Spectrally encoded parallel LiDAR driven by super-bunching light

Domain: physics
Published: 2025-12-26
Editorial outcome: major_revision

Abstract
Parallel light detection and ranging (LiDAR) is widely adopted for its low computational burden and rapid three-dimensional (3D) reconstruction. Yet, it remains constrained by inter-channel crosstalk and limited long-distance performance. Here, we introduce a spectrally encoded parallel LiDAR based on super-bunching light, exhibiting negligible cross-correlation between wavelengths, enabling quasi-orthogonal channel division without crosstalk. This approach supports robust parallel ranging, rapid and accurate 3D reconstruction, and effective target classification. Our scheme achieves high-precision ranging with errors as low as 4 mm and can detect targets moving at velocities as low as 5 mm/s. It further enables reliable ranging and 3D imaging beyond 40 m, with exceptional anti-interference performance, even when noise exceeds the echo signal by three orders of magnitude. Combining high precision, sensitivity, long-range detection, dynamic target acquisition, precise 3D reconstruction, and robust anti-interference, our LiDAR offers significant potential for enhancing environmental perception technologies.

Introduction
Light detection and ranging (LiDAR) form the backbone of environmental perception1–4, supporting autonomous capabilities across a wide range of unmanned systems, from autonomous vehicles5–9 and drones to AI-integrated robotics4,5. In contrast to vision-only approaches, LiDAR enables high-precision, real-time three-dimensional (3D) environmental mapping10–14 with low computational overhead15–17, ensuring the safety and reliability of autonomous systems. Leveraging its advanced optical ranging and recognition capabilities, LiDAR enhances the operational robustness of autonomous systems18,19, thereby supporting their survivability in complex and rapidly changing environments. A key trend in current LiDAR technology is the adoption of parallelized architectures20,21, which enable multi-channel simultaneous measurements22,23. On the one hand, parallel LiDAR achieves 3D imaging through one-dimensional (1D) scanning24–26, significantly improving acquisition rates and angular resolution12,27. On the other hand, the feature of negligible reliance on beam-steering mechanisms28–30 ensures robust vibration resistance12,19. Enabled by rapid scanning and precise imaging, parallel LiDAR will catalyze transformative progress in the field of advanced autonomous systems31,32.

Notwithstanding, the performance of parallel LiDAR is significantly hindered by the inter-channel interference in both temporal and spectral domains33–35. Several approaches have been developed to solve this issue. For example, time-stretch technology facilitates channel-isolated parallel ranging by temporally separating multi-wavelength pulses36–38. However, the intrinsic tradeoff between detection range and channel count prevents time-stretch technology from realizing high resolution and long-distance imaging. Furthermore, these systems exhibit diminished robustness in dynamic object detection. Following time-stretching technology, optical code division multiple access (OCDMA) effectively eliminates channel interference through time-domain orthogonal coding of the light, while also offering considerable refresh rates and image resolution11,21. Yet, the pseudo-random modulation employed in the encoding and decoding process of the OCDMA scheme relies on high-speed devices, which place high demands on both system hardware and software. Chaotic microcombs have recently gained significant attention in parallel LiDAR due to their inherent immunity to channel congestion39,40, which stems from their orthogonal optical channels. Nonetheless, the separate collection of symmetric combs centered at the pump frequency is a necessary process to avoid their interference, thereby imposing stringent demands on the number of detectors. Meanwhile, the capacity of long-distance ranging is hindered by the restricted propagation distance of chaotic lasers. Next-generation parallel LiDAR requires not only to be free from time-frequency congestion across multiple channels, but also to provide high precision, long-distance detection, and robust dynamic object recognition.

In this work, we introduce a parallel LiDAR equipped with spectrally encoded optical channels, where the natural quasi-orthogonality between different wavelengths of a super-bunching light is leveraged to eliminate time-frequency congestion between channels. This spectrally encoded parallel LiDAR achieves millimeter-level ranging accuracy and enables precise velocity measurements for slow-moving targets with speeds as low as 5 mm/s. We further demonstrate high-resolution 3D imaging utilizing 51 channels, with an integration time of just 10 μs for each individual scanning segment. Parallel LiDAR empowered by a super-bunching light overcomes the distance limitation of ranging imposed by pulse repetition periods without intricate encoding and decoding programs11,21, enabling spatially parallel ranging beyond 40 m. In addition, this system demonstrates exceptional noise immunity even under extreme conditions where noise surpasses the echo signal by over three orders of magnitude. The combination of super-bunching light and parallel LiDAR proposed in this work offers a pathway for advancing high-performance autonomous systems.

Results
Super-bunching enabled spectrally encoded optical channels
As a new-generation nonclassical light source, the super-bunching light exhibits giant second-order intensity correlation (g2(τ)) and extreme multi-photon emission probabilities. The normalized intensity correlation, g2(0), of the super-bunching light can be as high as 10, significantly stronger than that of thermal light (where g2(0) of thermal light equals 2, known as the bunching effect)41. The natural quasi-orthogonality between different wavelengths of a super-bunching light offers an opportunity to overcome the time-frequency congestion between channels. Figure 1a presents the schematic of the experimental setup for generating a super-bunching light source, which was produced via random nonlinear interactions in an optimized photonic crystal fiber (PCF) pumped by a picosecond (ps) laser with a central wavelength of 1064 nm and a repetition rate of 80 MHz. (See details in Experiment section) Fig. 1b presents the spectrum of super-bunching light, spanning from 480 to 1750 nm, a feature absent in super-bunching light generated via spontaneous parametric down-conversion (SPDC) or spontaneous four-wave mixing (SFWM)42,43, which serves as the foundation for multi-channel division in parallel LiDAR systems. An acousto-optic tunable filter (AOTF) was employed to divide the broadband super-bunching light into spectrally separated, artificially selected optical channels. Each optical channel was split into reference and output paths using a 1:9 beam splitter (BS). Channels in both paths were then spatially separated by a blazed grating and detected using avalanche photodetectors (APDs) for subsequent correlation analysis. The inset of Fig. 1b presents the results of second-order correlation measurement for the channel 633 nm, showcasing g(2)(0)=5, which confirms the super-bunching nature (g(2)(0)>2). In addition, the auto-correlation measurement for this channel was conducted, as shown in Fig. 1c. The result exhibits delta-function-like behavior, effectively suppressing self-interference within the channel. The ranging resolution Rres of the spectrally encoded parallel LiDAR is governed by the full width at half maximum (FWHM) of the auto-correlation function, which is fundamentally constrained by the detector’s bandwidth18:

Rres = c×FWHM / 2 (1)

where c is the speed of light. As depicted in the right inset of Fig. 1c, the FWHM of the auto-correlation function for channel 633 nm is 0.6 ns, corresponding to a ranging resolution of 9 cm. (Due to the high uniformity among channels divided by AOTF, the characteristics of channel 633 nm are representative of the entire set. See Supplementary NOTE 1)

To validate the quasi-orthogonality among the spectrally separated optical channels, we conducted cross-correlation function measurements. Figure 1e shows an extremum correlation peak (quantified by a peak-to-side lobe ratio (PSLR) greater than 2, see Supplementary NOTE 3) in the cross-correlation of channels 532 nm and 532 nm. In contrast, no such peak is observed in the cross-correlation between channels 532 nm and 542 nm. Additionally, Fig. 1d demonstrates pairwise cross-correlation measurements among 8 individual channels, with all values normalized to the global maximum. Notably, the presence of extremum correlation peaks in the measurements between identical channels (orange pixels in Fig. 1d) and their absence between different channels (blue/green pixels in Fig. 1d) confirms negligible inter-channel crosstalk. Consequently, the time-domain photon bunching effect of super-bunching light enables the division of frequency-quasi-orthogonal, crosstalk-free optical channels, which serve as spectrally encoded channels. The AOTF imposes a limitation on spectral resolution, resulting in a minimum crosstalk-free channel spacing of 8 nm (See Supplementary NOTE 4). During the ranging operation, as shown in Fig. 1e, the extremum correlation peak will shift from the zero-delay point, due to the differing optical path lengths between the output and reference loops. While Fig. 1e exhibits results beyond the pulse period, the ranging capability of conventional time-of-flight (ToF) LiDAR, which is driven by a pulsed laser, is strictly limited to the pulse repetition, making it difficult to perform measurements beyond the pulse period36. Although frequency-modulated continuous-wave (FMCW) LiDAR driven by a continuous laser is not limited by pulse period, it imposes stringent requirements on laser stability, detector sensitivity, and signal processing, all of which substantially increase system complexity, escalate costs, and hinder practical implementation. Leveraging the temporal photon bunching effect of super-bunching light, our system achieves precise distance measurements beyond the pulse period, for example, with a delay time of 133.3 ns for channel 532 nm, as shown in Fig. 1e, corresponding to a distance of 40.0 m. In contrast to optical code division multiple access (OCDMA) systems requiring intricate encoding and decoding11,21, our scheme enables ranging beyond the pulse periods with a simplified design. Moreover, OCDMA loses this capability when its channels share the same temporal code. The ability to perform light ranging beyond the pulse period paves the way for long-distance LiDAR applications.

Spectrally encoded parallel ranging
With spectrally encoded optical channels derived from super-bunching light, we present a parallel LiDAR. Figure 2a is a schematic of the spectrally encoded parallel LiDAR. In the output path, channels are spatially separated by a blazed grating (600 lines/mm) and then steered toward the target by a scanning galvanometer. The multiplexed echo signal reflected by the target is captured through a lens and focused onto a single APD. In the reference path, channels separated by the blazed grating are guided into the photodetectors (PDs) array, with each channel individually detected by one detector (See Supplementary NOTE 7). The ranging accuracy of the LiDAR system relies on detectors with superior performance in both the echo and reference paths, aligning with the setup used in ghost imaging44,45. Cross-correlation is performed between the mixed echo signal and each channel’s reference signal to retrieve individual range information22,40:

R(Δt) = ∫ xi(t) yi(t+Δt) dt / sqrt(∫ xi(t)^2 dt ∫ yi(t+Δt)^2 dt) (2)

where xi(t) is the reference signal of channel i, yi(t+Δt) is the echo signal, and Ri(Δt) is the cross-correlation function of channel i. Given that the extremum correlation peak appears exclusively between matching channels, as depicted in Fig. 2b, the delay time Δti represents the round-trip time of flight for channel i, traveling from the galvanometer to the target and returning to the detector. Therefore, the corresponding target distance Li is calculated as:

Li = c×Δti / 2 (3)

Performance tests of the LiDAR’s ranging capability were conducted using channel 633 nm. As shown in Fig. 2a, a whiteboard serves as the target, whose horizontal position is controlled by a high-precision stepper motor, moving away from the detector with a step size of 2 cm. The starting position of the whiteboard was set as the reference point to assess its relative distance in the test. Figure 2c presents the results of 22 tests, demonstrating a ranging error of up to 4 mm, representing an advanced level for parallel LiDAR systems. The ranging error was defined as the deviation between the ranging result and the known actual distance of the whiteboard. Moreover, with an integration time of just 10 μs per test, the system is capable of completing high-precision ranging tasks in rapidly changing environments. High-precision ranging capability allows for the reliable detection of low-speed targets. In low-speed target detection, the whiteboard was reciprocated at a constant velocity, and echo signals were continuously sampled using a high-speed oscilloscope. The corresponding results in Fig. 2d show an average speed measurement error of 4.1%, which is defined as the percentage deviation between the measured value and the preset speed of the stepper motor. Even when the target moves at a constant speed as low as 5 mm/s, the measured speed is 4.6 mm/s, resulting in a measurement error of 0.4 mm/s. With its precision ranging accuracy and dynamic detection capability, the spectrally encoded parallel LiDAR offers significant advantages in detecting low-speed dynamic targets.

Furthermore, we conducted measurements to assess the parallel performance of our scheme. A total of 16 optical channels were configured over the 530–680 nm spectral range with 10 nm spacing. Since all channels performed distance measurements of the whiteboard simultaneously, the ranging results of all channels should exhibit excellent consistency. The ranging results from the 5 tests shown in Fig. 2e are consistent with theoretical expectations, while the signal-to-noise ratio (SNR) for all 16 channels remains above 15 dB. These results demonstrate that our spectrally encoded parallel LiDAR achieves high-precision parallel ranging using only a single detector to collect the echo signals, while maintaining a high SNR.

3D imaging based on the spectrally encoded parallel LiDAR
The 3D imaging capabilities of spectrally encoded parallel LiDAR have also been explored. The 3D imaging targets, as shown in Fig. 3a, included a pedestrian, a cybertruck, and a helicopter, which were spatially arranged in sequence with 10 cm and 20 cm gaps between them (Fig. S8). Each pair of targets was spaced beyond the LiDAR’s ranging resolution, facilitating their distinction in 3D reconstruction. Figure 3b presents the 3D reconstruction results of the three targets based on vertical scanning data (Fig. S10). A total of 51 optical channels and 88 vertical scan segments were employed for 3D imaging, resulting in an image resolution of 51 × 88 pixels to recover finer target details. The horizontal and vertical errors introduced during the merging of multiple scans are negligible. (See Supplementary Note 9) The integration time for each scan segment is 10 μs. Considering the constraint imposed by the minimum allowable spectral spacing of 8 nm between adjacent channels, the spectral range of 530–680 nm cannot accommodate 51 mutually independent channels simultaneously. Hence, the 3D reconstruction is achieved through three successive scans, each utilizing 17 channels spaced at 9 nm intervals, as depicted in Fig. 3c. By interleaving and merging the three scans, a composite set of 51 channels with an effective spectral interval of 3 nm is constructed. Although state-of-the-art AOTFs are capable of achieving channel separation below 8 nm without crosstalk46, our approach offers a practical strategy for realizing multi-channel 3D imaging beyond the limitation of minimum channel intervals. While the OCDMA method must contend with a reduction in detection distance as channels are increased21,36, our approach delivers increased channels without any associated loss. Figure 3d presents the vertical scanning results for the 596 nm, 632 nm, and 659 nm channels. The distinct separation of data points corresponding to different targets in distance confirm that our LiDAR successfully achieves accurate 3D reconstruction of the three spatially distinct objects. This capacity is further supported by the photon-counting statistics of the echo signals from different distances, as illustrated in Fig. 3e.

