arXiv subjects
Publications and source records attributed to Nicolas Fontaine.
We demonstrate OESCL-band same-wavelength bi-directional transmission over 60 km HCF with 42.5 THz bandwidth, achieving GMIs comparable with the highest unidirectional SMF data-rates in both directions, with an aggregate of 423.7 + 426.5 Tb/s.
We study the use of separate cores of a multicore erbium-doped fiber amplifier (MC-EDFA) in a noise-canceled link for ultrastable optical frequency transfer. We demonstrate fractional frequency instability of $5\times10^{-19}$ at 1000 s averaging time for the stabilized MC-EDFA alone and $1.4\times10^{-18}$ at 1000 s averaging time when integrated with a 40 km-long 7-core spooled fiber. This study further establishes multicore fiber (MCF) networks as a promising platform for ultrastable frequency transfer, serving as an important step toward incorporating precision time and frequency distribution into future MCF communication infrastructures.
Originally envisioned as a solution for the capacity crunch in telecommunications networks, multicore fibers (MCF) are contributing to scientific fields beyond telecom, such as sensing and metrology. Confined within the same cladding, the cores of MCF have a high degree of noise correlation which can be harnessed for a variety of applications. Here, we investigate MCF as a solution to the challenging problem of quantum and classical light co-existence in quantum networks by operating the quantum and stabilization light in separate but highly correlated cores of a 7-core MCF. Over 40 km of spooled fiber, we achieved 100 attosecond integrated jitter on one core by using phase information derived from another core. This allows for 100% duty cycle on a quantum channel while maintaining a low spurious photon rate from crosstalk between stabilization and quantum channels. With cycle-slip-free stabilization over 6 hours, frequency detuning between designated stabilization and quantum channels, and an additional 40 dB rejection of noise photons provided by the low optical crosstalk between cores, we achieved a Raman scattering-induced spurious photon rate of only 0.01 photons/s in 100 GHz bandwidth. Our results with MCF are a promising approach to ultra-stable quantum networks with 100% duty cycle on the quantum channel.
We demonstrate a wavelength division multiplexing (WDM) concept using demultiplexer-free frequency combs at both transmitter and receiver in a 4-wavelength 200-GHz-grid WDM system with flexible symbol rates, aiming to avoid the power-hungry wavelength control on demultiplexers.
We demonstrate dual-wavelength distributed acoustic sensing over 37 km of standard single-mode fiber using $\phi$-OFDR, utilizing a scalable hybrid-integrated dual-wavelength laser chip frequency-locked to a high-Q integrated SiN coil resonator.
The telecommunications industry's deployment of billions of kilometers of optical fiber has created a vast global network that can be exploited for additional applications such as environmental sensing, quantum networking and international clock comparisons. However, for reasons such as the unidirectionality of long-haul fiber links, telecom fiber networks cannot always be adapted for important applications beyond data transmission. Fortunately, new multicore optical fibers create the opportunity for application coexistence with data traffic, creating expansive multifunctional networks. Towards that end, we propose and demonstrate the faithful transfer of ultrastable optical signals through multicore fiber in a way that is compatible with the unidirectionality of long-haul fiber optic systems, demonstrating a fractional frequency instability of 3x10-19 at 10,000 seconds. This opens the door towards intercontinental optical clock comparisons, with applications in fundamental physics and the redefinition of the second.
We demonstrate dual-frequency stabilization of a hybrid-integrated multi-channel laser to an integrated high Q-factor silicon nitride (SiN) coil resonator with more than 40 dB frequency noise suppression. The frequency locked channels are utilized for a proof-of-concept fiber sensing experiment.
Quantum machine learning (QML) is one of the most promising applications of quantum computation. However, it is still unclear whether quantum advantages exist when the data is of a classical nature and the search for practical, real-world applications of QML remains active. In this work, we apply the well-studied quantum support vector machine (QSVM), a powerful QML model, to a binary classification task which classifies peptides as either hemolytic or non-hemolytic. Using three peptide datasets, we apply and contrast the performance of the QSVM, numerous classical SVMs, and the best published results on the same peptide classification task, out of which the QSVM performs best. The contributions of this work include (i) the first application of the QSVM to this specific peptide classification task, (ii) an explicit demonstration of QSVMs outperforming the best published results attained with classical machine learning models on this classification task and (iii) empirical results showing that the QSVM is capable of outperforming many (and possibly all) classical SVMs on this classification task. This foundational work paves the way to verifiable quantum advantages in the field of computational biology and facilitates safer therapeutic development.
We study the effect of an AC bias on the low frequency noise, notably $1/f^γ$ with $γ<2$, of metal-insulator-metal tunnel junctions at room temperature. The measurement is performed in the 6Hz-100kHz frequency range with an AC excitation above 1MHz. We observe that $1/f^γ$ noise is dominant across our measurements though the shape of the spectra varies. The effect of the DC excitation seems to be very different on the noise generated by the junction than that of the AC excitation, thus questioning the fact that the observed noise is due to resistance fluctuations that the bias only reveals.
We experimentally demonstrate optical detection at 12.5~bits per incident photon, 9.4~dB higher than the theoretical limit of conventional coherent detection. A single laser transmits both data and optical clock, undergoes 77~dB of attenuation before quantum detection followed by optical clock and data recovery.
We experimentally solve the problem of maximizing capacity under a total supply power constraint in a massively parallel submarine cable context, i.e., for a spatially uncoupled system in which fiber Kerr nonlinearity is not a dominant limitation. By using multi-layer neural networks trained with extensive measurement data acquired from a 12-span 744-km optical fiber link as an accurate digital twin of the true optical system, we experimentally maximize fiber capacity with respect to the transmit signal's spectral power distribution based on a gradient-descent algorithm. By observing convergence to approximately the same maximum capacity and power distribution for almost arbitrary initial conditions, we conjecture that the capacity surface is a concave function of the transmit signal power distribution. We then demonstrate that eliminating gain flattening filters (GFFs) from the optical amplifiers results in substantial capacity gains per Watt of electrical supply power compared to a conventional system that contains GFFs.
We experimentally achieve a 19% capacity gain per Watt of electrical supply power in a 12-span link by eliminating gain flattening filters and optimizing launch powers using machine learning by deep neural networks in a massively parallel fiber context.