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Modulation conditioned on measurements on entangled photonic quantum states is a cornerstone technology of optical quantum information processing. Performing this task with low latency requires combining single-photon-level detectors with both electronic logic processing and optical modulation in close proximity. In the technologically relevant telecom wavelength band, detection of photonic quantum states is best performed with high-efficiency, low-noise, and high-speed detectors based on the photon-induced breakdown of superconductivity. Therefore, using these devices for feedforward requires mutual compatibility of all components under cryogenic conditions. Here, we demonstrate low-latency feedforward using a quasi-photon-number-resolved measurement on a quantum light source. Specifically, we use a multipixel superconducting nanowire single-photon detector, amplifier, logic, and an integrated electro-optic modulator in situ below 4K. We modulate the signal mode of a spontaneous parametric down-conversion source, conditional on a photon-number measurement of the idler mode, with a total latency of (23+/-3)ns. The photon-number discrimination actively manipulates the signal mode photon statistics, which is itself a central component in photonic quantum computing reliant on heralded single-photon sources. This represents an important benchmark for the fastest quantum photonic feedforward experiments comprising measurement, amplification, logic and modulation. This has direct applications in quantum computing, communication, and simulation protocols.

We present \textbf{H}ybrid-\textbf{A}utoregressive \textbf{IN}ference Tr\textbf{AN}sducers (HAINAN), a novel architecture for speech recognition that extends the Token-and-Duration Transducer (TDT) model. Trained with randomly masked predictor network outputs, HAINAN supports both autoregressive inference with all network components and non-autoregressive inference without the predictor. Additionally, we propose a novel semi-autoregressive inference paradigm that first generates an initial hypothesis using non-autoregressive inference, followed by refinement steps where each token prediction is regenerated using parallelized autoregression on the initial hypothesis. Experiments on multiple datasets across different languages demonstrate that HAINAN achieves efficiency parity with CTC in non-autoregressive mode and with TDT in autoregressive mode. In terms of accuracy, autoregressive HAINAN outperforms TDT and RNN-T, while non-autoregressive HAINAN significantly outperforms CTC. Semi-autoregressive inference further enhances the model's accuracy with minimal computational overhead, and even outperforms TDT results in some cases. These results highlight HAINAN's flexibility in balancing accuracy and speed, positioning it as a strong candidate for real-world speech recognition applications.

Online social networks use recommender systems to suggest relevant information to their users in the form of personalized timelines. Studying how these systems expose people to information at scale is difficult to do as one cannot assume each user is subject to the same timeline condition and building appropriate evaluation infrastructure is costly. We show that a simple agent-based model where users have fixed preferences affords us the ability to compare different recommender systems (and thus different personalized timelines) in their ability to skew users' perception of their network. Importantly, we show that a simple greedy algorithm that constructs a feed based on network properties reduces such perception biases comparable to a random feed. This underscores the influence network structure has in determining the effectiveness of recommender systems in the social network context and offers a tool for mitigating perception biases through algorithmic feed construction.

Recommender systems underpin many of the personalized services in the online information & social media ecosystem. However, the assumptions in the research on content recommendations in domains like search, video, and music are often applied wholesale to domains that require a better understanding of why and how users interact with the systems. In this position paper we focus on social media and argue that personalized timelines have an added layer of complexity that is derived from the social nature of the platform itself. In particular, definitions of exposure fairness should be expanded to consider the social environment each user is situated in: how often a user is exposed to others is as important as who they get exposed to.

This paper presents a new method for training sequence-to-sequence models for speech recognition and translation tasks. Instead of the traditional approach of training models on short segments containing only lowercase or partial punctuation and capitalization (PnC) sentences, we propose training on longer utterances that include complete sentences with proper punctuation and capitalization. We achieve this by using the FastConformer architecture which allows training 1 Billion parameter models with sequences up to 60 seconds long with full attention. However, while training with PnC enhances the overall performance, we observed that accuracy plateaus when training on sequences longer than 40 seconds across various evaluation settings. Our proposed method significantly improves punctuation and capitalization accuracy, showing a 25% relative word error rate (WER) improvement on the Earnings-21 and Earnings-22 benchmarks. Additionally, training on longer audio segments increases the overall model accuracy across speech recognition and translation benchmarks. The model weights and training code are open-sourced though NVIDIA NeMo., Comment: Accepted at SLT 2024

