Niranjan Sridhar - Academia.edu (original) (raw)

Papers by Niranjan Sridhar

Photon-number-revolving (PNR) detection allows the direct measurement of the Wigner quasiprobabil... more Photon-number-revolving (PNR) detection allows the direct measurement of the Wigner quasiprobability distribution of an optical mode without the need for numerically processing an inverse Radon transform [K. Banaszek and K. Wódkiewicz, Phys. Rev. Lett. 76, 4344 (1996)]. In this work, we reproduced the seminal experiment of Banaszek et al. [Phys. Rev. A 60, 674 (1999)] of quantum tomography of a pure coherent state, and of a statistical mixture thereof, and extended it to the more general case of photon fluxes with much more than one photon per detection time. This was made possible by the use of a superconducting transition-edge sensor to perform PNR detection from 0 to 5 photons at 1064 nm, at ∼ 70% system efficiency and with no dead time. We detail signal acquisition and detection efficiency and discuss prospects for applying such quantum tomography to non-Gaussian states.

arXiv (Cornell University), Apr 26, 2023

Automated and accurate human activity recognition (HAR) using body-worn sensors enables practical... more Automated and accurate human activity recognition (HAR) using body-worn sensors enables practical and cost efficient remote monitoring of Activity of Daily Living (ADL), which are shown to provide clinical insights across multiple therapeutic areas. Development of accurate algorithms for human activity recognition (HAR) is hindered by the lack of large real-world labeled datasets. Furthermore, algorithms seldom work beyond the specific sensor on which they are prototyped, prompting debate about whether accelerometer-based HAR is even possible [Tong et al., 2020]. Here we develop a 6-class HAR model with strong performance when evaluated on real-world datasets not seen during training. Our model is based on a frozen self-supervised representation learned on a large unlabeled dataset, combined with a shallow multi-layer perceptron with temporal smoothing. The model obtains in-dataset state-of-the art performance on the Capture24 dataset (κ = 0.86). Outof-distribution (OOD) performance is κ = 0.7, with both the representation and the perceptron models being trained on data from a different sensor. This work represents a key step towards device-agnostic HAR models, which can help contribute to increased standardization of model evaluation in the HAR field. * Equal contributions Preprint. Under review.

As we all know the usage of private vehicles is much greater than the usage of public vehicles. 3... more As we all know the usage of private vehicles is much greater than the usage of public vehicles. 30% of Indians out of 48% use private vehicles, remaining 18% use public services. In India 1, 46,000 people die due to road accidents and 1,14,0000 people die due to collision of two vehicles. India is losing 52 billion US dollars per year due to accidents. Life of a person is important than money. Much of the accidents occur due to dunk and drive, not wearing seat belt and High beam of upfront vehicles. If these things are solved then 65% of the road accidents can be reduced. To minimize all these things we planned to build a system which detects the seat belt and alcohol consumption of the driver. The vehicle can be started only after alcohol test and after wearing the seat belt. If the high beam of the upfront vehicle is irritating the driver then he can simply turn the high beam to low beam of upfront vehicle by dimming and dipping the light of his vehicle for 2 times.

medRxiv (Cold Spring Harbor Laboratory), Apr 17, 2023

Conventional histopathology involves expensive and labor intensive processes that often consume t... more Conventional histopathology involves expensive and labor intensive processes that often consume tissue samples, rendering them unavailable for other analysis. We present a novel end-to-end workflow for pathology powered by hyperspectral microscopy and deep learning. First, we developed a custom hyperspectral microscope to non-destructively image the autofluorescence of unstained tissue sections. We then train a deep learning model to use the autofluorescence to generate virtual histological stains, which avoids the cost and variability of chemical staining procedures and conserves tissue samples. We showed that the virtual images reproduce the histological features present in the real stained images using a randomized nonalcoholic steatohepatitis (NASH) scoring comparison study where both real and virtual stains are scored by pathologists. The test showed moderate to good concordance between pathologists' scoring on corresponding real and virtual stains. Finally, we developed deep learning-based models for automated NASH clinical research network (NASH CRN) score prediction. We showed that the end-to-end automated pathology platform is comparable to pathologists for NASH CRN scoring when evaluated against the expert pathologist consensus scores. This study provides proof of concept for this virtual staining strategy, which could improve cost, efficiency, and reliability in pathology, and enable novel approaches to spatial biology research.

