Dirk Englund

Spontaneous parametric downconversion and four-wave mixing in χ(2) and χ(3) media underpin most entangled-photon sources, but direct generation of higher-order entangled multiphoton states by nth order parametric downconversion remains extremely challenging because conventional materials exhibit tiny high-order nonlinearities. Here, we show that single-layer Nb3Cl8, an excitonic Mott insulator on a breathing Kagome lattice, supports exceptionally large nonlinear susceptibilities up to the seventh order. Many-body GW–Bethe–Salpeter (BSE) and time-dependent BSE/Kadanoff–Baym simulations yield resonant χ(2)–χ(7) for monolayer Nb3Cl8, with |χ(4)| and |χ(5)| surpassing values in prototypical transition metal dichalcogenides by several orders of magnitude. We trace this enhancement to flat bands and strongly bound Frenkel excitons with ferroelectrically aligned out-of-plane dipoles. Building on experimentally demonstrated 1 × N integrated beam splitters with arbitrary power ratios, we propose an on-chip architecture where each output arm hosts an Nb3Cl8 patch, optionally gated by graphene to tune the complex n-photon amplitudes. Using the ab initioχ(3) and χ(4) values, we predict that three-photon GHZ3 and four-photon cluster-state sources in this platform can achieve n-photon generation rates up to ∼108 and ∼106 times larger, respectively, than silica-fiber- and MoS2-based implementations with comparable geometry. We derive the quantum Hamiltonian and explicit n-photon generation rates for this platform and show how suitable interferometric networks enable electrically and spectrally tunable GHZ, W, and cluster states based on genuine high-order nonlinear processes in a 2D excitonic Mott insulator.

Quantum technologies promise secure communication and advanced information processing, but scaling these systems remains a challenge. Diamond is a promising platform because it hosts defects that emit single photons and store quantum information with high stability. However, the conventional fabrication of diamond optical structures is slow and difficult to scale. Here, we present a manufacturing approach that moves diamond quantum photonics closer to industrial production. Instead of patterning each device directly on diamond, we create high-precision silicon masks in commercial foundries and transfer them onto diamond by using microtransfer printing. This enables large arrays of nanoscale optical structures while improving uniformity, yield, and throughput. Using this method, we demonstrate hundreds of diamond quantum microchiplets with enhanced optical performance and controlled coupling to quantum emitters. The chiplet approach also allows faulty devices to be replaced and supports integration with existing photonic and electronic systems, offering a scalable path toward practical quantum technologies.

Abstract Detecting single photons is a crucial process in quantum science, quantum networking, biology, and advanced imaging. To detect the small quantum of energy carried in a photon, conventional mechanisms rely on energy excitation across either a semiconductor bandgap or superconducting gap that hinders their applications to low-energy photons. Here, we detect single near-infrared photons using the thermal properties of Dirac fermions in graphene. By exploiting the extremely low heat capacity of Dirac electrons near its charge neutrality point, we observe a temperature rise up to ~ 2 K using a hybrid Josephson junction. In this proof-of-principle experiment, we achieve an intrinsic quantum efficiency of 87% (75%) with dark count &lt; 1 per second (per week), reaching an effective noise equivalent power of 2 × 10 −22 W/ $$\sqrt{{{{\rm{Hz}}}}}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msqrt> <mml:mrow> <mml:mi>Hz</mml:mi> </mml:mrow> </mml:msqrt> </mml:math> . The highest operation temperature is 1.2 K. Our results highlight the potential of graphene bolometers for detecting lower-energy photons from the mid-IR to microwave regimes, opening pathways to study space science in far-infrared regime, to potential applications in dark matter searches, and to advance quantum technologies across a broader electromagnetic spectrum.

The ever-increasing data demand craves advancements in high-speed and energy-efficient computing hardware. Analog optical neural network (ONN) processors have emerged as a promising solution, offering benefits in bandwidth and energy consumption. However, existing ONN processors exhibit limited computational parallelism, and while certain architectures achieve high parallelism, they encounter serious scaling up roadblocks for large-scale implementation. Here, we introduce a spatial-wavelength-temporal hyper-multiplexed ONN processor, which is based on parallel diffractive beam routing. The architecture supports high three-dimensional data, high O(N3) computing parallelism, and is feasible for large-scale implementation. A 16 × 16 parallel diffractive beam routing is demonstrated, enabling a large-scale (16 × 16 − by − 16 × 16), high-parallelism (4096 multiply-and-accumulates/shot (MACs/shot)), high-speed (2 Gsa/s), single-shot matrix-matrix multiplication (MMM) optical tensor processor. It accelerates convolutional neural networks (CNNs) and deep neural networks (DNNs) through parallel matrix multiplication. We demonstrate benchmark image recognition using a CNN and a subsequently fully connected DNN in the optical domain. The network works with an ultra-low optical energy of ≈ 20 attojoules (aJ)/MAC at 96.4% classification accuracy. The ONN system supports broad spectral and spatial bandwidths and is capable for large-scale scaling up, paving the way for highly efficient large-scale optical computing for next-generation deep learning. Optical neural network processors offering benefits in bandwidth and energy consumption but problems in scaling and parallelism. The author presenting a novel optical tensor processor capable of optically performing large-scale, high-speed matrix-matrix multiplication in a single step.

