrate, with off-the-shelf equipment, the robustness of the system by co-propagating a classical signal at 370 Gbit/s, paving the way for a shared quantum and classical communication network.

Quantum measurement is essential to both the foundations and practical applications of quantum information science. Among many possible models of quantum measurement, feedback measurements that dynamically update their physical structure are highly interesting due to their flexibility which enables a wide range of measurements that might otherwise be hard to implement. Here we investigate by detector tomography a measurement consisting of a displacement operation combined with photon detection followed by a real time feedback operation. We design the measurement in order to discriminate the superposition of vacuum and single photon states -- the single-rail qubit -- and find that it can discriminate the superposition states with a certainty of 96\%. Such a feedback-controlled photon counter will facilitate the realization of quantum information protocols with single-rail qubits as well as the non-locality test of certain entangled states.

Strongly squeezed light finds many important applications within the fields of quantum metrology, quantum communication and quantum computation. However, due to the bulkiness and complexity of most squeezed light sources of today, they are still not a standard tool in quantum optics labs. We have taken the first steps in realizing a compact, high-performance 1550 nm squeezing source based on commercially available fiber components combined with a free-space double-resonant parametric down-conversion source. The whole setup, including single-pass second-harmonic generation in a waveguide, fits on a small breadboard and produces 9.3 dB of squeezing at a 5 MHz sideband-frequency. The setup is currently limited by phase noise, but further optimization and development should allow for a 19" sized turn-key squeezing source capable of delivering more than 10 dB of squeezing.

Measurement-based quantum computation offers exponential computational speed-up via simple measurements on a large entangled cluster state. We propose and demonstrate a scalable scheme for the generation of photonic cluster states suitable for universal measurement-based quantum computation. We exploit temporal multiplexing of squeezed light modes, delay loops, and beam-splitter transformations to deterministically generate a cylindrical cluster state with a two-dimensional (2D) topological structure as required for universal quantum information processing. The generated state consists of more than 30000 entangled modes arranged in a cylindrical lattice with 24 modes on the circumference, defining the input register, and a length of 1250 modes, defining the computation depth. Our demonstrated source of 2D cluster states can be combined with quantum error correction to enable fault-tolerant quantum computation.

Networking plays a ubiquitous role in quantum technology. It is an integral part of quantum communication and has significant potential for upscaling quantum computer technologies that are otherwise not scalable. Recently, it was realized that sensing of multiple spatially distributed parameters may also benefit from an entangled quantum network. Here we experimentally demonstrate how sensing of an averaged phase shift among four distributed nodes benefits from an entangled quantum network. Using a four-mode entangled continuous variable (CV) state, we demonstrate deterministic quantum phase sensing with a precision beyond what is attainable with separable probes. The techniques behind this result can have direct applications in a number of primitives ranging from biological imaging to quantum networks of atomic clocks.

Much attention has been given to automatic sleep staging algorithms in past years, but the detection of discrete events in sleep studies is also crucial for precise characterization of sleep patterns and possible diagnosis of sleep disorders. We propose here a deep learning model for automatic detection and annotation of arousals and leg movements. Both of these are commonly seen during normal sleep, while an excessive amount of either is linked to disrupted sleep patterns, excessive daytime sleepiness impacting quality of life, and various sleep disorders. Our model was trained on 1,485 subjects and tested on 1,000 separate recordings of sleep. We tested two different experimental setups and found optimal arousal detection was attained by including a recurrent neural network module in our default model with a dynamic default event window (F1 = 0.75), while optimal leg movement detection was attained using a static event window (F1 = 0.65). Our work show promise while still allowing for improvements. Specifically, future research will explore the proposed model as a general-purpose sleep analysis model., Comment: Accepted for publication in 41st International Engineering in Medicine and Biology Conference (EMBC), July 23-27, 2019

Cortical arousals are transient events of disturbed sleep that occur spontaneously or in response to stimuli such as apneic events. The gold standard for arousal detection in human polysomnographic recordings (PSGs) is manual annotation by expert human scorers, a method with significant interscorer variability. In this study, we developed an automated method, the Multimodal Arousal Detector (MAD), to detect arousals using deep learning methods. The MAD was trained on 2,889 PSGs to detect both cortical arousals and wakefulness in 1 second intervals. Furthermore, the relationship between MAD-predicted labels on PSGs and next day mean sleep latency (MSL) on a multiple sleep latency test (MSLT), a reflection of daytime sleepiness, was analyzed in 1447 MSLT instances in 873 subjects. In a dataset of 1,026 PSGs, the MAD achieved a F1 score of 0.76 for arousal detection, while wakefulness was predicted with an accuracy of 0.95. In 60 PSGs scored by multiple human expert technicians, the MAD significantly outperformed the average human scorer for arousal detection with a difference in F1 score of 0.09. After controlling for other known covariates, a doubling of the arousal index was associated with an average decrease in MSL of 40 seconds ($\beta$ = -0.67, p = 0.0075). The MAD outperformed the average human expert and the MAD-predicted arousals were shown to be significant predictors of MSL, which demonstrate clinical validity the MAD., Comment: 40 pages, 13 figures, 9 tables