Physical Review A, 1999

We report a direct measurement of the Wigner function characterizing the quantum state of a light... more We report a direct measurement of the Wigner function characterizing the quantum state of a light mode. The experimental scheme is based on the representation of the Wigner function as an expectation value of a displaced photon number parity operator. This allowed us to scan the phase space point-by-point, and obtain the complete Wigner function without using any numerical reconstruction algorithms.

Cornell University - arXiv, Dec 22, 2021

Measures of Activity of Daily Living (ADL) are an important indicator of overall health but diffi... more Measures of Activity of Daily Living (ADL) are an important indicator of overall health but difficult to measure in-clinic. Automated and accurate human activity recognition (HAR) using wrist-worn accelerometers enables practical and cost efficient remote monitoring of ADL. Key obstacles in developing high quality HAR is the lack of large labeled datasets and the performance loss when applying models trained on small curated datasets to the continuous stream of heterogeneous data in real-life. In this work we design a self-supervised learning paradigm to create a robust representation of accelerometer data that can generalize across devices and subjects. We demonstrate that this representation can separate activities of daily living and achieve strong HAR accuracy (on multiple benchmark datasets) using very few labels. We also propose a segmentation algorithm which can identify segments of salient activity and boost HAR accuracy on continuous real-life data.

This document provides supplementary information to "State-independent quantum tomography by phot... more This document provides supplementary information to "State-independent quantum tomography by photon-number-resolving measurements," https://doi.org/10.1364/OPTICA.6.001356. In Section 1 we provide an overview of our cavity-enhanced single-photon source. Section 2 discusses experimental calibrations and a model for loss analysis in the experiment.

Conference on Lasers and Electro-Optics, 2019

A single-photon state was generated by heralding cavity-enhanced spontaneous parametric downconve... more A single-photon state was generated by heralding cavity-enhanced spontaneous parametric downconversion in a PPKTP optical parametric oscillator. The Wigner distribution was reconstructed by quantum state tomography, using photon-number-resolving measurements with a system efficiency of 58±2%. © 2019 The Author(s)

2018 Computing in Cardiology Conference (CinC), 2018

This work evaluates the performance of convolutional and recurrent neural networks on the task of... more This work evaluates the performance of convolutional and recurrent neural networks on the task of detecting Respiratory Effort-Related Arousals (RERAs). Feature time-series were extracted from EEG, EOG, CHIN, CHEST, ABDOMINAL, AIRFLOW, SaO2, and ECG and normalized on a per-subject basis. Next, multi-timescale windows from these time-series were associated with the presence or absence of RERA during the window forming the data for model training. More than 1 million RERA-windows and 17 million no-arousal windows were used for model training, and more than 200K RERA-windows and 4 million no-arousal windows were used for testing and validation. Google Cloud ML Engine was used to select model hyperparameters using the validation data. The model with the best hyperparameter combination evaluated on the test set achieved an AUC-ROC score of 0.916 and AUC-PR score 0.573.

Sleep, 2021

Heart rate is well-known to be modulated by sleep stages. If clinically useful sleep scoring can ... more Heart rate is well-known to be modulated by sleep stages. If clinically useful sleep scoring can be performed using only cardiac rhythms, then existing medical and consumer-grade devices that can measure heart rate can enable low-cost sleep evaluations. We trained a neural network which uses dilated convolutional blocks to learn both local and long range features of heart rate extracted from ECG R-wave timing to predict for every non-overlapping 30s epoch of the input the probabilities of the epoch being in one of four classes—wake, light sleep, deep sleep or REM. The largest probability is chosen as the network’s class prediction and used to form the hypnogram. We used the Sleep Heart Health Study (SHHS) and Multi-Ethnic Study of Atherosclerosis Study (MESA) and Physionet Computing in Cardiology (CinC) dataset (over 10000 nights) for training and evaluation. Then we deployed the algorithm on PPG based heart rate measured by a wrist-worn device worn by subjects in a free-living sett...