At large scales, quantum systems may become advantageous over their classical counterparts at performing certain tasks. Developing tools to analyse these systems at the relevant scales, in a manner consistent with quantum mechanics, is therefore critical to benchmarking performance and characterising their operation. While classical computational approaches cannot perform like-for-like computations of quantum systems beyond a certain scale, classical high-performance computing (HPC) may nevertheless be useful for precisely these characterisation and certification tasks. By developing open-source customised algorithms using high-performance computing, we perform quantum tomography on a megascale quantum photonic detector covering a Hilbert space of $10^6$. This requires finding $10^8$ elements of the matrix corresponding to the positive operator valued measure (POVM), the quantum description of the detector, and is achieved in minutes of computation time. Moreover, by exploiting the structure of the problem, we achieve highly efficient parallel scaling, paving the way for quantum objects up to a system size of $10^{12}$ elements to be reconstructed using this method. In general, this shows that a consistent quantum mechanical description of quantum phenomena is applicable at everyday scales. More concretely, this enables the reconstruction of large-scale quantum sources, processes and detectors used in computation and sampling tasks, which may be necessary to prove their nonclassical character or quantum computational advantage., Comment: 31 pages, 8 figures

Cryogenic opto-electronic interconnects are gaining increasing interest as a means to control and read out cryogenic electronic components. The challenge is to achieve sufficient signal integrity with low heat load processing. In this context, we demonstrate the opto-electronic bias and readout of a commercial four-pixel superconducting nanowire single-photon detector array using a cryogenic photodiode and laser. We show that this approach has a similar system detection efficiency to a conventional bias. Furthermore, multi-pixel detection events are faithfully converted between the optical and electrical domain, which allows reliable extraction of amplitude multiplexed photon statistics. Our device has a passive heat dissipation of 2.6mW, maintains the signal rise time of 3ns, and operates in free-running (self-resetting) mode at a repetition rate of 600kHz. This demonstrates the potential of high-bandwidth, low noise, and low heat load opto-electronic interconnects for scalable cryogenic signal processing and transmission.

We experimentally demonstrate control of molecular rotation in bulk superfluid $^4\mathrm{He}$. Metastable helium dimers, $\mathrm{He}_2^*$, are rotationally excited by a periodic train of linearly polarized femtosecond laser pulses. We show that the degree of rotational excitation of $\mathrm{He}_2^*$ can be enhanced or suppressed by varying the period of the pulse train, whereas the directionality of molecular rotation can be controlled by the relative angle between the polarization vectors of pulses in the train. The experimental results are in agreement with numerical calculations, based on a simple model, in which $\mathrm{He}_2^*$ molecules do not interact with the superfluid.

We apply principal component analysis (PCA) to a set of electrical output signals from a commercially available superconducting nanowire single-photon detector (SNSPD) to investigate their photon-number-resolving capability. We find that the rising edge as well as the amplitude of the electrical signal have the most dependence on photon number. Accurately measuring the rising edge while simultaneously measuring the voltage of the pulse amplitude maximizes the photon-number resolution of SNSPDs. Using an optimal basis of principle components, we show unambiguous discrimination between one- and two-photon events, as well as partial resolution up to five photons. This expands the use-case of SNSPDs to photon-counting experiments, without the need of detector multiplexing architectures., Comment: 7 pages, 5 figures

Text normalization - the conversion of text from written to spoken form - is traditionally assumed to be an ill-formed task for language models. In this work, we argue otherwise. We empirically show the capacity of Large-Language Models (LLM) for text normalization in few-shot scenarios. Combining self-consistency reasoning with linguistic-informed prompt engineering, we find LLM based text normalization to achieve error rates around 40\% lower than top normalization systems. Further, upon error analysis, we note key limitations in the conventional design of text normalization tasks. We create a new taxonomy of text normalization errors and apply it to results from GPT-3.5-Turbo and GPT-4.0. Through this new framework, we can identify strengths and weaknesses of GPT-based TN, opening opportunities for future work., Comment: Accepted to ICASSP 2024

Decomposing complex unitary evolution into a series of constituent components is a cornerstone of practical quantum information processing. While the decompostion of an $n\times n$ unitary into a series of $2\times2$ subunitaries is well established (i.e. beamsplitters and phase shifters in linear optics), we show how this decomposition can be generalised into a series of $m\times m$ multimode devices, where $m>2$. If the cost associated with building each $m\times m$ multimode device is less than constructing with $\frac{m(m-1)}{2}$ individual $2\times 2$ devices, we show that the decomposition of large unitaries into $m\times m$ submatrices is is more resource efficient and exhibits a higher tolerance to errors, than its $2\times 2$ counterpart. This allows larger-scale unitaries to be constructed with lower errors, which is necessary for various tasks, not least Boson sampling, the quantum Fourier transform and quantum simulations.