Beyond accurate 3D reconstruction, spectrally encoded parallel LiDAR can also perform target-specific identification by analyzing the correlation features in the echo signals. (See Supplementary Note 10) In the boxplot shown in Fig. 3f, we statistically analyzed the maximum correlation coefficients from the unnormalized cross-correlation functions among all pixels of the three objects in Fig. 3b. The variation in object reflectivity results in differing statistical distributions of the correlation coefficients. Due to the low reflectivity of the pedestrian’s polyester clothing, the correlation coefficients remain below 1. The elevated surface reflectivity of the Cybertruck and helicopter contributes to stronger correlation coefficients, and the Cybertruck’s comparatively smoother surface results in the highest correlation coefficient among the targets. The integration of correlation-based target classification and identification with deep learning will lay the foundation for the advancement of intelligent parallel LiDAR systems.

Long-distance and interference-free parallel LiDAR
Despite growing demand, parallel LiDAR systems operating beyond 10 m remain unreported, even with approaches such as chaotic microcombs and time-stretching22,36,40. Based on the spectrally encoded parallel LiDAR, we demonstrate high-precision ranging at distances exceeding 20 m, addressing this critical challenge. Figure 4a presents the long-distance ranging results of 20 targets based on 20 channels and corresponding errors. The 20 targets were arranged at equal intervals within a distance range of 21 m to 40 m from the LiDAR, whose positions were set and verified using an electoral total station (ETS) (See Experiment Section). A dual-grating configuration is employed to spatially separate the optical channels and suppress angular dispersion over extended propagation distances (Fig. S11). Each optical channel was directed to a distinct whiteboard, enabling all parallel ranging results shown in Fig. 4a to be captured simultaneously in a single acquisition based on 20 channels. Remarkably, with an integration time of 10 μs, the ranging error remains below 3.5 cm, even at distances of up to 40 m. A performance comparison of advanced LiDAR systems was conducted, revealing that spectrally encoded parallel LiDAR achieves long-distance ranging beyond the pulse period while maintaining a lower system cost. (See Supplementary Note 15). In long-distance ranging, the triangulation error introduced by the optical path deviation results in all errors in Fig. 4a being non-negative (Fig. S12)47,48. We further demonstrated the 3D imaging capability of the spectrally encoded parallel LiDAR at distances beyond 10 m. Using 16 channels, a vertical scan was performed on an opaque, cross-shaped target located approximately 11 m away, with a whiteboard placed about 10 cm behind the target. The imaging results presented in Fig. 4b, together with the spatial pixel distribution across multiple spectral channels (Fig. S14), validate the system’s ability to perform high-precision 3D reconstruction of targets located over 10 m away.

Owing to the temporal photon bunching effect of super-bunching light and the mutual quasi-orthogonality of its optical channels, spectrally encoded parallel LiDAR demonstrates robust anti-interference performance. To assess the anti-interference performance, we employed two Gaussian-distributed noise sources: a continuous-wave (CW) laser and a pulsed laser with the same repetition rate as the super-bunching light. The noise signal was mixed with the echo and entered the same APD simultaneously, as illustrated in Fig. 4c, d (See Supplementary NOTE 13). By adjusting the noise intensity using a variable optical attenuator, we investigated the relationship between the interference-to-signal ratio (ISR) and the SNR of the cross-correlation function. The ISR is defined as the power ratio between the interference signal and the echo signal received by the APD22. As illustrated in Fig. 4c, under CW noise, the dynamic detection range of the correlation peak is broad. The SNR remains essentially constant when the ISR exceeds 25 dB. Beyond this threshold, further increases in noise lead to a linear degradation of the SNR. Even at an ISR of 30 dB, the SNR remains above the 3 dB detection threshold, demonstrating that our parallel LiDAR can effectively suppress CW noise exceeding the echo signal by over a thousand fold, thereby significantly reducing the required laser radiation power. In the case of pulsed laser noise, as shown in Fig. 4f, although the dynamic detection range is relatively narrow, our parallel LiDAR still demonstrates robust anti-interference capability, maintaining effective detection even when the noise is over 125 times stronger than echoes. The strong anti-interference capability ensures the reliable operation of the spectrally encoded parallel LiDAR in complex environments.

In summary, we demonstrate a spectrally encoded parallel LiDAR system enabled by a super-bunching light source. Harnessing the temporal photon bunching effect of super-bunching light, our spectrally encoded channels overcome the inter-channel interference in both temporal and spectral domains, and surpass the inherent pulse-period constraints of coherent detection. Our parallel LiDAR system demonstrates a ranging error of 4 mm and enables the accurate tracking of low-speed targets with a speed measurement error of 4.1%. By integrating dispersive elements and scanning galvanometers, our parallel LiDAR achieves rapid and high-precision 3D reconstruction of objects. In addition, we determine the long-distance ranging and imaging capabilities of this parallel LiDAR, along with its robust anti-interference performance. Further progress in system integration and extending the system’s capabilities to a broader range of spectral bands will speed up its practical application. With its high sensitivity, precision, adaptability to dynamic targets, extended range, and robust noise immunity, the spectrally encoded parallel LiDAR paves the way for the development of next-generation high-performance parallel LiDAR.

Methods
Schematic diagrams of the experimental setups are shown in Figs. 1a, 2a of the main text. A homemade picosecond laser with a center wavelength of 1064 nm was used to pump the PCF (SC-PRO, YSL Photonics). The pump pulse has an energy fluence of 123 nJ, a pulse duration of ~136 ps, a repetition of 80 MHz, and is coupled into a 10 m-long PCF. An AOTF (AOTF0311, YSL Photonics) was employed to divide the optical channels. The bandwidth of the individual channel output by the AOTF is 3 nm. The minimum channel spacing without crosstalk is 8 nm, all the channels are spatially combined and output simultaneously. The configurations for optical channel divisions of the AOTF are controlled by electrical signals, with a switching time of a few microseconds. A blazed grating (600 lines/mm, BG25-600-500) was used to spatially separate channels in both the output and reference paths. A scanning galvanometer (FSM-300-01, Newport) was used to direct channels toward the target. The mix echoes were collected by a lens (f=50 mm) and then focused onto an APD (APD210, Thorlabs). The PDs array (DET10A2, Thorlabs) was used in the reference path. A high-speed oscilloscope (MSO58B, Tektronix) was employed to sample the signals continuously.