Quantum Communications and Quantum Imaging XVII, 2019

Quantum state engineering and state characterization is a key task in quantum information process... more Quantum state engineering and state characterization is a key task in quantum information processing in both discrete and continuous variable systems in the optical domain. In particular, quantum states with non-Gaussian (i.e., non-positive) Wigner quasiprobability distribution functions are crucial to universal, fault-tolerant quantum computing with continuous variables. In this talk, we present our recent results on single-photon Fock state tomography using Photon-Number-Resolving (PNR) measurements. We generated a highly pure narrow-band single-photon Fock state by heralding cavity-enhanced spontaneous-parametric-downconversion from a PPKTP optical parametric oscillator. The Wigner function was reconstructed with photon statistics obtained using superconducting transition-edge sensors with an overall system efficiency of 58(2)%. We then discuss quantum state engineering for pure displaced single-photon Fock states, optical cat states, and approximate GKP states using coherent sta...

arXiv: Quantum Physics, 2019

The Wigner quasiprobability distribution of a narrowband single-photon state was reconstructed by... more The Wigner quasiprobability distribution of a narrowband single-photon state was reconstructed by quantum state tomography using photon-number-resolving measurements with transition-edge sensors (TES) at system efficiency 58(2)%. This method makes no assumptions on the nature of the measured state, save for the limitation on photon flux imposed by the TES. Negativity of the Wigner function was observed in the raw data without any inference or correction for decoherence.

npj Digital Medicine, 2020

An amendment to this paper has been published and can be accessed via a link at the top of the pa... more An amendment to this paper has been published and can be accessed via a link at the top of the paper.

npj Digital Medicine, 2020

Clinical sleep evaluations currently require multimodal data collection and manual review by huma... more Clinical sleep evaluations currently require multimodal data collection and manual review by human experts, making them expensive and unsuitable for longer term studies. Sleep staging using cardiac rhythm is an active area of research because it can be measured much more easily using a wide variety of both medical and consumer-grade devices. In this study, we applied deep learning methods to create an algorithm for automated sleep stage scoring using the instantaneous heart rate (IHR) time series extracted from the electrocardiogram (ECG). We trained and validated an algorithm on over 10,000 nights of data from the Sleep Heart Health Study (SHHS) and Multi-Ethnic Study of Atherosclerosis (MESA). The algorithm has an overall performance of 0.77 accuracy and 0.66 kappa against the reference stages on a held-out portion of the SHHS dataset for classifying every 30 s of sleep into four classes: wake, light sleep, deep sleep, and rapid eye movement (REM). Moreover, we demonstrate that th...

Optica, 2019

The Wigner quasiprobability distribution of a narrowband single-photon state was reconstructed by... more The Wigner quasiprobability distribution of a narrowband single-photon state was reconstructed by quantum state tomography using photon-number-resolving measurements with transition-edge sensors (TES) at system efficiency 58(2)%. This method makes no assumptions on the nature of the measured state, save for the limitation on photon flux imposed by the TES. Negativity of the Wigner function was observed in the raw data without any inference or correction for decoherence.

Department of Physics Doctor of Philosophy by Niranjan Sridhar My doctoral thesis details my rese... more Department of Physics Doctor of Philosophy by Niranjan Sridhar My doctoral thesis details my research on two major projects. Both projects investigate the correspondence between the discrete (photon number) and the continuous (quadrature) representation of optical states. Understanding the relationships between the two representations is very important for the investigation of quantum physics phenomena. Moreover it has significant applications in quantum computing where it is found that discrete-variable bases are easier to error-correct but some continuous-variable architectures are more scalable. Research in the connection between the two representation might help develop new architectures for scalable fault-tolerant quantum computing.

Physical Review A, 2016

One-way quantum computing is experimentally appealing because it requires only local measurements... more One-way quantum computing is experimentally appealing because it requires only local measurements on an entangled resource called a cluster state. Record-size, but non-universal, continuous-variable cluster states were recently demonstrated separately in the time and frequency domains. We propose to combine these approaches into a scalable architecture in which a single optical parametric oscillator and simple interferometer entangle up to (3 × 10 3 frequencies) × (unlimited number of temporal modes) into a new and computationally universal continuous-variable cluster state. We introduce a generalized measurement protocol to enable improved computational performance on the new entanglement resource.

Bulletin of the American Physical Society, Jun 9, 2015

On the heels of the experimental demonstrations of record-scale one-dimensional cluster-state ent... more On the heels of the experimental demonstrations of record-scale one-dimensional cluster-state entanglement-suitable for implementing single-qumode quantum computing gates-in the time domain [S. Yokoyama et al., Nat. Photon. 7, 982 (2013)] and the frequency domain [M. Chen et al., Phys. Rev. Lett. 112, 120505 (2014)], we show here that both degrees of freedom can be combined to generate a two-dimensional square-grid cluster-statesuitable for universal quantum computing-from a single optical parametric oscillator. This method, the most compact yet, has the potential to reach 10 9 entangled qumodes, based on the current state of the art.