Data and additional information for the main manuscript.

Data and additional information for the main manuscript.

We introduce a framework for scalable and broadband free-space phase-matched four-wave mixing in ring resonators. This method for four-wave mixing reduces the complexity of coupling an emitter to a quantum network by combining the spatial and spectral interfaces between them into one nonlinear optical process. The device is compliant with current heterogeneous integration capabilities and has a bandwidth of 165 nm for efficient spatio-spectral conversion. We outline a fabrication-ready diamond-on-insulator pathway towards modular unit cells that natively bridge visible color centers to the infrared spectrum for scalable quantum networks. We also present and analyze an end-to-end framework for considering single-photon coupling efficiency from a color center to a quantum network. This framework represents a step forwards in analyzing and reducing system-scale losses in a spin-photon interface.

Quantum networks require interfaces translating memory photons to telecom wavelengths while controlling spatial modes; tasks performed by separate components today. We present a programmable alternative: a structured pump writes a virtual Bragg grating enabling simultaneous spatio-spectral conversion and real-time controlling of emission. Using a LiNbO$_3$ whispering-gallery resonator, we demonstrate 93\% spatial coupling and bidirectional conversion between 736\,nm and 1347\,nm. This reconfigurable interface eliminates cascaded losses and hardware modifications.

Inspired by the remarkable ability of plasmons to boost radiative emission rates, we propose leveraging acoustic graphene plasmons (AGPs) to realize tunable, giant Purcell enhancements for single-photon, entangled-photon, and multipolar quantum emitters. These AGPs are localized inside a cavity defined by a graphene sheet and a metallic nanocube and filled with a dielectric of thickness of a few nanometers and consisting of stacked layers of 2D materials, containing impurities or defects that act as quantum light emitters. Through finite-difference time domain (FDTD) calculations, we show that this geometry can achieve giant Purcell enhancement factors over a large portion of the infrared (IR) spectrum, up to 6 orders of magnitude in the mid-IR and up to 4 orders of magnitude at telecommunications wavelengths, reaching quantum efficiencies of 95\% and 89\%, respectively, with high-mobility graphene. We obtain Purcell enhancement factors for single-photon electric dipole (E1), electric quadrupole (E2), and electric octupole (E3) transitions and two-photon spontaneous emission (2PSE) transitions, of the orders of $10^{4}$, $10^{7}$, $10^{9}$, and $10^9$, respectively, and a quantum efficiency of 79\% for entangled-photon emission with high-mobility graphene at a wavelength of $λ=1.55$ $μ$m. Importantly, AGP mode frequencies depend on the graphene Fermi energy, which can be tuned via electrostatic gating to modulate fluorescence enhancement in real time. As an example, we consider the Purcell enhancement of spontaneous single- and two-photon emissions from an erbium atom inside single-layer (SL) WS$_2$. Our results could be useful for electrically tunable quantum emitter devices with applications in quantum communication and quantum information processing.

We present photonics intelligent design and optimization, a proof-of-concept multi-agent framework that converts natural-language photonic integrated circuit (PIC) design requests into layout mask files. This work demonstrates end-to-end PIC design automation using large language models (LLMs), with the goal of achieving structurally valid rather than performance-qualified layouts. We compare seven reasoning LLMs using a testbench of 102 design descriptions that ranged from single devices to 112-component PICs. The success rate for single-device designs was up to 91%. For design queries with ≤15 components, o1, Gemini-2.5-pro, and Claude Opus 4 achieved the highest end-to-end pass@5 success rates of ∼57%, with Gemini-2.5-pro requiring the fewest output tokens and the lowest cost. Future work will extend this framework toward performance qualification through expanded datasets, tighter simulation and optimization loops, and fabrication feedback integration.

