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. While 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. While 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.
Analog Programmable-Photonic Computation (APC) leverages programmable integrated photonics (PIP) to perform high-speed matrix operations using optical waves. However, the continuous nature of optical waves that implement the analog bits or anbits - the fundamental unit of information in APC - makes computational results intrinsically sensitive to physical noise. Here, we establish and experimentally validate a comprehensive noise analysis in APC platforms using the geometric representation of the anbit in the Generalized Bloch Sphere (GBS). By modeling the physical noise sources in PIP circuits as random photocurrent fluctuations at the output of the opto-electrical (O/E) converter, and using error propagation theory, the noise statistics can be projected onto the GBS. This approach leads to specific noise maps in the GBS for each noise source, enabling the identification of the dominant noise sources within the APC system. Analytical predictions are numerically and experimentally validated on a fabricated silicon PIP chip. Beyond statistical characterization of system noise, the proposed model provides quantitative design criteria for noise-adapted analog constellations in the GBS, advancing APC towards scalable and robust optical computing systems, with potential applications in emerging paradigms such as photonic neuromorphic computing.
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.
The growing computational demands of classical neural networks have intensified the search for energy-efficient and powerful computational alternatives. Quantum neural networks (QNNs) implemented on integrated photonic platforms offer a compelling avenue, offering exceptional computational power enhancements, with inherent programmability and scalability of integrated architectures. A critical challenge, however, is implementing the fundamental non-unitary and nonlinear activation function of QNNs within a linear quantum photonic system. Existing strategies, such as the adding ancillary qubits and measurement-based feedback or forward are constrained by high qubit resource costs, overhead devices, and poor cascadability. Here, we propose a novel deep photonic QNN with an expanded computational Hilbert space via input replication and mode expansion, which enables the realization of effective non-unitary and nonlinear activation on a linear programmable quantum photonic chip. This approach eliminates the need for physical ancillary qubits, measurement-induced qubit consumption and the measurement device burden, thereby significantly reduce resource costs. The fabricated chip integrates four high-quality entanglement sources and a programmable high-dimensional interferometric network, enabling a two-hidden-layer QNN that exhibits dimension-enhanced expressivity over the existing QNN architectures. We demonstrate its capabilities across diverse tasks, including nonlinear classification, image generation, and quantum Gibbs state preparation. This work establishes a scalable and efficient architecture toward practical quantum deep learning systems capable of tackling problems beyond the reach of classical computation.
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.
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.