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As the field of artificial intelligence is pushed forward, the question arises of how fast autonomous machines can learn. Within artificial intelligence, an important paradigm is reinforcement learning, where agents - learning entities capable of decision making - interact with the world they are placed in, called an environment. Thanks to these interactions, agents receive feedback from the environment and thus progressively adjust their behaviour to accomplish a given goal. An important question in reinforcement learning is how fast agents can learn to fulfill their tasks. To answer this question we consider a novel reinforcement learning framework where quantum mechanics is used. In particular, we quantize the agent and the environment and grant them the possibility to also interact quantum-mechanically, that is, by using a quantum channel for their communication. We demonstrate that this feature enables a speed-up in the agent's learning process, and we further show that combining this scenario with classical communication enables the evaluation of such an improvement. This learning protocol is implemented on an integrated re-programmable photonic platform interfaced with photons at telecommunication wavelengths. Thanks to the full tunability of the device, this platform proves the best candidate for the implementation of learning protocols, where a continuous update of the learning process is required.

Photonic system component counts are increasing rapidly, particularly in CMOS-compatible silicon photonics processes. Large numbers of cascaded active photonic devices are difficult to implement when accounting for constraints on area, power dissipation, and response time. Plasma dispersion and the thermo-optic effect, both available in CMOS-compatible silicon processes, address a subset of these criteria. With the addition of a few back-end-of-line etch processing steps, silicon photonics platforms can support nano-opto-electro-mechanical (NOEM) phase shifters. Realizing NOEM phase shifters that operate at CMOS-compatible voltages (≤ 1.2 V) and with low insertion loss remains a challenge. Here, we introduce a novel NOEM phase shifter fabricated alongside 90 nanometer transistors that imparts 5.63 radians phase shift at 1.08 volts bias over an actuation length of 25μm with an insertion loss of less than 0.04 dB and 3 dB bandwidth of 0.26 MHz.

Betalains are a family of natural pigments found exclusively in the plant order Caryophyllales. All members of this chemical family are biosynthesized through the common intermediate betalamic acid, which is capable of spontaneously condensing with various primary and secondary amines to produce betalains. Of particular interest is the red-violet betanin, most commonly obtained from Beta vulgaris (beet) as a natural food dye. We demonstrate the first complete microbial production of betanin in Saccharomyces cerevisiae from glucose, an early step towards a fermentation process enabling rapid, on-demand production of this natural dye. A titer of 17mg/L was achieved, corresponding to a color intensity obtained from 10g/L of beetroot extract. Further, we expanded the spectrum of betalain colors by condensing betalamic acid with various amines fed to an engineered strain of S. cerevisiae. Our work establishes a platform for microbial production of betalains of various colors as a potential alternative to land- and resource-intensive agricultural production.

A putative lipopeptide biosynthetic gene cluster is conserved in many species of Actinobacteria, including Mycobacterium tuberculosis and M. marinum, but the specific function of the encoding proteins has been elusive. Using both in vivo heterologous reconstitution and in vitro biochemical analyses, we have revealed that the five encoding biosynthetic enzymes are capable of synthesizing a family of isonitrile lipopeptides (INLPs) through a thio-template mechanism. The biosynthesis features the generation of isonitrile from a single precursor Gly promoted by a thioesterase and a nonheme iron(II)-dependent oxidase homolog and the acylation of both amino groups of Lys by the same isonitrile acyl chain facilitated by a single condensation domain of a nonribosomal peptide synthetase. In addition, the deletion of INLP biosynthetic genes in M. marinum has decreased the intracellular metal concentration, suggesting the role of this biosynthetic gene cluster in metal transport.

As Moore’s law and Dennard scaling come to an end, new devices and computing architectures are being explored. The development of computing hardware designed to address the rapidly growing need for computational power to accelerate artificial intelligence applications has prompted investigations into both. While silicon photonics is typically viewed as a communications platform, we discuss its application to artificial intelligence and some outstanding challenges to be addressed.

