Featured Publications

Thin Film Lithium Niobate (TFLN)

Thin film lithium niobate (TFLN) is quickly becoming one of the most promising materials for next-generation telecommunication devices as well as integrated photonics, which will enable numerous future technologies such as quantum computing, quantum sensing, high-frequency modulators, and LiDAR. In addition...

May 15, 2024

Full Policy Paper

An Open Quantum System for Discrete Optimization

L. Nguyen, W. Dyk, M.A. Miri, M. Begliarbekov, R.J. Rupert, S. Wu, N. Vrahoretis, I. Huang, P. Mahamuni, C.M. Delgado, D. Haycraft, Y.M. Sua, and Y. Huang

We propose a novel framework for photonic computing specialized in solving discrete optimization problems by leveraging the quantum Zeno effect. We demonstrate the efficiency of this computing paradigm within a hybrid quantum optimization machine.

© 2024

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A trustless decentralized protocol for distributed consensus of public quantum random numbers

Lac Nguyen, Jeevanandha Ramanathan, Michelle Mei Wang, Yong Meng Sua, and Yuping Huang

Quantum random number (QRNG) beacons distinguish themselves from classical counterparts by providing intrinsic unpredictability originating from the fundamental laws of quantum mechanics. Most demonstrations have focused on certifiable randomness generators to guarantee the public that their genuineness is independent from imperfect implementations. These efforts however do not benefit applications where multiple distrusted users need a common set of random numbers, as they must rely on the honesty of beacon owners. In this paper, we formally introduce a design and proof-of-principle experiment of the first consensus protocol producing QRNs in a decentralized environment (dQRNG). Such protocol allows N number of participants contribute in the generation process and publicly verify numbers they collect. Security of the protocol is guaranteed given (N-1) dishonest participants. Our method is thus suited for distribute systems that requires a bias-resistant, highly secure, and public-verifiable random beacon.

August 30, 2021


Carbon-dioxide absorption spectroscopy with solar photon counting and integrated lithium niobate micro-ring resonator

Jiuyi Zhang, Yong Meng Sua, Jia-Yang Chen, Jeevanandha Ramanathan, Chao Tang, Zhan Li, Yongxiang Hu, Yu-Ping Huang

We demonstrate a spectroscope using single-photon counters and a chip-integrated lithium niobate micro-ring filter to measure the atmospheric CO2 absorption spectrum passively. By thermo-optically sweeping the filter over 150 pm and referencing the resulting photon counts to a bypass channel, we sample the absorption spectrum at an ultrahigh-resolution of 6 pm. Incorporating it into a ground-based field system, we characterize the CO2 absorption through the atmosphere by counting the solar photons across the absorption line around 1572.02 nm, which agrees well with its transmission spectrum at standard atmospheric pressure. Our results highlight the potential of adopting integrated photonics and single-photon counting in remote sensing systems for high detection sensitivity, superior resolution, and significantly reduced size, weight, and power.

April 27, 2021


Compressive Non-Line-of-Sight Imaging with Deep Learning

Shenyu Zhu, Yong Meng Sua, Ting Bu, and Yu-Ping Huang

In non-line-of-sight (NLOS) imaging, the spatial information of hidden targets is reconstructed from the time-of-light (TOF) of the multiple bounced signal photons. The need for NLOS imagers to perform extensive scanning in the transverse spatial dimensions constrains the imaging speed and reconstruction quality while limiting their applications on static scenes. Utilizing a photon TOF histogram with picosecond temporal resolution, we develop compressive non-line-of-sight imaging enabled by deep learning. Two-dimensional images (32 × 32 pixels) of the NLOS targets can be reconstructed with superior reconstruction quality via a convolutional neural network (CNN), using significantly downscaled data (8 × 8 scanning points) at a downsampling ratio of 6.25% compared to the traditional methods. The CNN is end-to-end trained purely using simulated data but robust for image reconstruction with experiment data. Our results suggest that deep learning is effective for reducing the scanning points and total capture time towards scanningless NLOS imaging and videography.

March 28, 2023


Efficient reservoir computing using field programmable gate array and electro-optic modulation

Prajnesh Kumar, Mingwei Jin, Ting Bu, Santosh Kumar, and Yu-Ping Huang

We experimentally demonstrate a hybrid reservoir computing system consisting of an electro-optic modulator and field programmable gate array (FPGA). It implements delay lines and filters digitally for flexible dynamics and high connectivity, while supporting a large number of reservoir nodes. To evaluate the system’s performance and versatility, three benchmark tests are performed. The first is the 10th order Nonlinear Auto-Regressive Moving Average test (NARMA-10), where the predictions of 1000 and 25,000 steps yield impressively low normalized root mean square errors (NRMSE’s) of 0.142 and 0.148, respectively. Such accurate predictions over into the far future speak to its capability of large sample size processing, as enabled by the present hybrid design. The second is the Santa Fe laser data prediction, where a normalized mean square error (NMSE) of 6.73 × 103 is demonstrated. The third is the isolate spoken digit recognition, with a word error rate close to 0.34%. Accurate, versatile, flexibly reconfigurable, and capable of long-term prediction, this reservoir computing system could find a wealth of impactful applications in real-time information processing, weather forecasting, and financial analysis.