In the test for Fig. 2c, a linear stepper motor stage (LTS150/M, Thorlabs) was used to move the whiteboard, with a positioning error of ±0.6 μm, which is negligible compared to our measurement errors. In the test for Fig. 4a, an electoral total station (Leica, TZ08) was employed to verify the positions of the whiteboards. The electoral total station has a precision of 1 mm (Measuring distances within 1000 m), far smaller than the error in ranging measurements.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The authors present a novel parallel LiDAR sensor that reduces crosstalk between individual channels, accelerates data acquisition, and increases the sampling rate. To achieve this, they use a super-bunching light source that utilizes higher-order photon correlations to enable parallel data acquisition through spectral encoding. The authors demonstrate both velocity and distance measurements up to 40 m. I consider this approach to be very promising and see it as a relevant contribution to the field. The manuscript is well written, the figures are of high quality, and the experimental data are technically sound.\nI therefore support publication in Nature Communications, provided that several aspects are clarified beforehand.\n1. In the abstract, the term “quasi-orthogonal temporal photon bunching effect” is used. I believe it would be helpful to replace this term with a brief explanatory description for clarity.\n2. The setup for generating the super-bunching light source should be described in more detail. For example, information on pulse energy, pulse duration, and fiber length is missing. In addition, the role of the AOTF should be clarified. What limitations arise from this component, and how might they be overcome? In particular, the number of channels and the bandwidth of each spectral channel are critical parameters. How fast can the channels be tuned? And would a demultiplexer also be a viable alternative?\n3. The authors should specify the optical power levels used in the LiDAR experiments.\n4. Another point that should be addressed is the wavelength range. For LiDAR applications, operation is often carried out around 1560 nm. What strategies could be employed to enable operation in this spectral region?\n5. The most significant competing approach for parallel LiDAR is based on modulated frequency combs. To strengthen the manuscript, the authors should provide a more detailed comparison with this and other state-of-the-art techniques. The presentation of such a comparison in tabular form, summarizing key parameters such as for example acquisition speed, number of channels, and sampling rate, would be particularly useful. While line 141 briefly compares the proposed method with other LiDAR approaches, FMCW LiDAR should be explicitly addressed, as it is a highly effective technique that is not limited by pulse duration.\n6. Another important aspect that should be addressed is the miniaturization of the system. How could the individual components be integrated or scaled down to enable a more compact implementation?\nMinor points:\n• In the Supplementary Materials, some of the axis labels in the figures are partially cut off and should be corrected.\n• There are a few minor spelling errors throughout the manuscript and the supplementary material that should be addressed.\n• The error bars should not be shown in yellow, as this color is hardly visible in the print version.", "reviewer_2": "1. The study makes use of super-bunching lights, which has the following challenges\n• Power loss and inefficiency due to multiple stages of interaction with each stage involving loss of optical power.\n• It requires sophisiticated setups which lead to additional expenses to build and maintain\n• It also has integration challenges for practical use in LiDAR\n• Detector overwhelming with a high photonon pulse from single flux can saturate the detector causing inaccurate readings\n• Complex statistics needed to interpret superbunching light, e.g., upturned-tail distributions, which complicates data processing and analysis\n2. Despite the claimed novelty of using the superbunching lights, experiments are not very well designed and several aspects are not explored. For example, what is the superiority of superbunching lights if a similar performance can be achieved using other setups, such as combo lights, which is easy to install and interpret, compared to practical challenges of superbunching lights?\n3. A potential work in parallel LiDAR is that of et al., i.e., “Breaking the temporal and frequency congestion of LiDAR by parallel chaos”, published in Nature Photonics, which orthogonality of the chaotic comb lines which also eliminates the inter-channel interference, as well as, simplifies the receiving architecture. It also offers non-cross-talk and high-precision parallel ranging. How the author put their work in comparison to that work\n4. Similar works which break the time-of-flight limit has already been published, e.g. “Ultrafast Parallel LiDAR with Time-encoding and Spectral Scanning: Breaking the Time-of-flight Limit”. How the authors justify the novelty of their study.\n5. The height of the objects in Figure 3b is given in meters which is misleading. I believe, the height is in cm. please double check.\n6. Figure 3f shows that the objects such as pedestrian, helicopter and cybertruck can be identified due to different correlation values based on surface reflectivity. How about those objects (vehicles) with similar referelctivity; there are no experiments concerning those. In addition, angle-dependent phase retardation values can also lead to different reflectivity.\n7. Despite the advantages of successive scans such as improved accuracy, reduced drift, etc. in the proposed LiDAR, risk accumulating is possible threat. In addition, merging successive scans also carry the risk of errors.\n8. Although, inter-channel investigated using Gaussian-distributed noise sources, inter-LiDAR interference is not investigated. Even a modest interference of 10-20 photons per pulse can effect accuracy by centimeters. With enhanced detection range, the probability of such interference needs to be considered.\n9. The last sentence of the Abstract overstates the achievements of the proposed LiDAR.\n10. 1650nm being hazardous to eye safety, safety concerns should be properly investigated.\n11. What is the power usage of the proposed LiDAR in comparison to existing parallel LiDARs.\n12. Performance comparison to existing parallel LiDARs must be carried out to show the supremacy of the proposed prototype. For example, “A hybrid 3D LIDAR imager based on pixel-by-pixel scanning and DS-OCDMA”, and “LiDAR pulse coding for high resolution range imaging at improved refresh rate” offer viable solution for high resolution images.", "reviewer_3": "This paper demonstrates a LiDAR implementation using photonic crystal fiber (PCF) to generate frequency-multiplexed super-bunching light, followed by second-order correlation measurements in each frequency channel. The advantage of this approach is that the generated super-bunching light has strong frequency correlations, which help reduce frequency–time crosstalk, while also providing anti-interference capability. The authors present various results, including measurements of moving objects and three-dimensional imaging, making this a valuable and interesting work. I thank the authors for their contribution. Below are some questions and comments regarding the paper:\n1. In photon sources such as SPDC or SFWM, frequency correlations also exist [Jin-Hum Kim, Optica 8 (12), pp. 1524-1531 (2021), Ilya N. Chuprina, Phys. Rev. A 100, 043843 (2019)], and super-bunching properties can be observed through second-order correlation measurements [J Mika et al, New J. Phys. 20 093002 (2018)]. Therefore, I believe similar experimental results could be obtained using such sources. Could the authors provide a comparison between using SPDC/SFWM to generate superbunching light and using PCF, in terms of advantages and disadvantages?\n2. In thermal light ranging, anti-interference features are also achievable [Jun Zhu, Applied Optics, 51 (20), pp. 4885-4890 (2012)], and frequency-channel separation can be implemented using this method [experimental quantum illumination]. In this context, I would like to ask: what is the specific advantage of using super-bunching light compared to simply using bunching light?\n3. In Fig. 2(c) and Fig. 4(a), the error values are defined as the difference between the actual distance and the distance measured by the experimental scheme (as noted around line 173). Could the authors clarify how the actual distance was determined? Including this information in the paper would strengthen the reliability of the reported error values, since the credibility of the error depends on the accuracy of the actual distance measurement method.\n4. In Supplementary Note VI, the authors conclude that “the detector’s performance at the reference ending does not impact the ranging accuracy.” My interpretation is that this setup is somewhat similar to ghost imaging (or ghost spectroscopy) schemes [Ilaria Gianani, Opt. Lett. 48(12), pp. 3299-3302 (2023)]. As seen in temporal ghost imaging [Daniele Faccio, Nature Photonics 10, pp. 150–152 (2016)], the temporal accuracy generally follows the performance of the better detector between the reference and the other paths. Thus, I assume that in this experiment, the reference detector’s performance does not limit the ranging accuracy because the detector at the output-path had higher performance. If the authors agree with this interpretation, it may be helpful to state this more explicitly (e.g., if the detector at the output path outperforms the reference detector, then degradation of the reference detector does not affect the ranging accuracy). I also agree with the authors’ conclusion that this feature helps reduce system complexity and experimental costs.\n5. In Supplementary Note V, the term “super-bunching laser” is used. I would suggest revising this expression, as the word “laser” usually refers to a coherent state in quantum optics with a second-order correlation value of 1, which may cause confusion. I would be interested in the authors’ view on this point.\n6. In Figure 1(d), the correlation coefficient was measured based on eight channels. I'm curious about the frequency range of each channel.\nThank you."}, "author_response": "Response Letter to Reviewers\nThank all the Reviewers for the helpful and instructive comments. We sincerely appreciate\nthe valuable suggestions from all the Reviewers, which have significantly improved our\nmanuscript. We have addressed the Reviewers’ questions point by point and revised the\nmanuscript accordingly. Below are our detailed responses to the Reviewers’ comments:\nReviewer #1:\nGeneral comment: The authors present a novel parallel LiDAR sensor that reduces crosstalk\nbetween individual channels, accelerates data acquisition, and increases the sampling rate. To\nachieve this, they use a super-bunching light source that utilizes higher-order photon\ncorrelations to enable parallel data acquisition through spectral encoding. The authors\ndemonstrate both velocity and distance measurements up to 40 m. I consider this approach to\nbe very promising and see it as a relevant contribution to the field. The manuscript is well\nwritten, the figures are of high quality, and the experimental data are technically sound.\nI therefore support publication in Nature Communications, provided that several aspects\nare clarified beforehand.\nGeneral response: We sincerely appreciate Reviewer #1’s positive assessment of our work,\nwhich demonstrates parallel LiDAR driven by super-bunching light, achieving crosstalk-free\noptical channel division and overcoming the pulse-period limitation to enable long-distance\nparallel ranging. The authors thank the Reviewer for the valuable suggestions to improve this\nwork. We have provided detailed responses after each corresponding comment.\nComment 1-1: In the Abstract, the term “quasi-orthogonal temporal photon bunching effect”\nis used. I believe it would be helpful to replace this term with a brief explanatory description\nfor clarity.\nResponse 1-1: We thank the Reviewer for this comment.\nSuper-bunching light generated in photonic crystal fiber (PCF) by picosecond (ps)-pulse\npumping exhibits a broadband range (480-1750 nm) and a photon bunching effect in the\ntemporal domain. Acousto-optic tunable filters (AOTF) are employed to separate the optical\nchannels of the super-bunching light. As shown in Figs. 1d and 1e, the cross-correlation of\nsame-channel produces a large peak-to-side lobe ratio (PSLR), corresponding to the extreme\ncorrelation peak noted in the main text. By contrast, the cross-correlation between different\nchannels lack this feature, demonstrating that inter-channel interference is negligible. The\npresence of the extreme correlation peak allows us to isolate and extract the ranging result of\nthe corresponding channel from the mixed echo signals. Consequently, time-domain photon\nbunching of the super-bunching light supports spectrally encoded, frequency-orthogonal optical\nchannels without crosstalk. Following the above discussion, we revised the corresponding\nsentence in the Abstract and added discussion in the main text.\nAction Taken:\n Update line 29 of page 2 (Abstract section), replacing “The quasi-orthogonal temporal\nphoton bunching effect of this light enables spectrally encoded optical channels to be\ndivided without inter-channel crosstalk, effectively breaking the pulse period constraint of\nconventional coherent LiDAR systems.” with “Here, we introduce a novel spectrally\nencoded parallel LiDAR based on super-bunching light, exhibiting negligible cross-\ncorrelation between different wavelengths, enabling orthogonal, spectrally encoded\noptical channel division in the frequency domain without inter-channel crosstalk.”\n Add discussion in line 143 of page 6 (Results and discussion section): “Consequently, the\ntime-domain photon bunching effect of super-bunching light enables the division of\nfrequency-orthogonal, crosstalk-free optical channels, which serve as spectrally encoded\nchannels.”\nComment 1-2: The setup for generating the super-bunching light source should be described\nin more detail. For example, information on pulse energy, pulse duration, and fiber length is\nmissing. In addition, the role of the AOTF should be clarified. What limitations arise from this\ncomponent, and how might they be overcome? In particular, the number of channels and the\nbandwidth of each spectral channel are critical parameters. How fast can the channels be tuned?\nAnd would a demultiplexer also be a viable alternative?\nResponse 1-2: We thank the Reviewer for this comment.\nThe Reviewer suggested providing more details about the super-bunching light and raised\nconcerns about the role of the AOTF. Additionally, the Reviewer proposed whether wavelength\ndivision multiplexers (WDM) could serve as an alternative to the AOTF for optical channel\ndivision. We address the above issues separately.\n Additional information on the super-bunching light. The super-bunching light was\nproduced via random nonlinear interactions in an optimized PCF pumped by a ps laser with\na central wavelength of 1064 nm. The pump pulse has an energy fluence of 123 nJ, a pulse\nduration of ~136 ps, a repetition of 80 MHz, and is coupled into a 10 m-long PCF.\nThe pulse duration is determined by converting the pump pulse into electrical signals\nusing a 50 GHz high-speed photodetector, and the output is subsequently measured with a\nhigh-speed sampling oscilloscope (Tektronix, DPO70000SX), as shown in Fig. R1-1.\nFig. R1-1 Pulse duration of the super-bunching light.