P. putida lysine metabolism can produce multiple commodity chemicals, conferring great biotechnological value. Despite much research, the connection of lysine catabolism to central metabolism in P. putida remained undefined. Here, we used random barcode transposon sequencing to fill the gaps of lysine metabolism in P. putida. We describe a route of 2-oxoadipate (2OA) catabolism, which utilizes DUF1338-containing protein P. putida 5260 (PP_5260) in bacteria. Despite its prevalence in many domains of life, DUF1338-containing proteins have had no known biochemical function. We demonstrate that PP_5260 is a metalloenzyme which catalyzes an unusual route of decarboxylation of 2OA to d-2-hydroxyglutarate (d-2HG). Our screen also identified a recently described novel glutarate metabolic pathway. We validate previous results and expand the understanding of glutarate hydroxylase CsiD by showing that can it use either 2OA or 2KG as a cosubstrate. Our work demonstrated that biological novelty can be rapidly identified using unbiased experimental genetics and that RB-TnSeq can be used to rapidly validate previous results., Despite intensive study for 50 years, the biochemical and genetic links between lysine metabolism and central metabolism in Pseudomonas putida remain unresolved. To establish these biochemical links, we leveraged random barcode transposon sequencing (RB-TnSeq), a genome-wide assay measuring the fitness of thousands of genes in parallel, to identify multiple novel enzymes in both l- and d-lysine metabolism. We first describe three pathway enzymes that catabolize l-2-aminoadipate (l-2AA) to 2-ketoglutarate (2KG), connecting d-lysine to the TCA cycle. One of these enzymes, P. putida 5260 (PP_5260), contains a DUF1338 domain, representing a family with no previously described biological function. Our work also identified the recently described coenzyme A (CoA)-independent route of l-lysine degradation that results in metabolization to succinate. We expanded on previous findings by demonstrating that glutarate hydroxylase CsiD is promiscuous in its 2-oxoacid selectivity. Proteomics of selected pathway enzymes revealed that expression of catabolic genes is highly sensitive to the presence of particular pathway metabolites, implying intensive local and global regulation. This work demonstrated the utility of RB-TnSeq for discovering novel metabolic pathways in even well-studied bacteria, as well as its utility a powerful tool for validating previous research.

Quantum algorithms for Noisy Intermediate-Scale Quantum (NISQ) machines have recently emerged as new promising routes towards demonstrating near-term quantum advantage (or supremacy) over classical systems. In these systems samples are typically drawn from probability distributions which --- under plausible complexity-theoretic conjectures --- cannot be efficiently generated classically. Rather than first define a physical system and then determine computational features of the output state, we ask the converse question: given direct access to the quantum state, what features of the generating system can we efficiently learn? In this work we introduce the Variational Quantum Unsampling (VQU) protocol, a nonlinear quantum neural network approach for verification and inference of near-term quantum circuits outputs. In our approach one can variationally train a quantum operation to unravel the action of an unknown unitary on a known input state; essentially learning the inverse of the black-box quantum dynamics. While the principle of our approach is platform independent, its implementation will depend on the unique architecture of a specific quantum processor. Here, we experimentally demonstrate the VQU protocol on a quantum photonic processor. Alongside quantum verification, our protocol has broad applications; including optimal quantum measurement and tomography, quantum sensing and imaging, and ansatz validation., Comment: Comments welcome. Updates references and acknowledgements

As Moore's law and Dennard scaling come to an end, new devices and computing architectures are being explored. The development of computing hardware designed specifically for machine learning has prompted explorations into both. While silicon photonics is typically viewed as a communications platform, it can serve as a particularly attractive computing platform for specific kinds of problems, including machine learning. Here, we will discuss our work towards realizing photonic matrix processors.