March 15, 2021


Interaction- and measurement-free quantum Zeno gates for universal computation with single-atom and single-photon qubits

Y. P. Huang and M. G. Moore

By extending the concept of interaction-free imaging to the few-atom level, we show that asymptotically on-demand interaction- and measurement-free quantum logic gates can be realized for both single-atom and single-photon qubits. The interaction-free feature suppresses the possibility of qubit decoherence via atomic spontaneous decay, while the elimination of measurements can significantly reduce errors arising from detector inefficiency. We present a general theory of universal quantum Zeno gates, and discuss physical implementations for quantum-information processing with individual atoms and photons. In addition, we propose a loss-tolerant protocol for long-distance quantum communication using quantum Zeno gates incorporated into a Mach-Zehnder interferometer. The efficiency of our Zeno gates is limited primarily by the imprecise control of atom-photon scattering and the finite number of feedback cycles N due to the limited finesse of the optical ring cavity. We find that the success probability scales as 1−O1/N, and for realistic parameters could be as high as 98.4%. Successful generation of atom-atom entanglement can be heralded by detection of the ancillary photon, upon which the fidelity scales as 1−O1/N2, with an achievable fidelity of 99.994%, which comes at the cost of reducing the success probability by the detector efficiency.

June 9, 2008


Near-infrared 3D imaging with upconversion detection

He Zhang, Santosh Kumar, Yong Meng Sua, Shenyu Zhu, And Yu-Ping Huang

We demonstrate a photon-sensitive, three-dimensional (3D) camera by active near-infrared illumination and fast time-of-flight gating. It uses picosecond pump pulses to selectively upconvert the backscattered photons according to their spatiotemporal modes via sum-frequency generation in a χ 2 nonlinear crystal, which are then detected by an electron-multiplying CCD with photon sensitive detection. As such, it achieves sub-millimeter depth resolution, exceptional noise suppression, and high detection sensitivity. Our results show that it can accurately reconstruct the surface profiles of occluded targets placed behind highly scattering and lossy obscurants of 14 optical depth (round trip), using only milliwatt illumination power. This technique may find applications in biomedical imaging, environmental monitoring, and wide-field light detection and ranging.

November 22, 2022


Noise-tolerant single photon sensitive three-dimensional imager

Patrick Rehain, Yong Meng Sua, Shenyu Zhu, Ivan Dickson, Bharathwaj Muthuswamy, Jeevanandha Ramanathan, Amin Shahverdi, and Yu-Ping Huang

Active imagers capable of reconstructing 3-dimensional (3D) scenes in the presence of strong background noise are highly desirable for many sensing and imaging applications. A key to this capability is the time-resolving photon detection that distinguishes true signal photons from the noise. To this end, quantum parametric mode sorting (QPMS) can achieve signal to noise exceeding by far what is possible with typical linear optics filters, with outstanding performance in isolating temporally and spectrally overlapping noise. Here, we report a QPMS-based 3D imager with exceptional detection sensitivity and noise tolerance. With only 0.0006 detected signal photons per pulse, we reliably reconstruct the 3D profile of an obscured scene, despite 34-fold spectral-temporally overlapping noise photons, within the 6 ps detection window (amounting to 113,000 times noise per 20 ns detection period). Our results highlight a viable approach to suppress background noise and measurement errors of single photon imager operation in high-noise environments.

February 17, 2020


Observation of distinct phase transitions in a nonlinear optical Ising machine

Santosh Kumar, Zhaotong Li, Ting Bu, Chunlei Qu, and Yuping Huang

Optical Ising machines promise to solve complex optimization problems with an optical hardware acceleration advantage. Here we study the ground state properties of a nonlinear optical Ising machine realized by spatial light modulator, Fourier optics, and second-harmonic generation in a nonlinear crystal. By tuning the ratio of the light intensities at the fundamental and second-harmonic frequencies, we experimentally observe two distinct ferromagnetic-to-paramagnetic phase transitions: a second-order phase transition where the magnetization changes to zero continuously and a first-order phase transition where the magnetization drops to zero abruptly as the effective temperature increases. Our experimental results are corroborated by a numerical simulation based on the Monte Carlo Metropolis-Hastings algorithm, and the physical mechanism for the distinct phase transitions can be understood with a mean-field theory. Our results showcase the flexibility of the nonlinear optical Ising machine, which may find potential applications in solving combinatorial optimization problems.