\nAction Taken:\n Add discussion in line 323 of page 13 (Experiment section): “A homemade\npicosecond laser with a center wavelength of 1064 nm and a repetition rate of 80 MHz\nwas used to pump the PCF (SC-PRO, YSL Photonics). The pump pulse has an energy\nfluence of 123 nJ, a pulse duration of ~136 ps, a repetition of 80 MHz, and is coupled\ninto a 10 m-long PCF.”\n Update line 110 of page 5 (Results and discussion section): “Figure 1a presents the\nschematic of the experimental setup for generating a super-bunching light source,\nwhich was produced via random nonlinear interactions in an optimized photonic\ncrystal fiber (PCF) pumped by a picosecond (ps) laser with a central wavelength of\n1064 nm and a repetition rate of 80 MHz. (See details in Experiment section)”\n Insert Fig. R1-1 as Fig. S2 on page 3 of the Supplementary Materials, and include the\ndiscussion: “The pulse duration of the pump pulse is determined by converting the\npump pulse into electrical signals using a 50 GHz high-speed photodetector, and the\noutput is subsequently measured with a high-speed sampling oscilloscope. As shown\nin Fig. S2, the pulse duration of the pump is ~136 ps.”\n Role of the AOTF. The spectral coverage of the super-bunching light is broadband and\ncontinuous, as shown in Fig. 1b. The AOTF divides broadband super-bunching light into\noptical channels, each appearing as a discrete spectrum in the frequency domain. It\nachieves a minimum crosstalk-free channel spacing of 8 nm, and all the channels are\nspatially combined and output simultaneously. The time required to switch between\ndifferent preset optical channel configurations is typically in the microsecond.\n The bandwidth of channels. The bandwidth of the individual channel output by\nAOTF is 3 nm, as shown in Fig. R1-2.\nFig. R1-2 The bandwidth of channel 670 nm output by AOTF.\n The number of channels. The number of channels depends on the minimum\nachievable channel spacing that ensures no crosstalk, along with the available spectral\ncoverage. The minimum channel spacing is 8 nm for the AOTF employed here,\nsupporting up to 21 channels in the 525-685 nm spectral range. State-of-the-art AOTFs\nare capable of achieving channel separation below 8 nm without crosstalk, enabling\nmore optical channels within the same spectral range. Additionally, Fig. 3 presents\nanother solution, which increases the number of channels in the final results by\nperforming multiple vertical scans with different channels.\n Currently, the volume of the AOTF poses challenges for system integration. The\nAOTF performs discrete optical channel division by adjusting the period of acousto-\noptic crystals via ultrasonic signals, and it is anticipated that on-chip AOTF devices\nwill be developed in the future [1,2]. Additionally, using wavelength division\nmultiplexers (WDMs) instead of AOTFs for optical channel separation is a potential\nsolution to improve system integration. The feasibility of this solution will be\ndiscussed below.\nAction Taken:\n Add discussion in line 327 of page 13 (Experiment section): “The bandwidth of the\nindividual channel output by AOTF is 3 nm. The minimum channel spacing without\ncrosstalk is 8 nm, all the channels are spatially combined and output simultaneously.\nThe configurations for optical channel divisions of the AOTF are controlled by\nelectrical signals, with a switching time of a few microseconds.”\n Wavelength Division Multiplexers (WDMs) scheme.\n Coarse wavelength division multiplexer (CWDM) for visible light.\nFig. R1-3a shows the spectral results from both output ends of the 532/670 nm\nCWDM (LBTEK, WDM-5367-FA) when the super-bunching light is used as input.\nThe output bandwidth of each channel is relatively broad, with significant spectral\noverlap between the two channels. As shown in Fig. R1-3b, the cross-correlation\nresults of the 532/670 nm CWDM outputs fail to display an extreme correlation peak,\nwhich makes it incompatible with this parallel ranging scheme.\nWe verified the demultiplexing performance of the 532/670 nm CWDM by\npreprocessing the super-bunching light with AOTF, splitting it into two channels at\n532 nm and 670 nm, which were subsequently input into the CWDM. Fig. R1-4\nillustrates the spectrum at the 532/670 nm CWDM outputs and the cross-correlation\nresults between channels. In this case, 532/670 nm CWDM effectively performs\ndemultiplexing, and the cross-correlation results of the same channels show extreme\ncorrelation peaks. However, this architecture does not eliminate the need for AOTF\nand offers limited improvements to system integration.\nFurthermore, when AOTF outputs with channel spacings of 3, 6, and 9 nm were\nused as 532/670 nm CWDM inputs, we observed that the spectral characteristics of\nthe 532/670 nm CWDM outputs were identical to the AOTF outputs, as shown in Fig.\nR1-5. This indicates that the 532/670 nm CWDM employed here is less capable of\ndemultiplexing optical channels in the case of broadband light sources. If the light\nsource outputs discrete spectral lines, such as an optical comb, this 532/670 nm\nCWDM can successfully demultiplex the different channels. However, for a super-\nbunching light with a continuous spectrum, this 532/670 nm CWDM is unable to carry\nout channel division independently.\nMoreover, for the visible band, both coarse and dense wavelength division\nmultiplexers (CWDMs, DWDMs) are non-standard products that require\ncustomization, and their performance is substantially inferior to that of WDMs for the\ninfrared band. Based on the above discussion, it is currently unfeasible to replace\nAOTFs with WDMs for optical channel demultiplexing in the visible band.\n The feasibility of WDMs schemes for the infrared band will be discussed in Responses\n1-4.\nFig. R1-3 Performance of cross-correlations from 532/670 nm CWDMs with continuous\nspectra as inputs. (a) Spectrum of the 532/670 nm; (b) The corresponding cross-correlation\nresults.\nFig. R1-4 Performance of cross-correlations from 532/670 nm CWDMs with discrete spectra\nas inputs. (a) Spectrum of discrete 532 nm and 670 nm, (b) the cross-correlation of 532/532 nm,\n(c) the cross-correlation of 530/670 nm. The discrete spectra are produced by an AOTF.\nFig. R1-5 The Channel division capability of AOTF and 532/670 nm CWDM for wavelengths\nwith different spacings. (a-c) The spectrum of the AOTF output with channel spacings set to 3,\n6, and 9 nm, respectively. (d-f) The spectrum of CWDMs at the 532 nm path when the output\nof the AOTF is used as the input.\nComment 1-3: The authors should specify the optical power levels used in the LiDAR\nexperiments.\nResponse 1-3: We thank the Reviewer for this comment.\nFig. R1-6 displays the power for each channel in all optical channel configurations\nmentioned in the main text, including the 8 channels from Fig. 1d, 16 channels from Fig. 2e, 51\nchannels from Fig. 3b, 20 channels from Fig. 4a, and 16 channels from Fig. 4b.\nAction Taken:\n Insert Fig. R1-6 as Fig. S19 on page 23 of the Supplementary Materials, and include the\ndiscussion “Fig. S19 displays the power for each channel in all optical channel\nconfigurations mentioned in the main text, including the 8 channels from Fig. 1d, 16\nchannels from Fig. 2e, 51 channels from Fig. 3b, 20 channels from Fig. 4a, and 16 channels\nfrom Fig. 4b.”\nFig. R1-6 Power of the optical channels used in the experiment. (a) 8-channel correlation\ndetection in Fig. 1d, (b) 16-channel parallel ranging in Fig. 2e, (c) 51-channel 3D imaging in\nFig. 3b, (d) 20-channel long-distance parallel ranging in Fig. 4a, and (e) 16-channel long-\ndistance imaging in Fig. 4b.\nComment 1-4: Another point that should be addressed is the wavelength range. For LiDAR\napplications, operation is often carried out around 1560 nm. What strategies could be employed\nto enable operation in this spectral region?\nResponse 1-4: We thank the Reviewer for this comment.\nCurrently, the AOTF employed in our system exhibits excellent filtering performance in\nthe visible band. Through this AOTF, we have demonstrated the key advantages and application\npotential of spectrally encoded parallel LiDAR in the visible band.\nSince 1560 nm is a more common choice for LiDAR, and following Reviewer #1’s\nsuggestion, we have verified the feasibility of spectrally encoded parallel LiDAR using\nInfrared-AOTF (YSL Photonics, AOTF-PRO2) and WDMs at this wavelength range.\n Infrared-AOTF scheme. Fig. R1-7 shows that, in the infrared band, the AOTF can achieve\ncrosstalk-free optical channel division, but the minimum channel spacing of this AOTF is\n40 nm. This result highlights that spectrally encoding parallel ranging in the infrared band\nbased on Infrared-AOTF is feasible, however, the extensive channel spacing limits its\nperformance in large-scale parallel ranging. Future advancements in AOTF technology are\nexpected to facilitate scalable channel division and parallel ranging.\nFig. R1-7 Cross-correlation performance of the Infrared-AOTF. (a) Spectrum of individual\n1480 nm, 1550nm, and dual 1480/1550 nm. (b) Cross-correlation result of 1550/1550 nm, (c)\nCross-correlation result of 1480/1550 nm.\n WDMs scheme. Given the maturity of WDMs in the infrared band, and inspired by the\nReviewer, we examined the channel division performance of CWDMs (DK Photonics,\n86.034534-006) and DWDMs (XYT Sharetop, XYT-DWDM-40CH-1R), respectively.\n Performance of CWDMs. Fig. R1-8 shows the demultiplexing results of super-\nbunching light based on 1530/1550/1570 nm CWDMs. The optical channel output by\nCWDMs exhibits a bandwidth of 21.6 nm. The cross-correlation results for the same\nchannel show an extreme correlation peak, and no crosstalk is observed between\n1530/1570 nm channels, indicating that the minimum spacing for crosstalk-free\noptical channel division through CWDMs is 40 nm.\nFig. R1-8 Cross-correlation performance of the CWDMs. The spectrum (a), cross-correlation\nresults of 1530/1530 nm (b) and 1530/1570 nm (c) of the 1530/1550/1570 nm CWDMs’ outputs.\n Performance of DWDMs. Fig. R1-9 shows the demultiplexing results of the\nDWDMs for C bands. Due to the huge insertion loss between DWDMs and the super-\nbunching light, the DWDMs employed here fail to perform the demultiplexing task,\nand the output signal is found to be extremely weak. As an alternative, we assessed\nthe demultiplexing performance of the DWDM with a commercial 1560 nm laser (NPI\nLASERS, Rainbow 1550 OEM). The spectral results are shown in Fig. R1-9. The\nDWDMs output channel has a bandwidth of 11.2 nm. Previous experience indicates\nthat when the power of super-bunching light is sufficient, DWDMs can successfully\nachieve crosstalk-free optical channel division with a channel spacing of ~20 nm.\nFig. R1-9 The spectrum of the 1530/1550/1570 nm DWDMs’ outputs.\nIn summary, both Infrared-AOTFs, CWDMs, and DWDMs are capable of dividing\nchannels without crosstalk. A common drawback of these methods, however, is the\nrelatively large channel spacing. Although they can be applied to spectrally encoded\nparallel LiDAR in the infrared band, they are not sufficient for large-scale parallel ranging\ntasks that require a significant number of channels. It is hoped that future advancements in\nInfrared-AOTFs and WDMs will support optical channel division with narrower channel\nspacing, thereby enabling the development of more advanced infrared spectrally encoded\nparallel LiDAR.\nComment 1-5: The most significant competing approach for parallel LiDAR is based on\nmodulated frequency combs. To strengthen the manuscript, the authors should provide a more\ndetailed comparison with this and other state-of-the-art techniques. The presentation of such a\ncomparison in tabular form, summarizing key parameters such as for example acquisition speed,\nnumber of channels, and sampling rate, would be particularly useful. While line 141 briefly\ncompares the proposed method with other LiDAR approaches, FMCW LiDAR should be\nexplicitly addressed, as it is a highly effective technique that is not limited by pulse duration.\nResponse 1-5: We thank the Reviewer for this comment.\nTable R1-1 presents a comparison of the performance of parallel frequency-modulated\ncontinuous-wave (FMCW) LiDAR, single-photon LiDAR, parallel Chaotic LiDAR, and our\nspectrally encoded parallel LiDAR. The parameters compared among the above LiDAR\nsystems include detector configuration, number of channels, sampling rate, ranging accuracy,\nspeed measurement capability, light power of individual channel, detection range, and overall\nintegration level. Our core advantage lies in realizing parallel LiDAR without channel crosstalk\nand long-distance ranging capability that are not limited by the pulse period, all at a relatively\nlow system cost. FMCW LiDAR, while not limited by the pulse period, comes with a complex\nand costly system. Single-photon LiDAR can perform ultra-long-distance ranging tasks;\nhowever, it lacks parallel ranging capabilities, and the pixel acquisition time is relatively long.\nChaotic micro-comb LiDAR eliminates channel crosstalk in parallel LiDAR but cannot perform\nlong-distance ranging tasks due to the limitation of its propagation distance.\nTable R1-1 Comparison of currently advanced parallel LiDARs\nParallel Chaotic\nParallel FMCW Single-Photon Spectrally Encoded\nLiDAR\nParameters LiDAR LiDAR Parallel LiDAR\n(Nature Photonics\n(Nature 2020)[3] (Optica 2025)[4] (This work)\n2023)[5]\nSuperconducting\nAvalanche Avalanche\nBalanced Nanowire Single\nDetector Photodiode Photodiode\nPhotodetector Photon Detector\n(APD) (APD)\n(SNSPD)\nNumber of\n30 1 51 51\nchannels\nSampling rate 10 μs 1 ms 12.5 μs 10 μs\nRanging\n＜1 cm mm scale ＜2 mm ＜4 mm\naccuracy\nSpeed 5 mm/s\n5.1 mm/s\nmeasurement 20 mm/s \\ (Minimum\n(Minimum velocity)\ncapability velocity)\nIndividual\n5-20 mW ≤3.5 mW <1 mW[6] <1 mW\nchannel power\n45/325 m (human 1.73 m (A\n10 m (The static\nface) simulated scene)\nSpace detection flywheel) 1.85 m (whiteboard,\n1000 m 11 m (cross-shaped\nrange 2.3 m (The EPFL A simulated scene)\n(Communications target)\nuniversity logo) 40 m (whiteboards)\nmast)\nIntegration Fiber Space Chip Space\nWe thank the Reviewer for pointing out that we omitted FMCW LiDAR, an important\nLiDAR technology, from the discussion in the main text. Time-of-flight (ToF) LiDAR, such as\nspectrally encoded parallel LiDAR and parallel chaotic LiDAR, employs a pulsed laser, with\nthe pulse period determining its effective detection range. FMCW LiDAR employs a\ncontinuous-wave laser and is therefore not constrained by the pulse-period limitation inherent\nin pulsed systems. FMCW LiDAR exhibits advantages such as extended detection range, high\nprecision, strong robustness against interference, and distinctive capability for measuring the\nvelocity of moving targets. However, FMCW LiDAR requires continuous frequency\nmodulation of the emitted laser and extracts the frequency difference between the echo and\nreference signals through heterodyne detection, thereby indirectly deriving the flight time to\ncalculate the target distance. This approach imposes stringent requirements on laser stability,\ndetector sensitivity, and signal processing, all of which substantially increase system complexity,\nescalate costs, and hinder practical implementation.\nAccordingly, we have revised the discussion on “limitations of pulse period on detection\nrange” in line 141 of page 6.\nAction Taken:\n Update lines 149 of page 6 (Results and discussion section), replacing “The ranging\ncapability of LiDAR based on classical coherent laser is strictly limited by the pulse\nrepetition rate, making it difficult to perform measurements beyond the pulse period.” with\n“While Fig. 1e exhibits results beyond the pulse period, the ranging capability of\nconventional time-of-flight (ToF) LiDAR, which is driven by a pulsed laser, is strictly\nlimited to the pulse repetition, making it difficult to perform measurements beyond the pulse\nperiod. Although frequency-modulated continuous-wave (FMCW) LiDAR driven by a\ncontinuous laser is not limited by pulse period, it imposes stringent requirements on laser\nstability, detector sensitivity, and signal processing, all of which substantially increase\nsystem complexity, escalate costs, and hinder practical implementation.”