The electron-rich isonitrile is an important functionality in bioactive natural products, but its biosynthesis has been restricted to the IsnA family of isonitrile synthases. We herein provide the first structural and biochemical evidence of an alternative mechanism for isonitrile formation. ScoE, a putative non-heme iron(II)-dependent enzyme from Streptomyces coeruleorubidus, was shown to catalyze the conversion of (R)-3-((carboxymethyl)amino)butanoic acid to (R)-3-isocyanobutanoic acid through an oxidative decarboxylation mechanism. This work further provides a revised scheme for the biosynthesis of a unique class of isonitrile lipopeptides, of which several members are critical for the virulence of pathogenic mycobacteria., National Institutes of Health (U.S.). Molecular Biophysics Training Grant (T32 GM008313)

1-Alkenes are important platform chemicals that are almost exclusively produced from fossil hydrocarbons. Bioproduction of 1-alkenes can mitigate our dependence on declining petrochemical resources, thereby representing an important step in the field of green chemistry. Here, we report the discovery of a new family of membrane-bound desaturase-like enzymes that convert medium-chain fatty acids (10–16 carbons) into the corresponding 1-alkenes through oxidative decarboxylation. We further show that these desaturase-like enzymes could be efficient in transforming lauric acid to 1-undecene in E. coli compared to the existing 1-alkene biosynthetic enzymes. This work expands the enzyme inventory for the transformation of fatty acid precursors to hydrocarbons and promotes the industrial production of medium-chain 1-alkenes through microbial fermentation.

While “knowledge learning” about the outgroup has been regarded as one of the key mechanisms for the contact–prejudice relation since the contact hypothesis’ first inception (Pettigrew & Tropp, 2008), “learning,” more broadly, has rarely been used as an explanatory framework to investigate the consequences of intergroup contact. In this article, we lay the foundation of a learning model of anxiety and stress in ingroup–outgroup interactions. We distinguish between episodic and chronic anxiety responses to the outgroup and recommend investigations on the complexities of their dynamic interplay, as individuals accumulate and dynamically integrate their experiences with the outgroup over time. Through a review of established and emerging psychophysiological and behavioral research of anxiety during ingroup–outgroup interactions, we identify evidence consistent with this dynamic outlook of intergroup contact effects. In this context, we also advance novel and untested predictions for future investigations onto the temporal integration of contact effects during an individual’s lifespan.

A putative lipopeptide biosynthetic gene cluster is conserved in many species of Actinobacteria, including Mycobacterium tuberculosis and M. marinum, but the specific function of the encoding proteins has been elusive. Using both in vivo heterologous reconstitution and in intro biochemical analyses, we have revealed that the five encoding biosynthetic enzymes are capable of synthesizing a new family of isonitrile lipopeptides (INLPs) through a thio-template mechanism. The biosynthesis features the generation of isonitrile from a single precursor Gly promoted by a thioesterase and a non-heme iron(II)-dependent oxidase homologue, and the acylation of both amino groups of Lys by the same isonitrile acyl chain facilitated by a single condensation domain of a non-ribosomal peptide synthetase (NRPS). In addition, the deletion of INLP biosynthetic genes in M. marinum has decreased the intracellular metal concentration, suggesting the role of this biosynthetic gene cluster in metal transport.Significance StatementMycobacterium tuberculosis is the leading causative agent of tuberculosis (TB), of which millions of deaths occur annually. A putative lipopeptide biosynthetic gene cluster has been shown to be essential for the survival of this pathogen in hosts, and homologous gene clusters have also been found in all pathogenic mycobacteria and other species of Actinobacteria. We have identified the function of these gene clusters in making a new family of isonitrile lipopeptides. The biosynthesis has several unique features, including an unprecedented mechanism for isonitrile synthesis. Our results have further suggested that these biosynthetic gene clusters play a role in metal transport, and thus have shed light on a new metal transport system that is crucial for virulence of pathogenic mycobacteria.

We present a network switch in the silicon-on-insulator platform capable of redistributing light to arbitrary outputs with continuously reconfigurable splitting ratios. We demonstrate software defined switching and physical-layer multicast of gigabit Ethernet.

Beyond communication, silicon photonics is becoming a promising platform for both quantum and classical information processing. Here, we discuss our recent results on using programmable nanophotonic systems to simulate quantum phenomena found in solid-state and biological systems as well as the implementation of optical neural networks for deep learning.

Something very surprising has been happening in photonics recently: the same foundries and processes that were developed to build transistors are being repurposed to build chips that can generate, detect, modulate, and otherwise manipulate light. This is pretty counterintuitive, since the electronics industry spends billions of dollars to develop tools, processes, and facilities that lend themselves to building the very best transistors without any thought about how to make these processes compatible with photonics (with the obvious exception of the processes designed to make CMOS and CCD camera chips).