High-speed thermal imaging in the long-wave infrared (LWIR) is critical for applications from autonomous navigation to medical screening, yet existing uncooled detectors are fundamentally constrained. Resistive bolometers are limited by electronic noise and the parasitic thermal load of wired readouts, while state-of-the-art nanomechanical resonators typically rely on vacuum packaging to maintain the mechanical $Q$ needed for sensitivity. Here, we introduce and demonstrate an uncooled thermal detector that addresses these challenges via an all-optical transduction mechanism. The heterogeneously integrated pixel is engineered for minimal thermal mass, combining pyrolytic carbon absorbers for broadband LWIR absorption, hollow zirconia structures for ultra-low-conductance thermal isolation, and a silicon photonic crystal cavity that serves as a high-$Q$ optical thermometer. Operating at ambient temperature and pressure, we measure a specific detectivity of $1.1\times10^{7}$ Jones and a thermal time constant of $27~μ\mathrm{s}$, corresponding to a speed that surpasses typical high-sensitivity uncooled technologies by an order of magnitude. Based on this detectivity measurement, which is limited by the noise floor of the external optical detection electronics, a physics-based model predicts a $&gt;25\text{-fold}$ performance enhancement to its fundamental thermorefractive-noise-limited value ($3\times10^{8}$ Jones). The optical readout remains functional across ambient and vacuum environments. We expect this architecture to provide a general route toward scalable, high-performance thermal imaging systems.

Advancements in light modulator technology have been driving discoveries and progress across various fields. The problem of large-scale coherent optical control of atomic quantum systems—including cold atoms, ions, and solid-state color centers—presents among the most stringent requirements. This motivates a new generation of high-speed large-scale modulator technology operating in the visible to near-infrared wavelength range. We introduce a scalable modulator technology based on piezoelectrically actuated silicon nitride resonant waveguide gratings fabricated on 200 mm diameter silicon wafers with CMOS-compatible processes. We present a proof-of-concept device with 4 × 4 individually addressable 50 μm × 50 μm pixels or channels, each containing a resonant waveguide grating with a ~ 780 nm design wavelength, supporting > 100 MHz modulation speeds, and a spectral response with > 20 dB extinction. Optical control of atomic quantum systems poses stringent requirements on modulators. Here, the authors present a piezoelectrically actuated silicon-nitride-based high-speed spatial light modulator technology meeting those needs.

Multiscale synergy -- the interplay of a system's distinct characteristic length, time, and energy scales -- is becoming a unifying thread across many contemporary branches of science. Ranging from moiré and super-moiré materials and cold atoms to DNA-templated superlattices and nested photonic networks, multiscale synergy produces behaviors not obtainable at any single scale alone. Yet a general framework that programs cross-scale interplay to steer spectra, transport, and topology has been missing. Here, we elevate multiscale synergy from a byproduct to a general design principle for emergent phenomena. Specifically, we introduce a scale-programmable framework for hierarchically nested lattices (HNLs) that can host quantum metamorphosis (QuMorph) -- a continuous evolution between system-dependent features governed by a dimensionless tunable parameter $α$ (the relative hopping). To exemplify, we show an HNL, in which as $α$ changes, the spectrum metamorphoses from integer quantum Hall-like to anomalous quantum Hall-like, passing through a cocoon regime with proliferating mini-gaps. This multiscale mixing yields multiple novel phenomena, including hybrid edge-bulk states, scale-dependent topology, topologically embedded flat bands, and isolated edge bands. We propose a feasible photonic implementation using commercially available coupled-resonator arrays, outline spatial-spectral signatures to map QuMorph, and explore applications for multi-timescale nonlinear optics. Our work establishes a scalable and programmable paradigm for engineering multiscale emergent phenomena.

We demonstrate $4π$-steradian vector magnetic field sensing using an ensemble of nitrogen-vacancy (NV) centers in a single-crystal diamond coupled to a microwave (MW) cavity. The MW cavity enhances the spin-photon coupling which enables efficient, high-contrast spin-state readout via MW interrogation and removes the need for bulky optical collection components. An applied AC bias magnetic field lifts the zero-field degeneracy of the four crystallographic NV orientations, allowing each orientation to be individually addressed and used for vector reconstruction of the magnetic field. The resulting magnetometer has a 40\% contrast (20x higher than typical for optical spin-ensemble readout) and achieves a single-axis sensitivity of 250 pT/$\sqrt{\mathrm{Hz}}$ which is flat from DC to 1 kHz. Noise models of the composite spin-cavity system establish MW amplitude noise as the dominant noise source and predict a thermal noise limit of 2 pT/$\sqrt{\mathrm{Hz}}$.