Photon-number-revolving (PNR) detection allows the direct measurement of the Wigner quasiprobabil... more Photon-number-revolving (PNR) detection allows the direct measurement of the Wigner quasiprobability distribution of an optical mode without the need for numerically processing an inverse Radon transform [K. Banaszek and K. Wódkiewicz, Phys. Rev. Lett. 76, 4344 (1996)]. In this work, we reproduced the seminal experiment of Banaszek et al. [Phys. Rev. A 60, 674 (1999)] of quantum tomography of a pure coherent state, and of a statistical mixture thereof, and extended it to the more general case of photon fluxes with much more than one photon per detection time. This was made possible by the use of a superconducting transition-edge sensor to perform PNR detection from 0 to 5 photons at 1064 nm, at ∼ 70% system efficiency and with no dead time. We detail signal acquisition and detection efficiency and discuss prospects for applying such quantum tomography to non-Gaussian states.

arXiv (Cornell University), Apr 26, 2023

Automated and accurate human activity recognition (HAR) using body-worn sensors enables practical... more Automated and accurate human activity recognition (HAR) using body-worn sensors enables practical and cost efficient remote monitoring of Activity of Daily Living (ADL), which are shown to provide clinical insights across multiple therapeutic areas. Development of accurate algorithms for human activity recognition (HAR) is hindered by the lack of large real-world labeled datasets. Furthermore, algorithms seldom work beyond the specific sensor on which they are prototyped, prompting debate about whether accelerometer-based HAR is even possible [Tong et al., 2020]. Here we develop a 6-class HAR model with strong performance when evaluated on real-world datasets not seen during training. Our model is based on a frozen self-supervised representation learned on a large unlabeled dataset, combined with a shallow multi-layer perceptron with temporal smoothing. The model obtains in-dataset state-of-the art performance on the Capture24 dataset (κ = 0.86). Outof-distribution (OOD) performance is κ = 0.7, with both the representation and the perceptron models being trained on data from a different sensor. This work represents a key step towards device-agnostic HAR models, which can help contribute to increased standardization of model evaluation in the HAR field. * Equal contributions Preprint. Under review.

As we all know the usage of private vehicles is much greater than the usage of public vehicles. 3... more As we all know the usage of private vehicles is much greater than the usage of public vehicles. 30% of Indians out of 48% use private vehicles, remaining 18% use public services. In India 1, 46,000 people die due to road accidents and 1,14,0000 people die due to collision of two vehicles. India is losing 52 billion US dollars per year due to accidents. Life of a person is important than money. Much of the accidents occur due to dunk and drive, not wearing seat belt and High beam of upfront vehicles. If these things are solved then 65% of the road accidents can be reduced. To minimize all these things we planned to build a system which detects the seat belt and alcohol consumption of the driver. The vehicle can be started only after alcohol test and after wearing the seat belt. If the high beam of the upfront vehicle is irritating the driver then he can simply turn the high beam to low beam of upfront vehicle by dimming and dipping the light of his vehicle for 2 times.

medRxiv (Cold Spring Harbor Laboratory), Apr 17, 2023

Conventional histopathology involves expensive and labor intensive processes that often consume t... more Conventional histopathology involves expensive and labor intensive processes that often consume tissue samples, rendering them unavailable for other analysis. We present a novel end-to-end workflow for pathology powered by hyperspectral microscopy and deep learning. First, we developed a custom hyperspectral microscope to non-destructively image the autofluorescence of unstained tissue sections. We then train a deep learning model to use the autofluorescence to generate virtual histological stains, which avoids the cost and variability of chemical staining procedures and conserves tissue samples. We showed that the virtual images reproduce the histological features present in the real stained images using a randomized nonalcoholic steatohepatitis (NASH) scoring comparison study where both real and virtual stains are scored by pathologists. The test showed moderate to good concordance between pathologists' scoring on corresponding real and virtual stains. Finally, we developed deep learning-based models for automated NASH clinical research network (NASH CRN) score prediction. We showed that the end-to-end automated pathology platform is comparable to pathologists for NASH CRN scoring when evaluated against the expert pathologist consensus scores. This study provides proof of concept for this virtual staining strategy, which could improve cost, efficiency, and reliability in pathology, and enable novel approaches to spatial biology research.