In the field of silicon photonics, it has only recently become possible to build complex systems. As system power constraints and complexity increase, design margins will decrease - making understanding device noise performance and device-specific noise origins increasingly necessary. We demonstrate a waveguide-coupled germanium metal-semiconductor-metal photodetector exhibiting photoconductive gain with a responsivity of 1.76 A/W at 5 V bias and 10.6±0.96 fF capacitance. Our measurements indicate that a significant portion of the dark current is not associated with the generation of shot noise. The noise elbow at 5 V bias is measured to be approximately 150 MHz and the high-frequency detector noise reaches the Johnson noise floor.

Large-scale quantum technologies require exquisite control over many individual quantum systems. Typically, such systems are very sensitive to environmental fluctuations, and diagnosing errors via measurements causes unavoidable perturbations. In this work we present an in situ frequency locking technique that monitors and corrects frequency variations in single photon sources based on microring resonators. By using the same classical laser fields required for photon generation as a probe to diagnose variations in the resonator frequency, our protocol applies feedback control to correct photon frequency errors in parallel to the optical quantum computation without disturbing the physical qubit. We implement our technique on a silicon photonic device and demonstrate sub 1 pm frequency stabilization in the presence of applied environmental noise, corresponding to a fractional frequency drift of, Comments welcome

Advances in photonic integrated circuits have recently enabled electrically reconfigurable optical systems that can implement universal linear optics transformations on spatial mode sets. This review paper covers progress in such “programmable nanophotonic processors” as well as emerging applications of the technology to problems including classical and quantum information processing and machine learning.

A large-scale tunable-coupling ring resonator array is demonstrated in a CMOS-compatible silicon photonics platform to achieve tunable frequency-dependent group delay. The system is proposed for a phase-encoded quantum data locking protocol.

We propose an on-chip nanophotonic system that do the neural network computing all in optical domain. Our system is able to give equivalent learning performance, while potentially achieve 3 orders of magnitude faster speed than conventional electronic neural nets.

We propose and analyze the design of a programmable photonic integrated circuit for high-fidelity quantum computation and simulation. We demonstrate that the reconfigurability of our design allows us to overcome two major impediments to quantum optics on a chip: it removes the need for a full fabrication cycle for each experiment and allows for compensation of fabrication errors using numerical optimization techniques. Under a pessimistic fabrication model for the silicon-on-insulator process, we demonstrate a dramatic fidelity improvement for the linear optics controlled-not and controlled-phase gates and, showing the scalability of this approach, the iterative phase estimation algorithm built from individually optimized gates. We also propose and simulate an experiment that the programmability of our system would enable: a statistically robust study of the evolution of entangled photons in disordered quantum walks. Overall, our results suggest that existing fabrication processes are sufficient to build a quantum photonic processor capable of high-fidelity operation.

Silicon micro-ring resonators have been demonstrated to be potential “on- chip” efficient sources of time-energy entangled photons. In these devices, photon pairs are generated by spontaneous four-wave-mixing (SFWM), an elastic scattering event involving two pump photons that results in the generation of an idler and a signal photon. The mass production of such integrated devices for quantum optic experiment requires the implementation of fast and reliable techniques to monitor the emitted two-photon state as well as the device performances. In this work we show that the joint spectral density of photon pairs that would be generated by SFWM in a silicon ring resonator can be measured with unprecedented resolution and speed, improving of several orders of magnitudes the results of traditional approaches based on coincidence measurements.

Quantum random walks (QRWs) implemented in photonic media have seen significant recent attention for their applicability to problems in quantum simulation and quantum transport. However, performing statistically robust and high-fidelity studies of these problems has required either manual tuning of optical elements or the fabrication of multiple integrated photonic chips. Here, we present our recent theoretical and preliminary experimental results on the role of disorder and decoherence in QRWs implemented in a large-scale, programmable nanophotonic processor (PNP).

