The limitations of digital electronics in handling real-time matrix operations for emerging computational tasks—such as artificial intelligence, drug design, and medical imaging—have prompted renewed interest in analog computing. Programmable integrated photonics (PIP) has emerged as a promising technology for scalable, low-power, and high-bandwidth analog computation. Although prior work has explored PIP implementations of quantum and neuromorphic computing, both approaches face significant limitations due to misalignments between their mathematical models and the native capabilities of photonic hardware. Building on the recently proposed analog programmable-photonic computation (APC)—a computation theory explicitly matched to the technological features of PIP—we introduce its critical missing component: an information theory. We present analog programmable-photonic information (API), a mathematical framework that addresses fundamental concepts beyond APC by examining the amount of information that can be generated, computed, and recovered in a PIP platform. API also demonstrates the robustness of APC against errors arising from system noise and hardware imperfections, enabling scalable computation without the extensive error correction overhead required in quantum computing. Together, APC and API provide a unified foundation for on-chip photonic computing, offering a complementary alternative to digital, quantum, and neuromorphic paradigms, and positioning PIP as a cornerstone technology for next-generation information processing.
The limitations of digital electronics in handling real-time matrix operations for emerging computational tasks—such as artificial intelligence, drug design, and medical imaging—have prompted renewed interest in analog computing. Programmable integrated photonics (PIP) has emerged as a promising technology for scalable, low-power, and high-bandwidth analog computation. Although prior work has explored PIP implementations of quantum and neuromorphic computing, both approaches face significant limitations due to misalignments between their mathematical models and the native capabilities of photonic hardware. Building on the recently proposed analog programmable-photonic computation (APC)—a computation theory explicitly matched to the technological features of PIP—we introduce its critical missing component: an information theory. We present analog programmable-photonic information (API), a mathematical framework that addresses fundamental concepts beyond APC by examining the amount of information that can be generated, computed, and recovered in a PIP platform. API also demonstrates the robustness of APC against errors arising from system noise and hardware imperfections, enabling scalable computation without the extensive error correction overhead required in quantum computing. Together, APC and API provide a unified foundation for on-chip photonic computing, offering a complementary alternative to digital, quantum, and neuromorphic paradigms, and positioning PIP as a cornerstone technology for next-generation information processing.
Quantum computing shows great potential in solving highly complex computational challenges in multiple applications, including artificial intelligence, chemical modelling, biology simulation, portfolio optimization, drug delivery, logistical planning, etc. Quantum networking can enable secure communication as well as scalable interconnects of multiple quantum processing units. We need to scale up the number of high-quality qubits and make quantum systems more compact, robust, and lower cost to further improve the performance of quantum computing and networking. Photonic integrated circuits (PICs) can play a significant role in reducing the size, improving the stability, and reducing the system bring-up time of optical subsystems. QuEra’s recent progress shows the potential of leveraging PICs as a viable solution to facilitate the builds of highly scalable and high-performance quantum computers [1]. These results position integrated photonics as a critical enabling technology for next-generation large-scale quantum systems and invite broader participation from the photonics community in advancing quantum technologies with significant industrial impact.
Programmable integrated photonics (PIP) has emerged as a powerful on-chip platform for optical signal processing and computing, enabling the implementation of reconfigurable N×N unitary matrix transformations through meshes of tunable interferometers, which realize 2$\times2unitarymatrices.However,theenergyconsumptionassociatedwithphase−shifteractuationisbecomingamajorlimitationtothescalabilityofPIPplatforms.Here,weintroduceageometricframeworkforenergyoptimizationinPIPcircuitsbyexploitingtherepresentationof2\times2unitarymatricesasconcatenationsofbasicEulerrotationsontheBlochsphere.WeshowthatequivalentimplementationsofthesameN\times$N unitary matrix (N ≥ 2) can exhibit markedly different energy costs depending on the rotation trajectories on the Bloch sphere implemented by each interferometer. Leveraging this insight, we identify minimum-energy configurations by systematically selecting the shortest rotation trajectories. We experimentally and numerically validate the proposed approach in diverse silicon PIP architectures, including feedforward and multipurpose hexagonal meshes, neural network accelerators, and photonic quantum-gate implementations. These results establish a general route toward more energy-efficient large-scale PIP processors for classical and quantum signal processing and computing applications.