Physical Review A, 1999

We report a direct measurement of the Wigner function characterizing the quantum state of a light... more We report a direct measurement of the Wigner function characterizing the quantum state of a light mode. The experimental scheme is based on the representation of the Wigner function as an expectation value of a displaced photon number parity operator. This allowed us to scan the phase space point-by-point, and obtain the complete Wigner function without using any numerical reconstruction algorithms.

Cornell University - arXiv, Dec 22, 2021

Measures of Activity of Daily Living (ADL) are an important indicator of overall health but diffi... more Measures of Activity of Daily Living (ADL) are an important indicator of overall health but difficult to measure in-clinic. Automated and accurate human activity recognition (HAR) using wrist-worn accelerometers enables practical and cost efficient remote monitoring of ADL. Key obstacles in developing high quality HAR is the lack of large labeled datasets and the performance loss when applying models trained on small curated datasets to the continuous stream of heterogeneous data in real-life. In this work we design a self-supervised learning paradigm to create a robust representation of accelerometer data that can generalize across devices and subjects. We demonstrate that this representation can separate activities of daily living and achieve strong HAR accuracy (on multiple benchmark datasets) using very few labels. We also propose a segmentation algorithm which can identify segments of salient activity and boost HAR accuracy on continuous real-life data.

This document provides supplementary information to "State-independent quantum tomography by phot... more This document provides supplementary information to "State-independent quantum tomography by photon-number-resolving measurements," https://doi.org/10.1364/OPTICA.6.001356. In Section 1 we provide an overview of our cavity-enhanced single-photon source. Section 2 discusses experimental calibrations and a model for loss analysis in the experiment.

Conference on Lasers and Electro-Optics, 2019

A single-photon state was generated by heralding cavity-enhanced spontaneous parametric downconve... more A single-photon state was generated by heralding cavity-enhanced spontaneous parametric downconversion in a PPKTP optical parametric oscillator. The Wigner distribution was reconstructed by quantum state tomography, using photon-number-resolving measurements with a system efficiency of 58±2%. © 2019 The Author(s)

2018 Computing in Cardiology Conference (CinC), 2018

This work evaluates the performance of convolutional and recurrent neural networks on the task of... more This work evaluates the performance of convolutional and recurrent neural networks on the task of detecting Respiratory Effort-Related Arousals (RERAs). Feature time-series were extracted from EEG, EOG, CHIN, CHEST, ABDOMINAL, AIRFLOW, SaO2, and ECG and normalized on a per-subject basis. Next, multi-timescale windows from these time-series were associated with the presence or absence of RERA during the window forming the data for model training. More than 1 million RERA-windows and 17 million no-arousal windows were used for model training, and more than 200K RERA-windows and 4 million no-arousal windows were used for testing and validation. Google Cloud ML Engine was used to select model hyperparameters using the validation data. The model with the best hyperparameter combination evaluated on the test set achieved an AUC-ROC score of 0.916 and AUC-PR score 0.573.

Sleep, 2021

Heart rate is well-known to be modulated by sleep stages. If clinically useful sleep scoring can ... more Heart rate is well-known to be modulated by sleep stages. If clinically useful sleep scoring can be performed using only cardiac rhythms, then existing medical and consumer-grade devices that can measure heart rate can enable low-cost sleep evaluations. We trained a neural network which uses dilated convolutional blocks to learn both local and long range features of heart rate extracted from ECG R-wave timing to predict for every non-overlapping 30s epoch of the input the probabilities of the epoch being in one of four classes—wake, light sleep, deep sleep or REM. The largest probability is chosen as the network’s class prediction and used to form the hypnogram. We used the Sleep Heart Health Study (SHHS) and Multi-Ethnic Study of Atherosclerosis Study (MESA) and Physionet Computing in Cardiology (CinC) dataset (over 10000 nights) for training and evaluation. Then we deployed the algorithm on PPG based heart rate measured by a wrist-worn device worn by subjects in a free-living sett...