We present an architecture for programmable nanophotonic processors capable of arbitrary discrete transformations for quantum and classical applications. A method to combat fabrication imperfections with high fidelity is discussed along with initial experimental results.

Environmental noise and disorder play critical roles in quantum particle and wave transport in complex media, including solid-state and biological systems. Recent work has predicted that coupling between noisy environments and disordered systems, in which coherent transport has been arrested due to localization effects, could actually enhance transport. Photonic integrated circuits are promising platforms for studying such effects, with a central goal being the development of large systems providing low-loss, high-fidelity control over all parameters of the transport problem. Here, we fully map the role of disorder in quantum transport using a nanophotonic processor consisting of a mesh of 88 generalized beamsplitters programmable on microsecond timescales. Over 64,400 transport experiments, we observe several distinct transport regimes, including environment-assisted quantum transport and the ''quantum Goldilocks'' regime in strong, statically disordered discrete-time systems. Low loss and high-fidelity programmable transformations make this nanophotonic processor a promising platform for many-boson quantum simulation experiments., Comment: 4 figures, 8 pages

A major challenge in nanofabrication is to pattern unconventional substrates that cannot be processed for a variety of reasons, such as incompatibility with spin coating, electron beam lithography, optical lithography, or wet chemical steps. Here, we present a versatile nanofabrication method based on re-usable silicon membrane hard masks, patterned using standard lithography and mature silicon processing technology. These masks, transferred precisely onto targeted regions, can be in the millimetre scale. They allow for fabrication on a wide range of substrates, including rough, soft, and non-conductive materials, enabling feature linewidths down to 10 nm. Plasma etching, lift-off, and ion implantation are realized without the need for scanning electron/ion beam processing, UV exposure, or wet etching on target substrates., United States. Dept. of Energy. Office of Basic Energy Sciences (Brookhaven National Laboratory. Contract DE-AC02-98CH10886), Alexander von Humboldt-Stiftung

With unprecedented resolution we measure the joint spectral density of photon pairs that would be generated by spontaneous four wave mixing in a silicon ring resonator and show how the quantum correlations can be tailored. © 2015 OSA.

Shared shuttle runs are an important factor in the microelectronics business ecosystem — allowing fables semiconductor companies to access advanced processes and supporting the development of new tools and processes. We report on the creation and progress of a shared shuttle program for access to advanced silicon photonics optoelectronic platforms. We present an 18 GHz integrated traveling-wave Mach-Zehnder modulator, with small-signal Vπ of 6V, and discuss sources of nonlinearity in our platform. A ring modulator is shown to have an SFDR IMD of 84 dB.Hz2/3. We present the first measurements of the nonlinear distortions of silicon-based traveling-wave modulators, and demonstrate an SFDR IMD of 103 dB.Hz2/3.

We present a traveling-wave Mach-Zehnder modulator operates at 25 Gb/s with 1 V pp . The 400 fJ/bit energy consumption is a 10× improvement over the best reported values in silicon Mach-Zehnders, becoming competitive with ring modulators.

Shared shuttle runs are an important factor of the microelectronics business ecosystem, allowing fabless semiconductor companies to access advanced processes and supporting the development of new tools and processes. We report on the creation and progress of a shared shuttle program for access to advanced silicon photonics optoelectronic platforms that we expect will create a similar environment for the field of integrated photonics.

OpSIS is a foundry service for silicon photonics offering open processes and low access costs. We present the success of our project in conventional applications and how it can enable breakthroughs in applied quantum optics.

We present a silicon photonics platform supporting 25 Gb/s data channels with monolithically integrated germanium photodetectors and silicon ring modulators along with yield and cross-wafer uniformity data.

We demonstrate an infrared spectrometer based on waveguide-coupled nanocavity filters in a planar photonic crystal structure. The input light is coupled into the waveguide, from which spectral components are dropped into the cavities and radiated off-chip for detection on a commercial InGaAs camera. The spectrometer has a footprint of only 60 μm by 8 μm. The spectral resolution is about 1 nm in the operation bandwidth of 1522–1545 nm. By substituting the membrane material and structure parameters, this design can be easily extended into the visible regime and developed for a variety of highly efficient, miniature photonic applications.