\n Add discussion in line 270 of page 11 (Results and discussion section): “A performance\ncomparison of advanced LiDAR systems was conducted, revealing that spectrally encoded\nparallel LiDAR achieves long-distance ranging beyond the pulse period while maintaining\na lower system cost. (See Supplementary Note XIV).”\n Insert Table R1-1 as Table S1 on page 24 of the Supplementary Materials.\nComment 1-6: Another important aspect that should be addressed is the miniaturization of the\nsystem. How could the individual components be integrated or scaled down to enable a more\ncompact implementation?\nResponse 1-6: We thank the Reviewer for this comment.\nThe integration of spectrally encoded parallel LiDAR is expected to be advanced in the\nfuture through the following 4 aspects.\n Integration of pump laser and PCF. By combining the 1064 nm picosecond fiber laser\nwith photonic crystal fiber, the space occupation of the system can be significantly reduced.\n On-chip AOTF. Numerous reports have been published on on-chip acousto-optic\nmodulators [1,2]. Breakthroughs in on-chip AOTFs in the future will greatly improve system\nintegration.\n Using an array of photodetectors in the reference path.\n WDMs scheme. Advances in WDMs will provide a more integrated alternative approach\nfor spectrally encoded optical channel division in the future.\nComment 1-7: Minor points:\n• In the Supplementary Materials, some of the axis labels in the figures are partially cut off and\nshould be corrected.\n• There are a few minor spelling errors throughout the manuscript and the supplementary\nmaterial that should be addressed.\n• The error bars should not be shown in yellow, as this color is hardly visible in the print version.\nResponse 1-7: We thank the Reviewer for this comment.\nWe sincerely apologize for any minor inaccuracies or oversights that may have influenced\nthe readability of the manuscript. We have carefully corrected the axis labels, spelling errors,\nand error bar colors as suggested by the Reviewer.\n Axis labels in the figures of the Supplementary Materials.\nFig. R1-10 (a) Original Fig. S16b. (b) Revised Fig. S16b\n Spelling errors.\n Revised line 100-104 of page 5 (Results and discussion section), “As a new-\ngeneration nonclassical light source, the superbunching super-bunching light exhibits\ngiant second-order intensity correlation (g2(τ)) and extreme multi-photon emission\nprobabilities. The normalized intensity correlation, g2(0), of the superbunching super-\nbunching light can be as high as 10, significantly stronger than that of thermal light\n(where g2(0) of thermal light equals 2, known as the bunching effect).”\n Revised line 496 of page 20 in the revised manuscript, “Experiment setup. In the\noutput path, different channels were spatially separated by a blazed grating (600\nmm/line lines/mm) and subsequently directed to distinct positions on the target by a\ngalvanometer.”\n Revised line 108 of page 8 in the Supplementary Materials. “Fig. S6 (a) Temporal\nwaveform of the 633 nm spectral optical channel. The cross-correlation images for\nHe-Ne laser (b), sub-picosecond laser (c), and super-bunching laser light (d)”\n Error bars in Fig. 2 and Fig. 4. Fig. 2 and Fig. 4 have been revised as shown in Fig. R1-\n11 and Fig. R1-12.\nFig. R1-11 Revised Fig. 2\nFig. R1-12 Revised Fig. 4.\nReferences\n[R1]. Zhao H, Li B, Li H, Li M. Enabling scalable optical computing in synthetic frequency\ndimension using integrated cavity acousto-optics. Nature Communications 13, 5426\n(2022).\n[R2]. Wan L, et al. Highly efficient acousto-optic modulation using nonsuspended thin-film\nlithium niobate-chalcogenide hybrid waveguides. Light: Science & Applications 11, 145\n(2022).\n[R3]. Riemensberger J, et al. Massively parallel coherent laser ranging using a soliton\nmicrocomb. Nature 581, 164-170 (2020).\n[R4]. McCarthy A, et al. High-resolution long-distance depth imaging LiDAR with ultra-low\ntiming jitter superconducting nanowire single-photon detectors. Optica 12, 168-177\n(2025).\n[R5]. Chen R, et al. Breaking the temporal and frequency congestion of LiDAR by parallel\nchaos. Nature Photonics 17, 306-314 (2023).\n[R6]. Lukashchuk A, Riemensberger J, Tusnin A, Liu J, Kippenberg T. J. Chaotic microcomb-\nbased parallel ranging. Nature Photonics 17, 814-821 (2023).\nReviewer #2:\nComment 2-1: The study makes use of super-bunching lights, which has the following\nchallenges\n Power loss and inefficiency due to multiple stages of interaction with each stage involving\nloss of optical power.\n It requires sophisiticated setups which lead to additional expenses to build and maintain\n It also has integration challenges for practical use in LiDAR\n Detector overwhelming with a high photonon pulse from single flux can saturate the\ndetector causing inaccurate readings\n Complex statistics needed to interpret superbunching light, e.g., upturned-tail distributions,\nwhich complicates data processing and analysis\nResponse 2-1: We thank the Reviewer for this comment. We have addressed the five concerns\nraised by the Reviewer, including optical power loss, system complexity and low integration,\ndetector saturation, and complex data processing.\n Power loss and inefficiency due to multiple stages of interaction with each stage involving\nloss of optical power.\nThe description of the power loss pointed out by Reviewer inappropriate. Indeed, such a\nloss is inevitable in any optical experiments. Detailed power measurements were performed at\nmultiple stages of the parallel LiDAR system, as indicated by the red markers in Fig. R2-1. To\nensure accuracy of measurement, we conducted tests on the individual channel power. As\nshown in Table R2-1, the maximum power loss is less than 3dB which mainly originates from\nthe grating diffraction. However, it is negligible compared with the echo-to-emitted power ratio\n(33.7 dB) and has little effect on the ranging results.\nFig. R2-1 Schematic of power measurements.\nTable R2-1 Corresponding results of power measurements.\nPosition Power Loss/dB\nP1 803 μW -\nP2 591 μW\n0.02 ((591+208) μW/803 μW)\nP3 208 μW\nP4 305 μW (grating diffraction loss) 2.87 (305 μW/591 μW)\nP5 107 μW (grating diffraction loss) 2.89 (107 μW/208 μW)\nP6 130 nW 33.70 (130 nW/305 μW)\nAction Taken:\n Insert Fig. R2-1 and Table R2-1 as Fig. S20 and Table S2 on page 25 of the Supplementary\nMaterials.\n It requires sophisiticated setups which lead to additional expenses to build and maintain\n It also has integration challenges for practical use in LiDAR\nThis work primarily presents a proof-of-concept and initial application demonstration of\nspectrally encoded parallel LiDAR. The current LiDAR system consists of a pump laser,\nphotonic crystal fiber (PCF), acousto-optic tunable filter (AOTF), scanning mirrors, and\ndetectors at both the receiving and reference paths. The current implementation is cost-intensive\nand suffers from limited system integration. To reduce system costs and enhance integration,\nwe will implement modular upgrades, which are primarily divided into the following four\naspects:\n Integration of pump laser and PCF. By combining the 1064 nm picosecond fiber\nlaser with photonic crystal fiber, the space occupation of the system can be\nsignificantly reduced.\n On-chip AOTF. Numerous reports have been published on on-chip acousto-optic\nmodulators [7, 8]. Breakthroughs in on-chip AOTFs in the future will greatly improve\nsystem integration.\n Using an array of photodetectors in the reference path.\n Wavelength division multiplexers (WDMs) scheme. Advances in WDMs will\nprovide a more integrated alternative approach for spectrally encoded optical channel\ndivision in the future.\n Detector overwhelming with a high photonon pulse from single flux can saturate the\ndetector causing inaccurate readings\nThe avalanche photodetector (APD) used in this work operates in the linear mode rather\nthan the Geiger mode. The APD at the receiving path has a saturation power of ~100 μW,\nwhereas the received echo signal in this work is ~130 nW. These values indicate that the\ndetector is far from saturation and operates within its normal range.\n Complex statistics needed to interpret superbunching light, e.g., upturned-tail distributions,\nwhich complicates data processing and analysis\nAlthough the characterizations of the super-bunching light typically involves complex\nstatistics, our ranging process does not require such analysis. The following describes the data\nacquisition and processing workflow of spectrally encoded parallel LiDAR based on super-\nbunching light:\ni. The sequence signals of echoes and references, with a duration of 10 μs, are collected\nusing an oscilloscope (MSO58B, Tektronix), as illustrated in Fig. R2-2.\nFig. R2-2 The sequence signals of echoes and references.\nii. The cross-correlation between echoes y(t+Δt) and references x(t):\ni i\n𝑇\n𝑅(Δ𝑡 )=\n∫\n0\n𝑥𝑖 (𝑡)𝑦(𝑡+Δ𝑡𝑖 )𝑑𝑡\n,\ni\n√(∫\n0\n𝑇 |𝑥𝑖 (𝑡)|2𝑑𝑡)(∫\n0\n𝑇 |𝑦(𝑡+Δ𝑡𝑖 )|2𝑑𝑡)\nFig. R2-3 Cross-Correlation of echoes and references.\niii. The target distance for the i-th channel is calculated from the time delay corresponding\nto the extreme correlation peak in the cross-correlation of its echoes and references.\n𝑐×∆𝑡\ni\n𝐿 =\ni 2\nBased on the above discussion, we can conclude that although the integration of our system\nis challenging currently, a proof-of-concept and initial applications of our spectrally encoded\nparallel LiDAR have been demonstrated, which is believed to produce an integrated LiDAR\nsystem with small-scale and low-cost features in the near future.\nComment 2-2: Despite the claimed novelty of using the superbunching lights, experiments are\nnot very well designed and several aspects are not explored. For example, what is the superiority\nof superbunching lights if a similar performance can be achieved using other setups, such as\ncombo lights, which is easy to install and interpret, compared to practical challenges of\nsuperbunching lights?\nResponse 2-2: We thank the Reviewer for this comment.\nBased on the Reviewer’s suggestion, we compared our spectrally encoded parallel LiDAR\nwith advanced LiDAR technologies such as frequency-modulated continuous-wave (FMCW)\nLiDAR, single-photon LiDAR, and parallel chaotic LiDAR, as shown in Table R2-2.\nTable R2-2 Comparison of currently advanced parallel LiDARs\nParallel Chaotic\nParallel FMCW Single-Photon Spectrally Encoded\nLiDAR\nParameters LiDAR LiDAR Parallel LiDAR\n(Nature photonics\n(Nature 2020)[9] (Optica 2025)[10] (This work)\n2023)[11]\nSuperconducting\nAvalanche Avalanche\nBalanced Nanowire Single\nDetector Photodiode Photodiode\nphotodetector Photon Detector\n(APD) (APD)\n(SNSPD)\nNumber of\n30 1 51 51\nchannels\nSampling rate 10 us 1 ms 12.5 us 10 us\nRanging\n＜1 cm mm scale ＜2 mm ＜4 mm\naccuracy\nSpeed 5 mm/s\n5.1 mm/s\nmeasurement 20 mm/s \\ (Minimum\n(Minimum velocity)\ncapability velocity)\nIndividual\n5-20 mW ≤3.5 mW <1 mW[12] <1 mW\nchannel power\n45 m (human\nface) 1.73 m (A\n10 m (The static\n325 m (human simulated scene) \nface) 1.85 m (whiteboard,\n1000 m 11 m (cross-shaped\nrange 2.3 m (The EPFL A simulated scene)\n(Communications target) \nface) 40 m (whiteboards)\nmast)\nIntegration Fiber Space Chip Space\nThe parameters compared among the above LiDAR systems include detector\nconfiguration, number of channels, sampling rate, ranging accuracy, speed measurement\ncapability, light power of individual channel, detection range, and overall integration level. Our\ncore advantage lies in realizing parallel LiDAR without channel crosstalk and long-distance\nranging capability not limited by the pulse period, all at a relatively low system cost. FMCW\nLiDAR, while not limited by the pulse period, comes with a complex and costly system. Single-\nphoton LiDAR can perform ultra-long-distance ranging tasks; however, it lacks parallel ranging\ncapabilities, and the pixel acquisition time is relatively long. Chaotic micro-comb LiDAR\neliminates channel crosstalk in parallel LiDAR but cannot perform long-distance ranging tasks\ndue to the limitation of its propagation distance.\nAction Taken:\n Insert Table R2-2 as Table S1 on page 24 of the Supplementary Materials, and include\ndiscussion: “The core advantage of our spectrally encoded parallel LiDAR lies in realizing\nparallel LiDAR without channel crosstalk and long-distance ranging capability not limited\nby the pulse period, all at a relatively low system cost. FMCW LiDAR, while not limited by\nthe pulse period, comes with a complex and costly system. Single-photon LiDAR can\nperform ultra-long-distance ranging tasks; however, it lacks parallel ranging capabilities,\nand the pixel acquisition time is relatively long. Chaotic micro-comb LiDAR eliminates\nchannel crosstalk in parallel LiDAR but cannot perform long-distance ranging tasks due\nto the limitation of its propagation distance.”\nComment 2-3: A potential work in parallel LiDAR is that of et al., i.e., “Breaking the temporal\nand frequency congestion of LiDAR by parallel chaos”, published in Nature Photonics, which\northogonality of the chaotic comb lines which also eliminates the inter-channel interference, as\nwell as, simplifies the receiving architecture. It also offers non-cross-talk and high-precision\nparallel ranging. How the author put their work in comparison to that work\nResponse 2-3: We thank the Reviewer very much for this comment.\nChen R, et al. [Ref. 22 of the original manuscript] demonstrated a parallel chaotic micro-\ncomb LiDAR on a photonic integrated chip, overcoming channel crosstalk in both time and\nfrequency domains and partially simplifying the receiver architecture [11]. Compared with their\nwork, our spectrally encoded parallel LiDAR based on super-bunching light is less integrated\nbut offers advantages in long-distance ranging and spectral bandwidth.\n Ranging distance. In terms of long-distance ranging, the low power of the optical channels\ngenerated by the chaotic micro-comb limits its applicability. In their work, the maximum\ndemonstrated ranging distance was 1.85 m. Another related study [Ref. 40 of the original\nmanuscript] reported a ranging distance of 105 m [12]. Nonetheless, the optical signal\npropagated 104 m through the fiber, while the free-space propagation distance was ~1 m.\nIn our system, the individual channel power is ~400 µW, which supports long-distance\nranging up to 40 m.\n Spectral bandwidth. The chaotic micro-comb produces many comb lines, however, the\nlimited total spectral bandwidth (1520–1580 nm) restricts its applicability for multi-scene\nswitching. Super-bunching lights produced through PCF spans a wide spectral range (480–\n1750 nm), offering the potential for versatility across different application scenarios.\n The receiving architecture. As reported in Chen R, et al., avoiding interference between\nsymmetric combs centered on the pump frequency requires their separate collection,\nnecessitating at least two APDs at the receiver. Our scheme requires a single APD at the\nreceiver, resulting in a similar receiving architecture to theirs.\nComment 2-4: Similar works which break the time-of-flight limit has already been published,\ne.g. “Ultrafast Parallel LiDAR with Time-encoding and Spectral Scanning: Breaking the Time-\nof-flight Limit”. How the authors justify the novelty of their study.\nResponse 2-4: We thank the Reviewer for this comment.\nThe work mentioned by the Reviewer #2 first appeared on the preprint server arXiv and\nwas later published in APL Photonics in 2022 with the title “Ultrafast parallel single-pixel\nLiDAR with all-optical spectro-temporal encoding” [13]. [Ref. 21 of the original manuscript].