Abstract Programmable photonic integrated circuits represent an emerging technology that amalgamates photonics and electronics, paving the way for light-based information processing at high speeds and low power consumption. Programmable photonics provides a flexible platform that can be reconfigured to perform multiple tasks, thereby holding great promise for revolutionizing future optical networks and quantum computing systems. Over the past decade, there has been constant progress in developing several different architectures for realizing programmable photonic circuits that allow for realizing arbitrary discrete unitary operations with light. Here, we systematically investigate a general family of photonic circuits for realizing arbitrary unitaries based on a simple architecture that interlaces a fixed intervening layer with programmable phase shifter layers. We introduce a criterion for the intervening operator that guarantees the universality of this architecture for representing arbitrary N×N N × N unitary operators with N+1 N + 1 phase layers. We explore this criterion for different photonic components, including photonic waveguide lattices and meshes of directional couplers, which allows the identification of several families of photonic components that can serve as the intervening layers in the interlacing architecture. Our findings pave the way for efficiently designing and realizing novel families of programmable photonic integrated circuits for multipurpose analog information processing.
Photonic quantum technologies require the integration of high-quality quantum light sources with programmable photonic circuitry to enable on-chip quantum information processing. Semiconductor quantum dots have emerged as a leading platform for quantum light generation, yet their monolithic integration with cryogenic-compatible, programmable photonic circuitry remains challenging. Here we demonstrate a programmable quantum photonic platform that enables simultaneous control of highly coherent quantum dot-based single photon emitters and reconfigurable optical elements at cryogenic temperatures. System-level integration enables individual electrical and optical addressing of active components on a single chip, allowing their concurrent operation while preserving coherent non-classical light emission. We demonstrate multiple quantum-photonic functionalities, including pure single-photon generation, two-photon interference, and resonant transmission and reflection measurements of integrated emitters. These results mark a step from isolated quantum-optical devices towards programmable quantum-photonic hardware platforms capable of combining coherent quantum emitters with large-scale photonic functionality.
Quantum simulation and computing traditionally has been based on two main paradigms, namely, digital and analog. In the digital paradigm, usually single and two-qubit gates (where qubit is an acronym for quantum bit) are employed as building blocks for scalable, universal quantum computing, although errors add up fast and error correction will be ultimately needed for scaling up. In the analog paradigm, large analog blocks are normally employed for a unitary dynamics that carries out the computation, enabling quantum operations on many qubits with reduced errors, but with the drawback of a limited choice of evolutions and lack of universality. In the past decade, a new paradigm has emerged, showing interesting possibilities for quantum simulation and computing in the near and mid term. This is the paradigm of digital-analog quantum technologies, which proposes to combine the best of both paradigms: large analog blocks, provided by native interactions of the employed quantum platform, enabling scalability, combined with digital gates, allowing for more versatility and, ultimately, universality. In this Perspective, I give an overview of the evolution of the field along the past decade, and an outlook for its future possibilities.
The growing scale and complexity of photonic quantum systems demand a transition from manual, heuristic control toward intelligent, autonomous operational frameworks. Quantum Artificial Intelligence (QAI) integrates machine learning, quantum-enhanced optimization, and real-time feedback to address key challenges in the calibration, stabilization, and scaling of photonic quantum technologies. Unlike conventional machine-learning-assisted quantum control and self-driving laboratory approaches that rely primarily on classical intelligence, the proposed QAI paradigm establishes a tighter integration between artificial intelligence and quantum hardware. Parameterized quantum circuits executed directly on the photonic processor participate in the agent’s decision-making process, while the same framework supports both real-time control tasks. QAI combines variational quantum neural networks, meta-reinforcement learning, multi-agent control architectures, and quantum optimization methods, such as the Quantum Approximate Optimization Algorithm, to enable adaptive calibration, error mitigation, and high-dimensional optimization in programmable photonic platforms. While this perspective focuses on real-time control and calibration, we also outline a conceptual pathway toward autonomous generative inverse design. To guide future development, we propose a three-stage roadmap that progresses from open-source hybrid quantum–classical control tools to fully autonomous laboratories capable of closed-loop self-optimization and experiment design. Experimental platforms include programmable silicon photonic circuits and lithium-niobate interferometers, where thermal drift, fabrication variability, and crosstalk require intelligent adaptation. Beyond quantum optics, the QAI framework has broader implications for quantum sensing, secure communications, and photonic computing. By introducing a unified QAI framework and a structured classification of Quantum Autonomy Levels (QALs), this work provides a roadmap toward autonomous quantum-optics laboratories and photonic quantum technologies.