Optically linked solid-state quantum memories such as color centers in diamond are a promising platform for distributed quantum information processing and networking. Photonic integrated circuits (PICs) have emerged as a crucial enabling technology for these systems, integrating quantum memories with efficient electrical and optical interfaces in a compact and scalable platform. Packaging these hybrid chips into deployable modules while maintaining low optical loss and resiliency to temperature cycling is a central challenge to their practical use. We demonstrate a packaging method for PICs using surface grating couplers and angle-polished fiber arrays that is robust to temperature cycling, offers scalable channel count, applies to a wide variety of PIC platforms and wavelengths, and offers pathways to automated high-throughput packaging. Using this method, we show optically and electrically packaged quantum memory modules integrating all required qubit controls on chip, operating at millikelvin temperatures with &lt;3 dB losses achievable from fiber to quantum memory for the TE 0 mode at a wavelength of 737 nm.

Super-resolution microscopy has revolutionized the imaging of complex physical and biological systems by surpassing the Abbe diffraction limit. Recent advancements, particularly in single-molecule localization microscopy, have pushed localization below nanometer precision, by applying prior knowledge of correlated fluorescence emission from single emitters. However, achieving a refinement from 1 nm to 1 Ångström demands a hundred-fold increase in collected photon signal. This quadratic resource scaling imposes a fundamental barrier in single-molecule localization microscopy, where the intense photon collection is challenged by photo-bleaching, prolonged integration times, and inherent practical constraints. Here, we break this limit by harnessing the periodic nature of the atomic lattice structure. Applying this discrete grid imaging technique (DIGIT) in a quantum emitter system, we observe an exponential collapse of localization uncertainty once surpassing the host crystal's atomic lattice constant. We further applied DIGIT to a large-scale quantum emitter array, enabling parallel positioning of each emitter through wide-field imaging. Collectively, these advancements establish DIGIT as a competitive tool for achieving unprecedented, precise measurements, ultimately paving the way to direct optical resolution of crystal and atomic features within quantum and biological systems.

Semiconductor single-photon detectors cannot be straightforwardly adapted for the microwave regime, primarily because microwave photons carry far less energy and thus require cryogenic temperatures and specialized architectures. Here, we propose a hybrid spin-optomechanical interface to detect single microwave photons where the microwave photons are coupled to a phononic resonator via piezoelectric actuation. This phononic cavity also acts as a photonic cavity with either a single embedded Silicon-Vacancy (SiV−) center in diamond or an ensemble of these centers, bridging optical single-photon detection protocols into the microwave domain. We model the detection process as a communication channel whose capacity is quantified by the mutual information I(A; B) between the true photon occupancy (A) and the detector outcome (B). Depending on experimentally achievable parameters, simulations predict I(A; B) in the range $$0.57\,\ln (2)$$ to $$0.67\,\ln (2)$$ , corresponding to true-positive (detection) probabilities above 90% and false-positive (dark count) probabilities below 10% per detection interval. These results suggest a viable path to low-noise, high-efficiency single-photon detection at microwave frequencies.

Accurate evaluation of nonlinear photonic integrated circuits requires separating input and output coupling efficiencies (i.e., $η_1$ and $η_2$), yet the conventional linear-transmission calibration method recovers only their product (i.e., $η_1\,η_2$) and therefore introduces systematic bias when inferring on-chip performance from off-chip data. We present bidirectional nonlinear optical tomography (BNOT), a direction-aware metrology that uses forward and backward pumping of complementary nonlinear probes, with process-appropriate detection, to break the ``degeneracy'' of $η_1\,η_2$ and estimate individual interface efficiencies with tight confidence intervals. The method links off-chip measurements to on-chip quantities through a compact observation model that explicitly incorporates pump fluctuations and detector noise, and it frames efficiency extraction as a joint constrained optimization. Monte Carlo studies show unbiased convergence of the estimated efficiencies to ground truth with low error across realistic operating regimes. Using these efficiency estimates to reconstruct on-chip nonlinear figures of merit yields distributions centered on the true values with reduced variance, whereas conventional ``degenerate'' calibration is biased and can substantially misestimate on-chip performance. BNOT is hardware-compatible and platform-agnostic, and provides unbiased characterization of off- and on-chip coupling efficiencies across nonlinear processes, enabling reproducible, coupling-resolved benchmarking for scalable systems in quantum optics, frequency conversion, and precision metrology.