\nAdditionally, this work is based on the “Time-stretch LiDAR as a spectrally scanned time-of-\nflight ranging camera” [14]. [Ref. 36 of the original manuscript]\nTheir work [13] employs a correlated time–frequency encoding scheme across 45\nwavelength channels to construct five parallel optical code division multiple access (OCDMA)\nchannels, effectively reducing the number of measurements from 45 to 9. At detection distances\nof 75 m and 25 m, it achieved 4.41 and 3.56 times the time-of-flight (ToF) speed limit,\ncorresponding to sampling rates of 8.82 MHz and 21.38 MHz. The “time-of-flight limitation”\ndiscussed in their work essentially refers to the same concept as the “pulse period limitation”\nmentioned in our manuscript, as both represent the constraint of the pulse period on the effective\ndetection distance. Benefiting from the programmable all-fiber encoder, the speed\nmultiplication factor can be flexibly reconfigured, paving the way for ultrafast single-pixel\nLiDAR capable of exceeding the conventional ToF limit.\nFor high-performance applications with stringent requirements on detection distance and\nangular field of view, their scheme inherently involves a trade-off among optical channel count,\ndetection distance, and scanning rate, as described by the equation in Ref [14]:\n𝑐 𝑐\n𝐿 = ∆𝑇 = ,\n𝑚𝑎𝑥\n2 2𝑓𝑟𝑒𝑝𝑁\nwhere L is the maximum detection distance, c is the light speed, ΔT is the flight time of\nmax\nsignals, f is the pulse repetition, and N is the number of channels.\nrep\nAlthough these studies have enhanced the acquisition rate and overcome the ToF limitation\nthrough pulse encoding techniques, their approaches also exhibit the following disadvantages:\ni. The parallel ranging capability of their system is determined by the number of\ntemporal codes. Increasing the number of temporal codes enhances the number of\nparallel channels; however, it also prolongs decoding time, degrades real-time ranging\nperformance, and raises hardware costs.\nii. Their system necessitates a modulation module capable of producing time-coded\npulses, as well as a synchronized receiving and decoding subsystem. The necessity of\ndecoding individual channel signals substantially increases the complexity of signal\nprocessing compared with conventional ToF LiDAR, imposing higher computational\nrequirements and leading to potential decoding latency during large-scale parallel\noperations.\niii. When different wavelength pulses share the same temporal code, their detection\nremains constrained by the pulse period.\nAs for our spectrally encoded parallel LiDAR, its design enables multi-wavelength\ndetection without being restricted by the pulse period.\ni. We employ a super-bunching light to realize crosstalk-free optical channel division\nand support large-scale parallel ranging without the need for temporal encoding and\ndecoding procedures.\nii. Ranging beyond the pulse period, as demonstrated in this work, is based on the\ntemporal photon bunching effect of super-bunching light. Theoretically, there is no\nlimitation on the detection distance if sufficient optical power is available. Fig. 1e in\nthe main text shows the cross-correlation results of 133.3 ns, where the pulse period\nis 12.5 ns.\nAction Taken:\n Update line 89-92 of page 4 (Introduction section): “Parallel LiDAR empowered by a\nsuper-bunching light overcomes the distance limitation of ranging imposed by pulse\nrepetition periods without intricate encoding and decoding programs [13, 15], enabling\nspatially parallel ranging beyond 40 m.”\n Add discussion in line 159 of page 7 (Results and discussion section): “In contrast to\noptical code division multiple access (OCDMA) systems requiring intricate encoding and\ndecoding [13, 15], our scheme enables ranging beyond the pulse periods with a simplified\ndesign. Moreover, OCDMA loses this capability when its channels share the same temporal\ncode.”\nComment 2-5: The height of the objects in Figure 3b is given in meters which is misleading. I\nbelieve, the height is in cm. please double check.\nResponse 2-5: We thank the Reviewer for this comment.\nWe apologize for the incorrect unit on the coordinate axis and have corrected it to cm.\nAdditionally, we have reviewed all figures in the main text and supplementary materials to\nconfirm that all units are now correct.\nAction Taken:\n Fig. 3b has been revised as shown in Fig. R2-4\nFig. R2-4 Revised Fig. 3b\nComment 2-6: Figure 3f shows that the objects such as pedestrian, helicopter and cybertruck\ncan be identified due to different correlation values based on surface reflectivity. How about those\nobjects (vehicles) with similar referelctivity; there are no experiments concerning those.\nIn addition, angle-dependent phase retardation values can also lead to different reflectivity.\nResponse 2-6: We thank the Reviewer for this comment.\nFig. 3f demonstrates that our LiDAR system is capable of differentiating objects with\nvarying reflectivity. Furthermore, our LiDAR system demonstrates high ranging accuracy,\nenabling it to resolve objects with identical reflectivity but varying distances. However, in\nscenarios where objects have the same distance and reflectivity, distinguishing the targets\nremains beyond the current capabilities of our system. We are actively developing color LiDAR.\nSince different colors produce unique spectral signatures, the system can distinguish objects\neven if their reflectivity is the same.\nComment 2-7: Despite the advantages of successive scans such as improved accuracy, reduced\ndrift, etc. in the proposed LiDAR, risk accumulating is possible threat. In addition, merging\nsuccessive scans also carry the risk of errors.\nResponse 2-7: We thank the Reviewer for this comment.\nFig. 3b shows the results of three successive vertical scans using three different channel\nconfigurations: {530, 539, …, 674 nm}, {533, 542, …, 677 nm}, and {536, 545, …, 680 nm}.\nBoth horizontal and vertical errors must be accounted for in the combination of multiple scan\nresults.\n Horizontal error. In our scheme, the horizontal error is negligible. Each optical channel\ncorresponds to a different wavelength and is spatially separated by a grating, illuminating\ndifferent locations on the target. As a result, for measurements on the same target, the\nspatial positions of the various wavelength channels remain almost constant, making\nhorizontal error negligible.\n Vertical error. The vertical error is determined by the repeatability of the scanning\ngalvanometers (Newport, FSM-300-01). With an accuracy of ≤0.262 mrad, the scanning\ngalvanometers produces a longitudinal error of 524 μm at a distance of 2 m, which is well\nbelow our ranging accuracy and therefore minimally affects the experimental results. We\nevaluated the performance of the scanning galvanometers at distances of 2 m and 10 m, as\nshown in the Extend Date. Video# 1. The position of the laser spot controlled by the\nscanning galvanometers exhibited no significant deviation over multiple scans.\nAction Taken:\n Add discussion in line 225 of page 9 (Results and discussion section): “The horizontal\nand vertical errors introduced during the merging of multiple scans are negligible.\n(See Supplementary Note IX)”\n Add the section “Supplementary Note IX. Errors in Merging of Multiple Scans” on page\n16 of the Supplementary Materials, and insert the above discussion into this section.\nComment 2-8: Although, inter-channel investigated using Gaussian-distributed noise sources,\ninter-LiDAR interference is not investigated. Even a modest interference of 10-20 photons per\npulse can effect accuracy by centimeters. With enhanced detection range, the probability of\nsuch interference needs to be considered.\nResponse 2-8: We thank the Reviewer for this comment.\nIn evaluating the anti-interference performance of LiDAR, noise arises from three sources:\nexternal signal sources, other optical channels of the LiDAR, and photon-counting/light\nintensity errors within the same channel.\n Noise arises from external signal sources. The anti-interference performance in such\nscenarios is demonstrated in Figs. 4c and 4d, showing that the system can maintain anti-\ninterference capability even when the noise level is 1000 times that of the echoes.\n Noise arises from other optical channels. There is no crosstalk between the optical\nchannels of the spectrally encoded parallel LiDAR, so this type of noise is negligible.\n Photon-counting errors within the same channel. Notably, the APD employed here\noperates in the linear mode rather than the Geiger mode, the corresponding saturation\npower is ~100 μW. To address the Reviewer's question, we further calculated the photon\ncount in the echoes using the measured optical power:\n𝑃 𝑃\n𝑁 = \n𝐸𝑝𝑢𝑙𝑠𝑒\n=\n𝑓𝑟𝑒𝑝\n=\n𝑓𝑟𝑒𝑝,\n𝐸𝑝ℎ𝑜𝑡𝑜𝑛 ℎ𝑣 ℎ𝑐/𝜆\nwhere E and E are the energy of the light and photon, P is the average power of the\npulse photon\nechoes, f is the pulse repetition, h is the Planck constant, υ is the laser frequency, c is the\nrep\nspeed of light, and λ is the laser wavelength. The number of photons is 5174 per pulse for\na wavelength of 633 nm corresponding to an echo power of 130 nW (The distance is 1.6\nm). The ranging accuracy remains unaffected by a counting error of 10–20 photons per\npulse in this scenario.\nAs a laser propagates through the atmosphere, the number of photons decreases with\ndistance, following the Lambert–Beer law:\n𝑁(𝐿) = 𝑁 𝑒−𝛼𝐿\n0\nwhere L is the propagation distance, N is the original number of photons, and α is the\n0\nattenuation factor. When the photon counts drops to 10–20 photons, the corresponding\npropagation distance ranges from 0.8~1.6 km in heavy haze (α = 3.7~7.8 km-1) to\n39~104 km under clear atmospheric conditions (α = 0.06–0.16 km-1). In these ranging\nconditions, deviations on the order of several centimeters are expected, and LiDAR\ntechnologies employed in autonomous driving seldom encounter such scenarios.\nComment 2-9: The last sentence of the Abstract overstates the achievements of the proposed\nLiDAR.\nResponse 2-9: We thank the Reviewer for this comment.\nWe have revised the sentence in the Abstract.\nAction Taken:\n Update line 40 of page 2 (Abstract section), replacing “Combining high precision,\nsensitivity, long-distance detection, dynamic target acquisition, precise 3D reconstruction,\nand robust anti-interference, the spectrally encoded parallel LiDAR paves the way for next-\ngeneration intelligent systems.” with “Combining high precision, sensitivity, long-distance\ndetection, dynamic target acquisition, precise 3D reconstruction, and robust anti-\ninterference, the spectrally encoded parallel LiDAR offers significant potential for\nenhancing environmental perception technologies.”\nComment 2-10: 1650nm being hazardous to eye safety, safety concerns should be properly\ninvestigated.\nResponse 2-10: We thank the Reviewer for this comment. The 1650 nm wavelength mentioned\nby the Reviewer is not employed in the LiDAR system proposed in this work. Does the\nReviewer mean that 1560 nm is a widely adopted eye-safe LiDAR wavelength, while our\nsystem operates in the visible range?\nCurrently, the AOTF employed in our system exhibits strong filtering performance in the\nvisible band. Through this AOTF, we have demonstrated the key advantages and application\npotential of spectrally encoded parallel LiDAR in the visible band.\nWe assessed the safety of our near-infrared LiDAR. With an emission power of 546 µW\nand an echo power of 130 nW, the system operates at a power level considered harmless.\nSince 1560 nm is a more common choice for LiDAR, we have verified the feasibility of\nspectrally encoded parallel LiDAR using Infrared-AOTF (YSL Photonics, AOTF-PRO2) and\nWDMs at this wavelength range.\n Infrared-AOTF scheme. Fig. R2-4 shows that, in the infrared band, the AOTF can achieve\ncrosstalk-free optical channel division, but the minimum channel spacing is 40 nm. This\nhighlights that while spectral encoding parallel ranging in the infrared band based on\nInfrared-AOTF is feasible, the extensive channel spacing limits its performance in large-\nscale parallel ranging. Future advancements in AOTF technology are expected to facilitate\nscalable channel division and parallel ranging.\nFig. R2-4 Cross-correlation performance of the infrared AOTF. (a) Spectrum of individual 1480\nnm, 1550nm, and dual 1480/1550 nm. (b) Cross-correlation result of 1550/1550 nm, (c) Cross-\ncorrelation result of 1480/1550 nm.\n WDMs scheme. Given the maturity of WDMs in the infrared band, and inspired by the\nReviewer, we examined the channel division performance of CWDMs (DK Photonics,\n86.034534-006) and DWDMs (XYT Sharetop, XYT-DWDM-40CH-1R), respectively.\n Performance of CWDMs. Fig. R2-5 shows the demultiplexing results of super-\nbunching light based on 1530/1550/1570 nm CWDMs. The optical channel output by\nCWDMs exhibits a bandwidth of 21.6 nm. The cross-correlation results for the same\nchannel show an extreme correlation peak, and no crosstalk is observed between\n1530/1570 nm channels, indicating that the minimum spacing for crosstalk-free\noptical channel division using CWDMs is 40 nm.\nFig. R2-5 Cross-correlation performance of the CWDMs. The spectrum (a), cross-correlation\nresults of 1530/1530 nm (b) and 1530/1570 nm (c) of the 1530/1550/1570 nm CWDMs’ outputs.\n Performance of DWDMs. Fig. R2-6 shows the demultiplexing results of the\nDWDMs for C bands. Due to the low power of super-bunching light in the C-band,\nthe DWDMs employed here fails to perform the demultiplexing task, and the output\nsignal is found to be extremely weak. As an alternative, we assessed the\ndemultiplexing performance of the DWDM with a commercial 1560 nm laser. (NPI\nLASERS, Rainbow 1550 OEM) The spectral results are shown in Fig. R2-6. The\nDWDMs output channel has a bandwidth of 11.2 nm. Previous experience indicates\nthat when the power of super-bunching light is sufficient, DWDMs can successfully\nachieve crosstalk-free optical channel division with a channel spacing of ~20 nm.\nFig. R2-6 The spectrum of the 1530/1550/1570 nm DWDMs’ outputs.\nIn summary, both infrared-AOTFs, CWDMs, and DWDMs are capable of dividing\nchannels without crosstalk. A common drawback of these methods, however, is the relatively\nlarge channel spacing. Although they can be applied to spectrally encoded parallel LiDAR in\nthe infrared band, they are not sufficient for large-scale parallel ranging tasks that require a\nsignificant number of channels. It is hoped that future advancements in infrared-AOTFs and\nWDMs will support optical channel division with narrower channel spacing, thereby enabling\nthe development of more advanced infrared spectrally encoded parallel LiDAR.\nComment 2-11: What is the power usage of the proposed LiDAR in comparison to existing\nparallel LiDARs.\nResponse 2-11: We thank the Reviewer for this comment.\nFig. R2-7 displays the power for each channel in all optical channel configurations\nmentioned in the main text, including the 8 channels from Fig. 1d, 16 channels from Fig. 2e, 51\nchannels from Fig. 3b, 20 channels from Fig. 4a, and 16 channels from Fig. 4b. Furthermore,\nTable R2-2 in response 2-2 contains a comparison of power.\nAction Taken:\n Insert Fig. R2-7 as Fig. S19 on page 23 of the Supplementary Materials, and include the\ndiscussion “Fig. S19 displays the power for each channel in all optical channel\nconfigurations mentioned in the main text, including the 8 channels from Fig. 1d, 16\nchannels from Fig. 2e, 51 channels from Fig. 3b, 20 channels from Fig. 4a, and 16 channels\nfrom Fig. 4b.”\nFig. R2-7 Power of the optical channels used in the experiment. (a) 8-channel correlation\ndetection in Fig. 1d, (b) 16-channel parallel ranging in Fig. 2e, (c) 51-channel 3D imaging in\nFig. 3b, (d) 20-channel long-distance parallel ranging in Fig. 4a, and (e) 16-channel long-\ndistance imaging in Fig. 4b.