Quantum Communications and Quantum Imaging XVII, 2019

Quantum state engineering and state characterization is a key task in quantum information process... more Quantum state engineering and state characterization is a key task in quantum information processing in both discrete and continuous variable systems in the optical domain. In particular, quantum states with non-Gaussian (i.e., non-positive) Wigner quasiprobability distribution functions are crucial to universal, fault-tolerant quantum computing with continuous variables. In this talk, we present our recent results on single-photon Fock state tomography using Photon-Number-Resolving (PNR) measurements. We generated a highly pure narrow-band single-photon Fock state by heralding cavity-enhanced spontaneous-parametric-downconversion from a PPKTP optical parametric oscillator. The Wigner function was reconstructed with photon statistics obtained using superconducting transition-edge sensors with an overall system efficiency of 58(2)%. We then discuss quantum state engineering for pure displaced single-photon Fock states, optical cat states, and approximate GKP states using coherent sta...

arXiv: Quantum Physics, 2019

The Wigner quasiprobability distribution of a narrowband single-photon state was reconstructed by... more The Wigner quasiprobability distribution of a narrowband single-photon state was reconstructed by quantum state tomography using photon-number-resolving measurements with transition-edge sensors (TES) at system efficiency 58(2)%. This method makes no assumptions on the nature of the measured state, save for the limitation on photon flux imposed by the TES. Negativity of the Wigner function was observed in the raw data without any inference or correction for decoherence.

npj Digital Medicine, 2020

An amendment to this paper has been published and can be accessed via a link at the top of the pa... more An amendment to this paper has been published and can be accessed via a link at the top of the paper.

npj Digital Medicine, 2020

Clinical sleep evaluations currently require multimodal data collection and manual review by huma... more Clinical sleep evaluations currently require multimodal data collection and manual review by human experts, making them expensive and unsuitable for longer term studies. Sleep staging using cardiac rhythm is an active area of research because it can be measured much more easily using a wide variety of both medical and consumer-grade devices. In this study, we applied deep learning methods to create an algorithm for automated sleep stage scoring using the instantaneous heart rate (IHR) time series extracted from the electrocardiogram (ECG). We trained and validated an algorithm on over 10,000 nights of data from the Sleep Heart Health Study (SHHS) and Multi-Ethnic Study of Atherosclerosis (MESA). The algorithm has an overall performance of 0.77 accuracy and 0.66 kappa against the reference stages on a held-out portion of the SHHS dataset for classifying every 30 s of sleep into four classes: wake, light sleep, deep sleep, and rapid eye movement (REM). Moreover, we demonstrate that th...

Optica, 2019

The Wigner quasiprobability distribution of a narrowband single-photon state was reconstructed by... more The Wigner quasiprobability distribution of a narrowband single-photon state was reconstructed by quantum state tomography using photon-number-resolving measurements with transition-edge sensors (TES) at system efficiency 58(2)%. This method makes no assumptions on the nature of the measured state, save for the limitation on photon flux imposed by the TES. Negativity of the Wigner function was observed in the raw data without any inference or correction for decoherence.

Department of Physics Doctor of Philosophy by Niranjan Sridhar My doctoral thesis details my rese... more Department of Physics Doctor of Philosophy by Niranjan Sridhar My doctoral thesis details my research on two major projects. Both projects investigate the correspondence between the discrete (photon number) and the continuous (quadrature) representation of optical states. Understanding the relationships between the two representations is very important for the investigation of quantum physics phenomena. Moreover it has significant applications in quantum computing where it is found that discrete-variable bases are easier to error-correct but some continuous-variable architectures are more scalable. Research in the connection between the two representation might help develop new architectures for scalable fault-tolerant quantum computing.

Physical Review A, 2016

One-way quantum computing is experimentally appealing because it requires only local measurements... more One-way quantum computing is experimentally appealing because it requires only local measurements on an entangled resource called a cluster state. Record-size, but non-universal, continuous-variable cluster states were recently demonstrated separately in the time and frequency domains. We propose to combine these approaches into a scalable architecture in which a single optical parametric oscillator and simple interferometer entangle up to (3 × 10 3 frequencies) × (unlimited number of temporal modes) into a new and computationally universal continuous-variable cluster state. We introduce a generalized measurement protocol to enable improved computational performance on the new entanglement resource.

Bulletin of the American Physical Society, Jun 9, 2015

On the heels of the experimental demonstrations of record-scale one-dimensional cluster-state ent... more On the heels of the experimental demonstrations of record-scale one-dimensional cluster-state entanglement-suitable for implementing single-qumode quantum computing gates-in the time domain [S. Yokoyama et al., Nat. Photon. 7, 982 (2013)] and the frequency domain [M. Chen et al., Phys. Rev. Lett. 112, 120505 (2014)], we show here that both degrees of freedom can be combined to generate a two-dimensional square-grid cluster-statesuitable for universal quantum computing-from a single optical parametric oscillator. This method, the most compact yet, has the potential to reach 10 9 entangled qumodes, based on the current state of the art.