We present Ax-Prover, a multi-agent system for automated theorem proving in Lean that can solve problems across diverse scientific domains and operate either autonomously or collaboratively with human experts. To achieve this, Ax-Prover approaches scientific problem solving through formal proof generation, a process that demands both creative reasoning and strict syntactic rigor. Ax-Prover meets this challenge by equipping Large Language Models (LLMs), which provide knowledge and reasoning, with Lean tools via the Model Context Protocol (MCP), which ensure formal correctness. To evaluate its performance as an autonomous prover, we benchmark our approach against frontier LLMs and specialized prover models on two public math benchmarks and on two Lean benchmarks we introduce in the fields of abstract algebra and quantum theory. On public datasets, Ax-Prover is competitive with state-of-the-art provers, while it largely outperforms them on the new benchmarks. This shows that, unlike specialized systems that struggle to generalize, our tool-based agentic theorem prover approach offers a generalizable methodology for formal verification across diverse scientific domains. Furthermore, we demonstrate Ax-Prover's assistant capabilities in a practical use case, showing how it enabled an expert mathematician to formalize the proof of a complex cryptography theorem.

The development of color centers in silicon enables scalable quantum technologies by combining telecom-wavelength emission and compatibility with mature silicon fabrication. However, large-scale integration requires precise control of each emitter’s optical transition to generate indistinguishable photons for quantum networking. Here, we demonstrate a foundry-fabricated photonic integrated circuit (PIC) combining suspended silicon waveguides with a microelectromechanical (MEMS) cantilever to apply local strain and spectrally tune individual G-centers. Applying up to 35 V between the cantilever and the substrate induces a reversible wavelength shift of the zero-phonon line exceeding 100 pm, with no loss in brightness. Moreover, by modeling the strain-induced shifts with a digital twin physical model, we achieve vertical localization of color centers with sub-3 nm vertical resolution, directly correlating their spatial position, dipole orientation, and spectral behavior. This method enables on-demand, low-power control of emission spectrum and nanoscale localization of color centers, advancing quantum networks on a foundry-compatible platform. Precise control of color centers in silicon can enable scalable quantum photonic networks. Here, the authors demonstrate emission wavelength tuning and nanoscale vertical localization of individual quantum emitters within photonic integrated circuits via localized electromechanical strain.

Er-doped Al$_2$O$_3$ is a promising host for telecom-band integrated photonics. Here we combine ab initio calculations with a symmetry-resolved analysis to elucidate substitutional Er on the Al site (Er$_\mathrm{Al}$) in $α$-Al$_2$O$_3$. First-principles relaxations confirm the structural stability of Er$_\mathrm{Al}$. We then use the local trigonal crystal-field symmetry to classify the Er-derived impurity levels by irreducible representations and to derive polarization-resolved electric-dipole selection rules, explicitly identifying the symmetry-allowed $f$\textendash$d$ hybridization channels. Kubo--Greenwood absorption spectra computed from Kohn--Sham states quantitatively corroborate these symmetry predictions. Furthermore, we connect the calculated intra-$4f$ line strengths to Judd--Ofelt theory, clarifying the role of $4f$\textendash$5d$ admixture in enabling optical activity. Notably, we predict a characteristic absorption near $1.47~μ\mathrm{m}$ (telecom band), relevant for on-chip amplification and emission. To our knowledge, a symmetry-resolved first-principles treatment of Er:Al$_2$O$_3$ with an explicit Judd--Ofelt interpretation has not been reported, providing a transferable framework for tailoring rare-earth dopants in wide-band-gap oxides for integrated photonics. Our results for the optical spectra are in good agreement with experimental data.

The growing demand for low-latency, energy-efficient inference in large language models (LLMs) has catalyzed interest in heterogeneous architectures. While GPUs remain dominant, they are poorly suited for integration with emerging domain-specific accelerators like the Photonic Tensor Units (PTUs), which offer low-power, high-throughput linear computation. This motivates hybrid compilation strategies that combine photonic and electronic resources. We present LightCode, a compiler framework and simulator for mapping LLM inference workloads across hybrid photonic-electronic systems. LightCode introduces the Stacked Graph, an intermediate representation that encodes multiple hardware-specific realizations of each tensor operation. Hardware assignment is formulated as a constrained subgraph selection problem optimized for latency or energy under parametric cost models. We evaluate LightCode on the prefill stage of GPT-2 and Llama-7B showing that under our workload and hardware assumptions, (i) Photonic hardware reduced energy by up to 50% in our simulated workloads at maximum sequence length; (ii) multiplexing and assignment strategy yielded latency improvements exceeding 10x; and (iii) Optimizing for latency or energy resulted in distinct hardware mappings in our simulations. LightCode offers a module, foundational framework and simulator for compiling LLMs to emerging photonic accelerators.