We demonstrate the generation of quantum-correlated photon pairs combined with the spectral filtering of the pump field by more than 95 dB on a single silicon chip using electrically tunable ring resonators and passive Bragg reflectors. Moreover, we perform the demultiplexing and routing of signal and idler photons after transferring them via an optical fiber to a second identical chip. Nonclassical two-photon temporal correlations with a coincidence-to-accidental ratio of 50 are measured without further off-chip filtering. Our system, fabricated with high yield and reproducibility in a CMOS-compatible process, paves the way toward large-scale quantum photonic circuits by allowing sources and detectors of single photons to be integrated on the same chip., National Science Foundation (U.S.). Graduate Research Fellowship (Grant 1122374), United States. Air Force Research Laboratory (Grant FA8750-14-2-0120)

There has been great interest in the silicon platform as a material system for integrated photonics. A key challenge is the development of a low-power, low drive voltage, broadband modulator. Drive voltages at or below 1 Vpp are desirable for compatibility with CMOS processes. Here we demonstrate a CMOS-compatible broadband traveling-wave modulator based on a reverse-biased pn junction. We demonstrate operation with a drive voltage of 0.63 Vpp at 20 Gb/s, a significant improvement in the state of the art, with an RF energy consumption of only 200 fJ/bit.

We present a silicon photonic platform that offers monolithically integrated silicon modulators and germanium photodetectors with sufficient device bandwidth (>19 GHz) to support 25 Gb/s data channels. Cross-wafer uniformity and yield data are included.

Compact silicon integrated devices, such as micro-ring resonators, have recently been demonstrated as efficient sources of quantum correlated photon pairs. The mass production of integrated devices demands the implementation of fast and reliable techniques to monitor the device performances. In the case of time-energy correlations, this is particularly challenging, as it requires high spectral resolution that is not currently achievable in coincidence measurements. Here we reconstruct the joint spectral density of photons pairs generated by spontaneous four-wave mixing in a silicon ring resonator by studying the corresponding stimulated process, namely stimulated four wave mixing. We show that this approach, featuring high spectral resolution and short measurement times, allows one to discriminate between nearly-uncorrelated and highly-correlated photon pairs., Fondazione Cariplo (Project 2010-0523, Nanophotonics for thin-film photovoltaics), Fondazione Alma Mater Ticinensis, Italy. Ministry of Education, Universities and Research (FIRB “Futuro in Ricerca” project RBFR08XMVY)

Breaking the reciprocity of light propagation in photonic integrated circuits (PIC) - especially in the CMOS-compatible silicon-on-insulator platform - is a topic of intense research. However, a practical solution for monolithic integrating of optical isolators and circulators remains elusive. Here, we propose and analyze a new non-reciprocal photonic architecture operating with standard single-mode waveguides (or optical fibers). Our design exploits cascaded phase modulators separated by optical delay lines and suitably driven by time shifted waveforms. Because it is based on fully balanced interferometers and does not involve resonant structures, our scheme is also intrinsically broadband. Using realistic parameters we calculate an extinction ratio superior to 20 dB and insertion loss below -3 dB., Comment: 11 pages, 5 figures

Bragg waveguides are promising optical filters for pump suppression in spontaneous four-wave mixing (FWM) photon sources. In this work, we investigate the generation of unwanted photon pairs in the filter itself. We do this by taking advantage of the relation between spontaneous and classical FWM, which allows for the precise characterization of the nonlinear response of the device. The pair generation rate estimated from the classical measurement is compared with the theoretical value calculated by means of a full quantum model of the filter, which also allows investigation of the spectral properties of the generated pairs. We find a good agreement between theory and experiment, confirming that stimulated FWM is a valuable approach to characterize the nonlinear response of an integrated filter, and that the pairs generated in a Bragg waveguide are not a serious issue for the operation of a fully integrated nonclassical source.

We introduce a reconfigurable silicon quantum photonic network for implementing general linear optics transformations in the spatial mode basis. This network enables implementation of a range of quantum algorithms; we discuss the phase estimation algorithm.