\nComment 2-12: Performance comparison to existing parallel LiDARs must be carried out to\nshow the supremacy of the proposed prototype. For example, “A hybrid 3D LIDAR imager\nbased on pixel-by-pixel scanning and DS-OCDMA”, and “LiDAR pulse coding for high\nresolution range imaging at improved refresh rate” offer viable solution for high resolution\nimages.\nResponse 2-12: We thank the Reviewer for this comment.\nThe works mentioned by the Reviewer explored pulse coding methods for LiDAR to\nsimultaneously enhance image refresh rates and improve imaging resolution by optimizing the\ncoding scheme [15] [Ref. 11 of the original manuscript]. This approach draws inspiration from\ncode division multiple access (CDMA) systems in wireless communications, where each pixel\nis assigned a unique, quasi-orthogonal code sequence, improving acquisition efficiency and\nminimizing inter-channel crosstalk.\nHowever, applying these strategies to LiDAR technology presents greater challenges.\nOrthogonal coding of light can mitigate interference and improve refresh rates, yet digital\nLiDAR based on pseudo-random modulation requires high-speed optoelectronic components.\nComplex pulse coding increases both system complexity and cost. Achieving high resolution\nand fast refresh simultaneously requires precise pulse generation, broadband signal acquisition,\nand advanced decoding algorithms, placing significant demands on both hardware and software.\nIn this study, we present a novel parallel ranging scheme, emphasizing light source\nperformance, orthogonal-channel-based ranging capabilities, and robust anti-interference\nabilities to highlight its practical significance. The works mentioned by the Reviewer have a\nfocus that is substantially different from that of our work. Moreover, these studies primarily\nrelied on numerical simulations and lacked experimental validation, making performance\ncomparisons impractical. In response to the Reviewers’ recommendations and to provide a\nmore comprehensive comparison of parallel ranging methods, we have included an analysis of\nits advantages and disadvantages in the manuscript. Furthermore, we compared our spectrally\nencoded parallel LiDAR with advanced LiDAR technologies such as frequency-modulated\ncontinuous-wave (FMCW) LiDAR, single-photon LiDAR, and parallel chaotic LiDAR, as\nshown in Table R2-2. (See Response 2-2)\nAction Taken:\n Add discussion in line 67 of page 3 (Introduction section), “Following time-stretching\ntechnology, optical code division multiple access (OCDMA) effectively eliminates channel\ninterference through time-domain orthogonal coding of the light, while also offering\nconsiderable refresh rates and image resolution [13, 15]. Yet, the pseudo-random modulation\nemployed in the encoding and decoding process of the OCMDA scheme relies on high-\nspeed devices, which places high demands on both system hardware and software.”\n Add discussion in line 236 of page 9 (Results and discussion section), “While the\nOCDMA method must contend with a reduction in detection distance as channels are\nincreased, our approach delivers increased channels without any associated loss.”\nReferences\n[R7]. Zhao H, Li B, Li H, Li M. Enabling scalable optical computing in synthetic frequency\ndimension using integrated cavity acousto-optics. Nature Communications 13, 5426\n(2022).\n[R8]. Wan L, et al. Highly efficient acousto-optic modulation using nonsuspended thin-film\nlithium niobate-chalcogenide hybrid waveguides. Light: Science & Applications 11, 145\n(2022).\n[R9]. Riemensberger J, et al. Massively parallel coherent laser ranging using a soliton\nmicrocomb. Nature 581, 164-170 (2020).\n[R10]. McCarthy A, et al. High-resolution long-distance depth imaging LiDAR with ultra-low\ntiming jitter superconducting nanowire single-photon detectors. Optica 12, 168-177\n(2025).\n[R11]. Chen R, et al. Breaking the temporal and frequency congestion of LiDAR by parallel\nchaos. Nature Photonics 17, 306-314 (2023).\n[R12]. Lukashchuk A, Riemensberger J, Tusnin A, Liu J, Kippenberg T. J.. Chaotic\nmicrocomb-based parallel ranging. Nature Photonics 17, 814-821 (2023).\n[R13]. Zang Z, et al. Ultrafast parallel single-pixel LiDAR with all-optical spectro-temporal\nencoding. APL Photonics 7, 046102 (2022).\n[R14]. Jiang Y, Karpf S, Jalali B. Time-stretch LiDAR as a spectrally scanned time-of-flight\nranging camera. Nature Photonics 14, 14-18 (2019).\n[R15]. Kim G, Park Y. LIDAR pulse coding for high resolution range imaging at improved\nrefresh rate. Optics Express 24, 23810-23828 (2016).\nReviewer #3\nGeneral comment: This paper demonstrates a LiDAR implementation using photonic crystal\nfiber (PCF) to generate frequency-multiplexed super-bunching light, followed by second-order\ncorrelation measurements in each frequency channel. The advantage of this approach is that the\ngenerated super-bunching light has strong frequency correlations, which help reduce\nfrequency–time crosstalk, while also providing anti-interference capability. The authors present\nvarious results, including measurements of moving objects and three-dimensional imaging,\nmaking this a valuable and interesting work. I thank the authors for their contribution. Below\nare some questions and comments regarding the paper:\nGeneral response: We sincerely appreciate the Reviewer’s positive assessment of our work,\nhighlighting our demonstration of parallel LiDAR using the super-bunching light that achieves\ncrosstalk-free optical channel division and extends ranging beyond the pulse-period limitation.\nThe authors thank the Reviewer for the valuable suggestions to improve this work. We have\nprovided detailed responses after each corresponding comment.\nComment 3-1: In photon sources such as SPDC or SFWM, frequency correlations also exist\n[Jin-Hum Kim, Optica 8 (12), pp. 1524-1531 (2021), Ilya N. Chuprina, Phys. Rev. A 100,\n043843 (2019)], and super-bunching properties can be observed through second-order\ncorrelation measurements [J Mika et al, New J. Phys. 20 093002 (2018)]. Therefore, I believe\nsimilar experimental results could be obtained using such sources. Could the authors provide a\ncomparison between using SPDC/SFWM to generate superbunching light and using PCF,\nin terms of advantages and disadvantages?\nResponse 3-1: We thank the Reviewer for this comment.\nLiDAR applications can be realized using the super-bunching light generated by\nspontaneous parametric down-conversion (SPDC) or spontaneous four-wave mixing (SFWM)\nprocesses [16-19]. Phillip Blakey utilized SPDC to generate the super-bunching light and\ndemonstrated a quantum LiDAR based on time-frequency entanglement, which exhibited\nrobust imaging capability in noisy environments [18]. Ming-Da Huang proposed a frequency-\nmodulated continuous-wave (FMCW) quantum interferometer LiDAR that exploits quantum\nproperties to enhance measurement accuracy and resolution, surpassing the limitations of\nconventional FMCW LiDAR [19].\nPhoton pairs in these schemes exhibit momentum and polarization entanglement, enabling\ncorrelations across space, polarization, and time, and thereby achieving superior noise\nsuppression in two-photon coincidence imaging.\nHowever, the super-bunching light generated by these two approaches exhibits both an\nextremely narrow bandwidth (<1 nm) and a very low photon flux (~106 Hz) [20], restricting its\ndetection to single-photon detectors and making it unsuitable for frequency-division\nmultiplexing in parallel LiDAR applications. The insufficient light power also constrains\nranging to relatively short distances, generally within a few meters [21].\nAction Taken:\n Update line 111-113 of page 5 (Results and discussion section): “Figure 1b presents the\nspectrum of super-bunching light, spanning from 480 to 1750 nm, a feature absent in super-\nbunching light generated via spontaneous parametric down-conversion (SPDC) or\nspontaneous four-wave mixing (SFWM) [16, 17], which serves as the foundation for multi-\nchannel division in parallel LiDAR systems.”\nComment 3-2: In thermal light ranging, anti-interference features are also achievable [Jun Zhu,\nApplied Optics, 51 (20), pp. 4885-4890 (2012)], and frequency-channel separation can be\nimplemented using this method [experimental quantum illumination]. In this context, I would\nlike to ask: what is the specific advantage of using super-bunching light compared to simply\nusing bunching light?\nResponse 3-2: We thank the Reviewer for this comment.\nThe author agrees that dividing thermal light into frequency-channels, as proposed by the\nReviewer, is feasible for anti-interference ranging. Thermal-light-based ranging demonstrates\nstrong resistance to interference, while quantum illumination can achieve crosstalk-free\nfrequency-channel division. The primary differences between the thermal-light-based and\nsuper-bunching-light-based parallel LiDAR schemes are as follows. This discussion focuses on\ntwo categories of thermal light: broadband thermal light and pseudothermal light sources.\n Divergence angle of broadband thermal light. The super-bunching light exhibits a small\nbeam divergence, comparable to that of solid-state lasers, allowing it to maintain a small\nspot size over long distances (40 m in this work). In contrast, broadband thermal light has\na large beam divergence, limiting its effectiveness for long-distance parallel ranging.\n Bandwidth of pseudothermal light. Pseudothermal light sources generated via spatial\nmodulation are typically single-wavelength and unsuitable for parallel ranging [22, 23]. The\nsuper-bunching light generated by photonic crystal fiber (PCF) spans a broad spectral range\n(480–1750 nm). Using filtering devices, it enables crosstalk-free optical channel division\nand supports high-performance parallel ranging.\n Super-bunching light exhibits higher anti-interference capability. Spectrally coded\nparallel LiDAR mainly accomplishes the ranging by acquiring the cross-correlation\nbetween the echo signal and the reference signal. Consequently, the anti-interference\nperformance mainly originates from the intensity correlation between different photons,\nwhich can be partly characterized by their second-order correlations, i.e., the values of\ng(2)(0). The larger the value, the better the anti-interference performance [24]. In this work,\nthe g(2)(0) value for the super-bunching can be up to 10 (Fig. R3-1), while that value for\nthe thermal light is 2. In this case, the super-bunching light exhibits higher anti-interference\ncapability.\nFig. R3-1 Statistical properties of bunching and super-bunching Light. (a) Second-order\ncorrelation measurement of bunching light, (b) second-order correlation measurement of super-\nbunching light.\n Ranging distance. The super-bunching light employed in this work consists of picosecond\npulses, which concentrate more energy per pulse at the same average power, enhancing\nlong-distance ranging capability.\nComment 3-3: In Fig. 2(c) and Fig. 4(a), the error values are defined as the difference between\nthe actual distance and the distance measured by the experimental scheme (as noted around line\n173). Could the authors clarify how the actual distance was determined? Including this\ninformation in the paper would strengthen the reliability of the reported error values, since the\ncredibility of the error depends on the accuracy of the actual distance measurement method.\nResponse 3-3: We thank the Reviewer for this comment.\nIn Fig. 2c, the starting position of the whiteboard is taken as the reference, with the first\nmeasured point corresponding to a distance of 0 m. We measured the relative distance of the\nwhiteboard with respect to its initial position. A linear stepper motor stage (LTS150/M, Thorlabs)\nwas used to move the whiteboard, with a positioning error of ±0.6 μm, which is negligible\ncompared to our measurement errors.\nFor the long-distance test shown in Fig. 4a, we employed engineering surveying\ntechniques and used an electoral total station (Leica, TZ08) to calibrate the target’s position\naccurately. The electoral total station used has a precision of 1 mm (Measuring distances within\n1000 m), far smaller than the error in ranging measurements.\nAction Taken:\n Add discussion in line 190 of page 8 (Results and discussion section): “The starting\nposition of the whiteboard was set as the reference point to assess its relative distance in\nthe test.”\n Add discussion in line 337 of page 13 (Experiment section): “In the test for Fig. 2c, a\nlinear stepper motor stage (LTS150/M, Thorlabs) was used to move the whiteboard, with\na positioning error of ±0.6 μm, which is negligible compared to our measurement errors.”\n Update line 260-263 of page 10 (Results and discussion section): “Figure 4a presents the\nparallel ranging results for 20 whiteboards evenly spaced between 21 m and 40 m, whose\npositions were set and verified using an electoral total station (ETS) (See Experiment\nsection)”\n Add discussion in line 339 of page 13 (Experiment section): “In the test for Fig. 4a, an\nelectoral total station (Leica, TZ08) was employed to verify the positions of the\nwhiteboards. The electoral total station has a precision of 1 mm (Measuring distances\nwithin 1000 m), far smaller than the error in ranging measurements.”\nComment 3-4: In Supplementary Note VI, the authors conclude that “the detector’s\nperformance at the reference ending does not impact the ranging accuracy.” My interpretation\nis that this setup is somewhat similar to ghost imaging (or ghost spectroscopy) schemes [Ilaria\nGianani, Opt. Lett. 48(12), pp. 3299-3302 (2023)]. As seen in temporal ghost imaging [Daniele\nFaccio, Nature Photonics 10, pp. 150–152 (2016)], the temporal accuracy generally follows the\nperformance of the better detector between the reference and the other paths. Thus, I assume\nthat in this experiment, the reference detector’s performance does not limit the ranging accuracy\nbecause the detector at the output-path had higher performance. If the authors agree with this\ninterpretation, it may be helpful to state this more explicitly (e.g., if the detector at the output\npath outperforms the reference detector, then degradation of the reference detector does not\naffect the ranging accuracy). I also agree with the authors’ conclusion that this feature helps\nreduce system complexity and experimental costs.\nResponse 3-4: We sincerely appreciate the Reviewer’s insightful comment.\nDuring the test, we found that the ranging accuracy is primarily determined by the better-\nperforming detector at both the echo and reference paths. However, we did not initially realize\nthat this is analogous to ghost imaging, where the timing precision is governed by the better\ndetector. We appreciate the Reviewer’s insight and have included a discussion on this topic in\nthe manuscript.\nAction Taken:\n Add discussion in line 174 of page 7 (Results and discussion section): “The ranging\naccuracy of the LiDAR system relies on detectors with superior performance in both the\necho and reference paths, aligning with the setup used in ghost imaging [25, 26],” and\ndetailed explanations are provided on page 11 of the Supplementary Materials.\nComment 3-5: In Supplementary Note V, the term “super-bunching laser” is used. I would\nsuggest revising this expression, as the word “laser” usually refers to a coherent state in\nquantum optics with a second-order correlation value of 1, which may cause confusion. I would\nbe interested in the authors’ view on this point.\nResponse 3-5: We thank the Reviewer for this comment.\nWe apologize for the incorrect description in this statement. “Super-bunching light” is the\ncorrect term, consistent with the description in the main text. We have thoroughly reviewed all\ntext in the manuscript and supplementary materials to ensure that such errors are not repeated.\nComment 3-6: In Figure 1(d), the correlation coefficient was measured based on eight channels.