We experimentally realize a wavelength–space–time multiplexed, high-parallelism optical tensor processor leveraging chromatic dispersion in a free-space diffraction grating and demonstrate batch DNN inference at 96.4% accuracy with optical energy consumption of 20 aJ/MAC.

Atom-like emitters in solids are promising platforms for quantum sensing and information processing, but inhomogeneities in the emitter fine structure complicate quantum control. We present a framework that leverages this diversity to reduce the resources for generating optically heralded spin cluster states across $N_q$ emitters from the conventional order $O(N_q)$ to $O(1)$ in ensembles of $N_q \sim 10$-$100$. An optimized pulse sequence simultaneously corrects pulse-length and detuning errors, achieving single-qubit gate fidelities exceeding $99.99\%$ for errors (normalized relative to the Rabi drive strength) up to 0.3, while maintaining fidelities above $99\%$ for errors as large as 0.4. Applied as a Carr-Purcell-Meiboom-Gill (CPMG) dynamical decoupling protocol to the dominant noise spectrum of silicon-vacancy centers in diamond, it enhances ensemble coherence times by over $7\times$ compared to interleaved bang-bang based CPMG. For state-of-the-art dilution refrigerators, global resonant optimal decoupling across $N_q$ spins sharply reduces heating, addressing the trade-off between the spin coherence and scaling to $N_q \gg 1$. We further introduce a modified single-photon entanglement protocol with an efficient algorithm for deterministic entanglement compilation. Depending on the decoupling time window, our method yields order $O(10^2$-$10^4)$ more entanglement links than bang-bang sequences, with theoretical guarantees of order $Ω(N_q)$ unique links, improvable by control tuning. Together, these techniques provide scalable tools - including global control, phase denoising, remote entanglement, and compilation - for robust quantum computing architectures with heterogeneous spin ensembles.

We present Photonics Intelligent Design and Optimization (PhIDO), a multi-agent framework that converts natural-language photonic integrated circuit (PIC) design requests into layout mask files. We compare 7 reasoning large language models for PhIDO using a testbench of 102 design descriptions that ranged from single devices to 112-component PICs. The success rate for single-device designs was up to 91%. For design queries with less than or equal to 15 components, o1, Gemini-2.5-pro, and Claude Opus 4 achieved the highest end-to-end pass@5 success rates of approximately 57%, with Gemini-2.5-pro requiring the fewest output tokens and lowest cost. The next steps toward autonomous PIC development include standardized knowledge representations, expanded datasets, extended verification, and robotic automation.

High-fidelity control of the thousands to millions of programmable qubits needed for utility-scale quantum computers presents a formidable challenge for control systems. In leading atomic systems, control is optical: UV-NIR beams must be fanned out over numerous spatial channels and modulated to implement gates. While photonic integrated circuits (PICs) offer a potentially scalable solution, they also need to simultaneously feature high-speed and high-extinction modulation, strong inter-channel isolation, and broad wavelength compatibility. Here, we introduce and experimentally validate a foundry-fabricated PIC platform that overcomes these limitations. Designed for Rubidium-87 neutral atom quantum computers, our 8-channel PICs, fabricated on a 200-mm wafer process, demonstrate an advanced combination of performance metrics. At the 795 nm single-qubit gate wavelength, we achieve a mean extinction ratio (ER) of 71.4 $\pm$ 1.1 dB, nearest-neighbor on-chip crosstalk of -68.0 $\pm$ 1.0 dB, and -50.8 $\pm$ 0.2 dB after parallel beam delivery in free-space. This high-performance operation extends to the 420 nm and 1013 nm wavelengths for two-qubit Rydberg gates, showing ERs of 42.4 dB (detector-limited) and 61.5 dB, respectively. The devices exhibit 10-90% rise times of 26 $\pm$ 7 ns, achieve dynamic switching to -60 dB levels within microsecond timescales, and show pulse stability errors at the $10^{-3}$ level. This work establishes a scalable platform for developing advanced large-scale optical control required in fault-tolerant quantum computers and other precision technologies.