A NEMA performance evaluation was conducted on the new General Electric (GE) digital Omni Legend PET-CT system with 32-cm extended field-of-view. This study marks the introduction of the first-ever commercially available clinical digital bismuth germanate technology. Testing was performed in accordance with the NEMA NU2-2018 standard. A comparison was made with the performance of two other commercial GE scanners with extended fields-of-view. A digital lutetium yttrium orthosilicate system (Discovery MI - 6 ring) and a non-digital bismuth germanate system (Discovery IQ). For the Omni assessment, the tangential, radial, and axial spatial resolutions at 1 cm radial offset were measured as 3.76 mm, 3.73 mm, and 4.25 mm FWHM. The total system sensitivity to a line source at the center was 44.36 cps/kBq. The peak NECR was 501 kcps at 17.8 kBq/mL. The scatter fraction at NECR peak was 35.48%, and the maximum count-rate error at and below NEC peak was 5.5%. Sphere contrast recovery coefficients were from 52% (10 mm) to 93% (37 mm). The system does not use time of flight; thus, no assessment of timing resolution was made. The PET-CT co-registration accuracy was 2.4 mm. The performance of the Omni Legend surpassed that of the Discovery MI on all NEMA tests, except for assessments of background variability (image noise). Time of flight is associated with inherent improvements in signal-to-noise ratio. In lieu of time of flight capabilities, the Omni provides software corrections in the form of a pre-trained neural network (trained on non-ToF to ToF). With such corrections, average performance is competitive when compared to ToF systems. Further validation is required to optimize clinical imaging protocols and hyperparameters associated with such software corrections and to examine the effect of non-linear corrections with varying target size, particularly for real world, clinical scans.

Superconducting nanowire single-photon detectors (SNSPDs) have been widely used to study the discrete nature of quantum states of light in the form of photon-counting experiments. We show that SNSPDs can also be used to study continuous variables of optical quantum states by performing homodyne detection at a bandwidth of $400~\mathrm{kHz}$. By measuring the interference of a continuous-wave field of a local oscillator with the field of the vacuum state using two SNSPDs, we show that the variance of the difference in count rates is linearly proportional to the photon flux of the local oscillator over almost five orders of magnitude. The resulting shot-noise clearance of $(46.0\pm1.1)~\mathrm{dB}$ is the highest reported clearance for a balanced optical homodyne detector, demonstrating their potential for measuring highly squeezed states in the continuous-wave regime. In addition, we measured a $\mathrm{CMRR}=22.4~\mathrm{dB}$. From the joint click counting statistics, we also measure the phase-dependent quadrature of a weak coherent state to demonstrate our device's functionality as a homodyne detector., Comment: 7 pages, 4 figures

Lithium niobate has emerged as a promising platform for integrated quantum optics, enabling efficient generation, manipulation, and detection of quantum states of light. However, integrating single-photon detectors requires cryogenic operating temperatures, since the best performing detectors are based on narrow superconducting wires. While previous studies have demonstrated the operation of quantum light sources and electro-optic modulators in LiNbO3 at cryogenic temperatures, the thermal transition between room temperature and cryogenic conditions introduces additional effects that can significantly influence device performance. In this paper, we investigate the generation of pyroelectric charges and their impact on the optical properties of lithium niobate waveguides when changing from room temperature to 25K, and vice versa. We measure the generated pyroelectric charge flow and correlate this with fast changes in the birefringence acquired through the Senarmont method. Both electrical and optical influence of the pyroelectric effect occurs predominantly at temperatures above 100K.

We demonstrate the generation of degenerate photon pairs from spontaneous parametric down-conversion in titanium in-diffused waveguides in lithium niobate at cryogenic temperatures. Since the phase-matching cannot be temperature tuned inside a cryostat, we rely on a precise empirical model of the refractive indices when fabricating a fixed poling period. We design the phase-matching properties of our periodic poling to enable signal and idler photons at (1559.3 $\pm$ 0.6) nm, and characterize the indistinguishability of our photons by performing a Hong-Ou-Mandel interference measurement. Despite the effects of photorefraction and pyroelectricity, which can locally alter the phase-matching, we achieve cryogenic indistinguishable photons within 1.5 nm to our design wavelength. Our results verify sufficient understanding and control of the cryogenic nonlinear process, which has wider implications when combining quasi-phase-matched nonlinear optical processes with other cryogenic photonic quantum technologies, such as superconducting detectors., Comment: 7 pages, 6 figures

Advanced electro-optic processing combines electrical control with optical modulation and detection. For quantum photonic applications these processes must be carried out at the single photon level with high efficiency and low noise. Integrated quantum photonics has made great strides achieving single photon manipulation by combining key components on integrated chips which are operated by external driving electronics. Nevertheless, electrical interconnects between driving electronics and the electro-optic components, some of which require cryogenic operating conditions, can introduce parasitic effects. Here we show an all-optical interface which simultaneously delivers the operation power to, and extracts the measurement signal from, an advanced photonic circuit, namely, bias and readout of a superconducting nanowire single photon detector (SNSPD) on a single stage in a 1K cryostat. To do so, we supply all power for the single photon detector, output signal conditioning, and electro-optic readout using optical interconnects alone, thereby fully decoupling the cryogenic circuitry from the external environment. This removes the need to heatsink electrical connections, and potentially offers low-loss, high-bandwidth signal processing. This method opens the possibility to operate other advanced electrically decoupled photonic circuits such as optical control and readout of superconducting circuits, and feedforward for photonic quantum computing.