February 04, 2023


Programmable quantum random number generator without postprocessing

Lac Nguyen, Patrick Rehain, Yong Meng Sua, And Yu-Ping Huang

We demonstrate a viable source of unbiased quantum random numbers whose statistical properties can be arbitrarily programmed without the need for any postprocessing such as randomness distillation or distribution transformation. It is based on measuring the arrival time of single photons in shaped temporal modes that are tailored with an electro-optical modulator. We show that quantum random numbers can be created directly in customized probability distributions and pass all randomness tests of the NIST and Dieharder test suites without any randomness extraction. The min-entropies of such generated random numbers are measured close to the theoretical limits, indicating their near-ideal statistics and ultrahigh purity. Easy to implement and arbitrarily programmable, this technique can find versatile uses in a multitude of data analysis areas.

February 6, 2018


Quantum Parametric Mode Sorting: Beating the Time-Frequency Filtering

Amin Shahverdi, Yong Meng Sua, Lubna Tumeh, and Yu-Ping Huang

Selective detection of signal over noise is essential to measurement and signal processing. Time-frequency filtering has been the standard approach for the optimal detection of non-stationary signals. However, there is a fundamental tradeoff between the signal detection efficiency and the amount of undesirable noise detected simultaneously, which restricts its uses under weak signal yet strong noise conditions. Here, we demonstrate quantum parametric mode sorting based on nonlinear optics at the edge of phase matching to improve the tradeoff. By tailoring the nonlinear process in a commercial lithium-niobate waveguide through optical arbitrary waveform generation, we demonstrate highly selective detection of picosecond signals overlapping temporally and spectrally but in orthogonal time-frequency modes as well as against broadband noise, with performance well exceeding the theoretical limit of the optimized time-frequency filtering. We also verify that our device does not introduce any significant quantum noise to the detected signal and demonstrate faithful detection of pico-second single photons. Together, these results point to unexplored opportunities in measurement and signal processing under challenging conditions, such as photon-starving quantum applications.

July 26, 2017


Quantum Systems for Monte Carlo Methods and Applications to Fractional Stochastic Processes

Sebastian F. Tudor, Rupak Chatterjee, Lac Nguyen, Yuping Huang

Random numbers are a fundamental and useful resource in science and engineering with important applications in simulation, machine learning and cyber-security. Quantum systems can produce true random numbers because of the inherent randomness at the core of quantum mechanics. As a consequence, quantum random number generators are an efficient method to generate random numbers on a large scale. We study in this paper the applications of a viable source of unbiased quantum random numbers (QRNs) whose statistical properties can be arbitrarily programmed without the need for any post-processing and that pass all standard randomness tests of the NIST and Dieharder test suites without any randomness extraction. Our method is based on measuring the arrival time of single photons in shaped temporal modes that are tailored with an electro-optical modulator. The advantages of our QRNs are shown via two applications: simulation of a fractional Brownian motion, which is a non-Markovian process, and option pricing under the fractional SABR model where the stochastic volatility process is assumed to be driven by a fractional Brownian motion. The results indicate that using the same number of random units, our QRNs achieve greater accuracy than those produced by standard pseudo-random number generators. Moreover, we demonstrate the advantages of our method via an increase in computational speed, efficiency, and convergence.

October 11, 2018


Single-Photon Vibrometry

Patrick Rehain, Jeevanandha Ramanathan, Yong Meng Sua, Shenyu Zhu, Daniel Tafone, and Yu-Ping Huang

We propose and demonstrate a single-photon sensitive technique for optical vibrometry. It uses high speed photon counting to sample the modulated backscatter- ing from a vibrating target. Designed for remote vibration sensing with ultralow photon flux, we show that this technique can detect small displacements down to 110 nm and resolve vibration frequencies from DC up to several kilohertz, with less than 0.01 detected photons per pulse. This single-photon sensitive optical vibrometry may find important applications in acousto-optic sensing and imaging, especially in photon-starved environments.

March 9, 2021


Single-Pixel Pattern Recognition with Coherent Nonlinear Optics

Ting Bu, Santosh Kumar, He Zhang, Irwin Huang, And Yuping Huang

We propose and experimentally demonstrate a nonlinear-optics approach to pattern recognition with single-pixel imaging and deep neural network. It employs mode selective image up-conversion to project a raw image onto a set of coherent spatial modes, whereby its signature features are extracted nonlinear-optically. With 40 projection modes, the classification accuracy reaches a high value of 99.49% for the MNIST handwritten digit images, and up to 95.32% even when they are mixed with strong noise. Our experiment harnesses rich coherent processes in nonlinear optics for efficient machine learning, with potential applications in online classification of large-size images, fast lidar data analyses, complex pattern recognition, and so on.

October 7, 2020


Efficient quasi-phase-matched frequency conversion in a lithium niobate racetrack microresonator

Jia-Yang Chen, Zhao-Hui Ma, Yong Meng Sua, Zhan Li, Chao Tang, And Yu-Ping Huang

We demonstrate quasi-phase-matched frequency conversion in a chip-integrated lithium niobate microring resonator, whose normalized efficiency reaches 230,000%∕W or 10−6 per single photon.

September 18, 2019