This work proposes a prototype system for utilizing nitrogen-vacancy center-based quantum sensing for generalized threat detection systems. Changes to the operation or environment of an IC will cause differences in the magnetic field emanations, which can be detected through changes in a spin-state-dependent photocurrent within a diamond. Threat detection circuitry can be integrated within the sensitive CMOS IC itself at a high spatial resolution for real-time monitoring and spatially resolved low-overhead protections. The key contributions of this work are the CMOS and NV center system for high magnetometer sensitivity while maintaining CMOS design flexibility, the novel security application for quantum sensing, and the proposed method of heterogeneous integration for a complete system.

A central goal of quantum information science is transferring qubits between space, time, and modality. Spin-based systems in solids are promising quantum memories, but high-fidelity transfer of their quantum states to telecom optical fields remains challenging. Here, we introduce a phonon-mediated interface between spins in a diamond nanobeam optomechanical crystal and telecom optical fields by a simultaneous deep-subwavelength confinement of optical and acoustic fields with mode volumes $${V}_{{\rm{mech}}}/{\Lambda }_{{\rm{p}}}^{3} \sim 1{0}^{-5}$$ and Vopt/λ3 ~ 10−3, respectively. This confinement boosts the spin-mechanical coupling rate of Group-IV silicon vacancy (SiV−) centers by an order of magnitude to ~ 32 MHz while retaining high acousto-optical couplings. The optical cavity couples to the spin irrespective of the emitter’s native excited states, avoiding spectral diffusion. Using Quantum Monte Carlo simulations, we estimate heralded entanglement fidelities exceeding 0.96 between two such interfaces. We anticipate broad utility beyond diamond emitter-telecom systems to most solid-state quantum memories.

Phase transitions can dramatically alter system dynamics, unlocking new behavior and improving performance. Exceptional points (EPs), where the eigenvalues and corresponding eigenvectors of a coupled linear system coalesce, are particularly relevant for sensing applications as they can increase sensor response to external perturbations to a range of phenomena from optical phase shifts to gravitational waves. However, the coalescence of eigenstates at linear EPs amplifies noise, negating the signal-to-noise ratio (SNR) enhancement. Here, we overcome this limitation using nonlinearity, which exhibits exceptional SNR around a bistable transition point (BP). We couple a state-of-the-art diamond quantum sensor to a nonlinear Van der Pol oscillator, forming a self-oscillating hybrid system that exhibits both a single-valued and bistable phase. The boundaries between these phases are marked by both adiabatic and deterministic non-adiabatic transitions that enable chiral state switching and state coalescence at the BP. Crucially, NV magnetometry performed near the BP exhibits a 17x enhancement in SNR, achieving a record sensitivity of 170 fT/\sqrt{Hz}. This result surpasses the sensitivity limit of an ideal, thermally-limited electron magnetometer and resolves a long-standing debate regarding EP-like physics in advanced quantum sensing.

Photonic crystal cavities (PhCCs) can confine optical fields in ultra-small volumes, enabling efficient light-matter interactions for quantum and non-linear optics, sensing and all-optical signal processing. The inherent nanometric tolerances of micro-fabrication platforms can induce cavity resonant wavelength shifts two-orders of magnitude larger than cavity linewidths, prohibiting fabrication of arrays of nominally identical devices. We address this device variability by fabricating PhCCs as releasable pixels that can be transferred from their native substrate to a receiver where ordered micro-assembly can overcome the inherent fabrication variance. We demonstrate the measurement, binning and transfer of 119 PhCCs in a single session, producing spatially ordered arrays of PhCCs, sorted by resonant wavelength. Furthermore, the rapid in-situ measurement of the devices enables measurements of the PhCCs dynamic response to the print process for the first time, showing plastic and elastic effects in the seconds to hours range.

We experimentally realize a wavelength-space-time multiplexed, high-parallelism optical tensor processor leveraging chromatic dispersion in a free-space diffraction grating and demonstrate batch DNN inference at 96.4% accuracy with optical energy consumption of 20 aJ/MAC.

Silicon photonic crystal cavities (PhCC) exhibit high sensitivity to their surrounding environments, making them suitable for a diverse range of sensing applications. These applications include chemical sensing, optomechanical pressure sensing, displacement and radiation pressure measurements, as well as biosensing with potential applications in rapid medical and clinical diagnostics [1]–[3]. The integration of these devices with optical fibre, which offers additional benefits such as flexibility, robustness and immunity to electromagnetic interference [4], enables the development of highly sensitive sensors that can be utilised in challenging environments, such as cryostats or gas chambers.