\nI’m curious about the frequency range of each channel.\nResponse 3-6: We thank the Reviewer for this comment.\nFig. R3-2 presents the spectrum of the eight channels corresponding to Fig. 1d.\nFig. R3-2 Spectrum of the eight channels corresponding to Fig. 1d.\nAction Taken:\n Insert Fig. R3-2 as Fig. S7 on page 10 of the Supplementary Materials.\nReferences\n[R16]. Kim J. H., et al. Noise-resistant quantum communications using hyperentanglement.\nOptica 8, 1524-1531 (2021).\n[R17]. Mika J, et al. Generation of ideal thermal light in warm atomic vapor. New Journal of\nPhysics 20, 093002 (2018).\n[R18]. Blakey P. S., et al. Quantum and non-local effects offer over 40 dB noise resilience\nadvantage towards quantum lidar. Nature Communications 13, 5633 (2022).\n[R19]. Huang M. D., et al. Frequency-modulated continuous-wave quantum interferometric\nlidar with enhanced accuracy and resolution. Physical Review Applied 23, 034055\n(2025).\n[R20]. Zhang Z, et al. High-performance quantum entanglement generation via cascaded\nsecond-order nonlinear processes. npj Quantum Information 7, 123 (2021).\n[R21]. Fan X, et al. High-performance quantum imaging at telecom band with enhanced noise\nresilience. APL Photonics 10, 076115 (2025).\n[R22]. Y. Yan, et al., “Noise-tolerant correlated coincidence imaging based on super-correlated\nlight at 1550 nm”, arXiv:2509.15770 (2025).\n[R23]. Yang Z, et al. Digital spiral object identification using random light. Light: Science\n&Applications 6, e17013-e17013 (2017).\n[R24]. Zhou Y, et al. Superbunching pseudothermal light. Physical Review A 95, 053809 (2017).\n[R25]. Faccio D. Temporal ghost imaging. Nature Photonics 10, 150-152 (2016).\n[R26]. Gianani I, Sánchez-Soto L. L., Goldberg A. Z., Barbieri M. Efficient line shape\nestimation by ghost spectroscopy. Optics Letters 48, 3299-3302 (2023)."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have provided a comprehensive and well-structured response letter that addresses all of my previous comments. I believe these revisions have improved the quality of the manuscript, and the previously unclear points have now been clarified.\nIn my opinion, the manuscript can be accepted for publication in its current form.", "reviewer_2": "I appreciate the wonderful efforts by the authors to revise the paper as per my comments. I have the following minor comments for the revised version:\n1. The work presents a proof-of-concept for the LiDAR. Please add challenges to its practical application.\n2. Please add limitations of the proposed LiDAR including its inability to distinguish the objects with the same reflectivity, & placed within the same distance.", "reviewer_3": "Thank you for your response regarding my comment. I support the publication of this paper due to its interesting work and the technically sound experimental data. However, I would like to raise a minor point.\nFirstly, the term \"orthogonal\" seems inappropriate when we only observe the correlation value data in Figures 1(c) to (e). The correlation values of other peaks are relatively high compared to the main peak. This could be primarily attributed to the use of a periodically pulsed laser. Additionally, Equation (2) indicates that the correlation calculation method used is more similar to second-order correlation in quantum optics, as this experiment measured the light intensity as the signal. If the detector directly measured the light field, Equation (2) would yield Pearson's correlation coefficient. I believe the actual orthogonality among the spectrally separated optical channels exists experimentally, but the data does not strongly support this due to these reasons. I think the experiment using a continuous pump laser and cross-correlation calculation with the Pearson correlation coefficient will clearly demonstrate orthogonality (the maximum correlation value except the main peak would be approximately 0). However, I believe that some readers consider it challenging to validate the orthogonality between each channel, so I prefer to use the term \"quasi-orthogonal\" in this context.\nSecondly, while the Supplementary Note XIV provides the table comparing the experimental performance metrics of each method, I believe it would be more beneficial to include the table of advantages and limitations for different LIDAR methods. (For example, in terms of spatial detection range and individual channel power limitations.) This would offer readers a clearer understanding of the actual limitations and trade-offs in each LIDAR method. For instance, while space detection range is influenced by factors other than laser power, whether individual channel power has its own inherent limits by generation method of the light signal. The table in Supplementary Note XIV is based on experimental data, and considering that multiple experiments could be considered proof-of-concept, I think the table does not fully capture the actual limitations of each item.\nIn conclusion, despite these minor concerns, I remain supportive of the paper's publication. The findings are interesting, and the technical rigor of the experimental design is commendable."}, "author_response": "Response Letter to Reviewers\nThank all the Reviewers for the helpful and instructive comments. We sincerely\nappreciate the valuable suggestions from all the Reviewers, which have significantly\nimproved our manuscript. We have addressed each of the Reviewers’ questions point\nby point and revised the manuscript accordingly. Below are our detailed responses to\nthe Reviewers’ comments:\nReviewer #1:\nGeneral comment: The authors have provided a comprehensive and well-structured\nresponse letter that addresses all of my previous comments. I believe these revisions\nhave improved the quality of the manuscript, and the previously unclear points have\nnow been clarified.\nIn my opinion, the manuscript can be accepted for publication in its current form.\nGeneral response: We would like to express our sincere thanks to the Reviewer for\nreviewing our manuscript a second time and very positive evaluation of our manuscript,\nespecially concerning the “wavelength division multiplexing scheme,” which has\nguided the direction of our future work. We appreciate the Reviewer for recommending\nthe manuscript for publication in Nature Communications.\nReviewer #2:\nGeneral comment: I appreciate the wonderful efforts by the authors to revise the paper\nas per my comments. I have the following minor comments for the revised version:\nGeneral response: We sincerely thank the Reviewer for reviewing our work a second\ntime. We really appreciate the Reviewer’s recognition of our work. In response to the\nsuggestions raised by Reviewer #2, we have revised the manuscript accordingly.\nComment 2-1: The work presents a proof-of-concept for the LiDAR. Please add\nchallenges to its practical application.\nResponse 2-1: We thank the Reviewer for this comment.\nAs outlined in the response letter to the first round of revisions, the proposed\nspectrally encoded parallel LiDAR will face substantial integration limitations that\nhinder its practical deployment. Furthermore, extending the system into the infrared\nband will allow for more versatile applications.\nAction taken:\n Add discussion in line 317 of page 12 (Conclusion section): “Further progress in\nsystem integration and extending the system’s capabilities to a broader range of\nspectral bands will speed up its practical application.”\nComment 2-2: Please add limitations of the proposed LiDAR including its inability to\ndistinguish the objects with the same reflectivity, & placed within the same distance.\nResponse 2-2: We thank the Reviewer for this comment.\nA discussion on this issue has been added to the supplementary materials.\nAction taken:\n Update line 244 of page 10 (Results and discussion section): “Beyond accurate\n3D reconstruction, spectrally encoded parallel LiDAR can also perform target-\nspecific identification by analyzing the correlation features in the echo signals.\n(See Supplementary Note X)”\n Add discussion in page 17 of the Supplementary Materials:\nSUPPLEMENTARY NOTE X\n“Target identification capability of LiDAR systems\nFig. 3f demonstrates that the proposed LiDAR system is capable of differentiating\nobjects with varying reflectivity. Furthermore, the LiDAR system demonstrates\nhigh ranging accuracy, enabling it to resolve objects with identical reflectivity but\nvarying distances. However, in scenarios where objects have the same distance and\nreflectivity, distinguishing the targets remains beyond the current capabilities of\nour system. The development of color LiDAR in the future will significantly\nimprove target recognition in this scenario.”\nReviewer #3:\nGeneral comment: Thank you for your response regarding my comment. I support the\npublication of this paper due to its interesting work and the technically sound\nexperimental data. However, I would like to raise a minor point.\nGeneral response: We sincerely thank the Reviewer for reviewing our work a second\ntime. We appreciate your insightful comments and suggestions, which we have\nincorporated into the revised version of the manuscript. We believe—after such\nthorough revision—that the revised manuscript is substantially improved, addressed all\npoints raised by the Reviewer, and therefore meets the criteria for publication in Nature\nCommunications.\nComment 3-1: Firstly, the term “orthogonal” seems inappropriate when we only\nobserve the correlation value data in Figures 1(c) to (e). The correlation values of other\npeaks are relatively high compared to the main peak. This could be primarily attributed\nto the use of a periodically pulsed laser. Additionally, Equation (2) indicates that the\ncorrelation calculation method used is more similar to second-order correlation in\nquantum optics, as this experiment measured the light intensity as the signal. If the\ndetector directly measured the light field, Equation (2) would yield Pearson's\ncorrelation coefficient. I believe the actual orthogonality among the spectrally separated\noptical channels exists experimentally, but the data does not strongly support this due\nto these reasons. I think the experiment using a continuous pump laser and cross-\ncorrelation calculation with the Pearson correlation coefficient will clearly demonstrate\northogonality (the maximum correlation value except the main peak would be\napproximately 0). However, I believe that some readers consider it challenging to\nvalidate the orthogonality between each channel, so I prefer to use the term “quasi-\northogonal” in this context.\nResponse 3-1: We sincerely thank the Reviewer’s insightful comments and suggestions.\nWe completely agree with the Reviewer’s suggestion to replace “orthogonal” with\n“quasi-orthogonal.”\nAs mentioned by the Reviewer, the super-bunching light, generated by pumping a\nphotonic crystal fiber with a pulsed laser, resulted in side lobes that showed\nconsiderable correlation value with the main peak, as shown in Figs 1c and 1e of the\nmanuscript. Channel orthogonality is only achieved when the correlation value of the\nside lobe is nearly 0.\nWhile the spectrally encoded optical channels based on super-bunching light do\nnot fully satisfy the channel orthogonality condition, we emphasize that this does not\naffect our ranging operation. The distance information is derived from the time delay\nof extreme correlation peaks, which are identified by as a PSLR greater than 2.\nAction taken:\n Update line 30 of page 2 (Abstract section): “Here, we introduce a novel\nspectrally encoded parallel LiDAR based on super-bunching light, exhibiting\nnegligible cross-correlation between different wavelengths, enabling orthogonal\nquasi-orthogonal, spectrally encoded optical channel division in the frequency\ndomain without inter-channel crosstalk.”\n Update line 83 pf page 4 (Introduction section): “In this work, we introduce a\nnovel parallel LiDAR equipped with spectrally encoded optical channels, where\nthe natural orthogonality quasi-orthogonality between different wavelengths of a\nsuper-bunching light is leveraged to eliminate time-frequency congestion between\nchannels.”\n Update line 104 of page 5 (Results and discussion section): “The natural\northogonality quasi-orthogonality between different wavelengths of a super-\nbunching light offers an opportunity to overcome the time-frequency congestion\nbetween channels.”\n Update line 133 of page 6 (Results and discussion section): “To validate the\northogonality quasi-orthogonality among the spectrally separated optical\nchannels, we conducted cross-correlation function measurements.”\n Update line 144 of page 6 (Results and discussion section): “Consequently, the\ntime-domain photon bunching effect of super-bunching light enables the division\nof frequency-orthogonal frequency-quasi-orthogonal, crosstalk-free optical\nchannels, which serve as spectrally encoded channels.”\n Update line 284 of page 11(Results and discussion section): “Owing to the\ntemporal photon bunching effect of super-bunching light and the mutual\northogonality quasi-orthogonality of its optical channels, spectrally encoded\nparallel LiDAR demonstrates robust anti-interference performance.”\nComment 3-2: Secondly, while the Supplementary Note XIV provides the table\ncomparing the experimental performance metrics of each method, I believe it would be\nmore beneficial to include the table of advantages and limitations for different LIDAR\nmethods. (For example, in terms of spatial detection range and individual channel\npower limitations.) This would offer readers a clearer understanding of the actual\nlimitations and trade-offs in each LIDAR method. For instance, while space detection\nrange is influenced by factors other than laser power, whether individual channel power\nhas its own inherent limits by generation method of the light signal. The table in\nSupplementary Note XIV is based on experimental data, and considering that multiple\nexperiments could be considered proof-of-concept, I think the table does not fully\ncapture the actual limitations of each item.\nResponse 3-2: We thank the Reviewer for this comment.\nWe have revised Table S1 of Supplementary Note XV. In the revised Table S1, we\nhave included details of light sources used for each LiDAR, along with a summary of\ntheir respective advantages and limitations.\nRevised Table S1 Comparison of currently advanced parallel LiDARs.\nComment 3-3: In conclusion, despite these minor concerns, I remain supportive of the\npaper’s publication. The findings are interesting, and the technical rigor of the\nexperimental design is commendable.\nResponse 3-3: The authors are very grateful to the Reviewer for the high evaluation of\nour work. We appreciate the Reviewer for supporting the manuscript for publication in\nNature Communications.\nRemark to both Reviewers\nHaving addressed the Reviewer’s remarks, we would like to ask their permission\nto correct, by our own initiative, a axis label in Fig.3b of the manuscript, where we\nfound and corrected a mistake. The unit of the Z-axis has been revised from ‘cm’ to\n‘mm’.\nFig. R1 Original Fig. 3b\nFig. R2 Revised Fig. 3b"}, {"round": 3, "reviewer_comments": {"reviewer_2": "The authors have resolved my comments and the paper now can be accepted.", "reviewer_3": "The authors have crafted a thorough and well-organized response letter that comprehensively addresses all of my previous comments. In my assessment, the manuscript is sufficiently robust and methodologically sound to warrant acceptance for publication in its current form. Their detailed and thoughtful reply demonstrates a clear understanding of my concerns. The authors have effectively acknowledged and responded to each of my points, thereby I am grateful and supportive of the papers publication"}, "author_response": ""}]

Decision letter