Machine intelligence on edge devices enables low-latency processing and improved privacy, but is often limited by the energy and delay of moving and converting data. Current systems frequently avoid local model storage by sending queries to a server, incurring uplink cost, network latency, and privacy risk. We present the opposite approach: broadcasting model weights to clients that perform inference locally using in-physics computation inside the radio receive chain. A base station transmits weights as radio frequency (RF) waveforms; the client encodes activations onto the waveform and computes the result using existing mixer and filter stages, RF components already present in billions of edge devices such as cellphones, eliminating repeated signal conversions and extra hardware. Analysis shows that thermal noise and nonlinearity create an optimal energy window for accurate analog inner products. Hardware-tailored training through a differentiable RF chain preserves accuracy within this regime. Circuit-informed simulations, consistent with a companion experiment, demonstrate reduced memory and conversion overhead while maintaining high accuracy in realistic wireless edge scenarios.

Edholm's law predicts exponential growth in data rate and spectrum bandwidth for communications. Owing to exponentially increasing deep neural network computing demands and the slowing of Moore's law, new computing paradigms are required for future advanced communications like 6G. Optical neural networks (ONNs) are promising accelerators but struggle with scalability and system overhead. Here, we introduce our multiplicative analog frequency transform optical neural network (MAFT-ONN), an artificial intelligence hardware accelerator that experimentally computes fully analog deep learning on raw radio frequency (RF) signals, performing modulation classification that quickly converges to 95% accuracy. MAFT-ONN also exhibits scalability with nearly 4 million fully analog operations for MNIST digit classification. Because of the Shannon capacity-limited analog data movement, MAFT-ONN is also hundreds of times faster than traditional RF receivers.

The escalating data volume and complexity resulting from the rapid expansion of artificial intelligence (AI), Internet of Things (IoT), and 5G/6G mobile networks is creating an urgent need for energy-efficient, scalable computing hardware. Here, we demonstrate a hypermultiplexed tensor optical processor that can perform trillions of operations per second using space-time-wavelength three-dimensional optical parallelism, enabling O(N 2 ) operations per clock cycle with O(N) modulator devices. The system is built with wafer-fabricated III/V micrometer-scale lasers and high-speed thin-film lithium niobate electro-optics for encoding at tens of femtojoules per symbol. Lasing threshold incorporates analog inline rectifier (ReLU) nonlinearity for low-latency activation. The system scalability is verified with machine learning models of 405,000 parameters. A combination of high clock rates, energy-efficient processing, and programmability unlocks the potential of light for low-energy AI accelerators for applications ranging from training of large AI models to real-time decision-making in edge deployment.

Long distance quantum networking requires combining efficient spin-photon interfaces with long-lived local memories. Group-IV color centers in diamond (SiV, GeV, and SnV) are promising candidates for this application, containing an electronic spin-photon interface and dopant nuclear spin memory. Recent work has demonstrated state-of-the-art performance in spin-photon coupling and spin-spin entanglement. However, coupling between the electron and nuclear spins introduces a phase kickback during optical excitation that limits the utility of the nuclear memory. Here, we propose using the large hyperfine coupling of SnV-117 to operate the device at zero magnetic field in a regime where the memory is insensitive to optical excitation. We further demonstrate ground state spin control of a SnV-117 color center integrated in a photonic integrated circuit, showing 97.8% gate fidelity and 2.5 ms coherence time for the memory spin level. This shows the viability of the zero-field protocol for high fidelity operation, and lays the groundwork for building quantum network nodes with SnV-117 devices.

Solid-state quantum sensors based on ensembles of nitrogen-vacancy (NV) centers in diamond have emerged as powerful tools for precise sensing applications. Nuclear spin sensors are particularly well suited for applications requiring long coherence times, such as inertial sensing, but remain underexplored due to control complexity and limited optical readout efficiency. In this work, we propose cooperative cavity quantum electrodynamic (cQED) coupling to achieve efficient nuclear spin readout. Unlike previous cQED methods used to enhance electron spin readout, here we employ two-field interference in the NV hyperfine subspace to directly probe the nuclear spin transitions. We model the nuclear spin NV-cQED system (nNV-cQED) and observe several distinct regimes, including electromagnetically induced transparency, masing without inversion, and oscillatory behavior. We then evaluate the nNV-cQED system as an inertial sensor, indicating a rotation sensitivity improved by 3 orders of magnitude compared to previous solid-state spin demonstrations. Furthermore, we show that the NV electron spin can be simultaneously used as a comagnetometer, and the four crystallographic axes of NVs can be employed for vector resolution in a single nNV-cQED system. These results showcase the applications of two-field interference using the nNV-cQED platform, providing critical insights into the manipulation and control of quantum states in hybrid NV systems and unlocking new possibilities for high-performance quantum sensing.