Abstract This research presents a theoretical framework for realizing photonic quantum logic using nonlinear phase-space methods rooted in quantum optics. By modeling multi-mode interferometric networks through non-Gaussian transformations and operator-based Wigner dynamics, we construct an approach that bypasses the limitations of Gaussian-only computation. The work enables scalable, matterfree quantum information processing through light, supporting entanglement generation, squeezing control, and universal logic gate design. The proposed model contributes to the future of all-optical quantum computing by offering a mathematically rigorous path for implementing computation in quantum technologies. It serves both as a fundamental extension of optical quantum theory and as a practical tool for advancing light-based quantum devices.
Abstract This research presents a theoretical framework for realizing photonic quantum logic using nonlinear phase-space methods rooted in quantum optics. By modeling multi-mode interferometric networks through non-Gaussian transformations and operator-based Wigner dynamics, we construct an approach that bypasses the limitations of Gaussian-only computation. The work enables scalable, matterfree quantum information processing through light, supporting entanglement generation, squeezing control, and universal logic gate design. The proposed model contributes to the future of all-optical quantum computing by offering a mathematically rigorous path for implementing computation in quantum technologies. It serves both as a fundamental extension of optical quantum theory and as a practical tool for advancing light-based quantum devices.
The precise manipulation of quantum states of light lies at the heart of advancing photonic technologies, yet scalable solutions for hybrid photonic systems—where disparate optical modes such as polarization, time-bin, and frequency must coherently interact—remain a critical challenge. While current literature demonstrates various energy-conserving devices that effectively map between Hilbert spaces of unequal dimensions, there is a growing need for frameworks that address variable-dimensional transformations in a more unified manner. In this paper, we introduce an operator-level framework that systematically integrates both unitary and rectangular isometric energy-conserving transformations in multi-dimensional photonic systems. Unlike prior mode-specific approaches, our formalism enables modular modeling of hybrid optical modes within effective signal spaces, offering practical tools for scalable photonic quantum processors. By classifying these transformations into fixed- and variable-dimensional regimes, we derive universal design criteria for energy-conserving photonic signal processors. This work not only advances the fundamental understanding of photonic quantum processing but also delivers actionable tools for engineering next-generation scalable optical technologies, including multi-purpose, programmable photonic circuits, energy-efficient optical signal processing in reconfigurable networks, and high-dimensional quantum key distribution with hybrid mode encoding.
Encoding quantum information within bosonic modes offers a promising direction for hardware-efficient and fault-tolerant quantum information processing. However, achieving high-fidelity universal control over bosonic encodings using native photonic hardware remains a significant challenge. We establish a quantum control framework to prepare and perform universal logical operations on arbitrary multimode multi-photon states using a quantum photonic neural network. Central to our approach is the optical nonlinearity, which is realized through strong light-matter interaction with a three-level Λ atomic system. The dynamics of this passive interaction are asymptotically confined to the single-mode subspace, enabling the construction of deterministic entangling gates and overcoming limitations faced by many nonlinear optical mechanisms. Using this nonlinearity as the element-wise activation function, we show that the proposed architecture is able to deterministically prepare a wide array of multimode multi-photon states, including essential resource states. We demonstrate universal code-agnostic control of bosonic encodings by preparing and performing logical operations on symmetry-protected error-correcting codes. Our architecture is not constrained by symmetries imposed by evolution under a system Hamiltonian such as purely χ (2) and χ (3) processes, and is naturally suited to implement non-transversal gates on photonic logical qubits. Additionally, we propose an error-correction scheme based on non-demolition measurements that is facilitated by the optical nonlinearity as a building block. Our results pave the way for near-term quantum photonic processors that enable error-corrected quantum computation, and can be achieved using present-day integrated photonic hardware.
Photon-based quantum logic gate has substantial advantages over conventional atom-based designs as a result of a longer coherence time and an inherent compatibility with quantum communication protocol of flying qubits, photons. As a vital logic gate for universal quantum computing, the two-photon controlled-phase gate demands a few-photon nonlinearity, which historically suffers from either an indeterministic nature in the linear optics regime, or a weak nonlinearity within naturally-occurring materials in the nonlinear optics regime. It is intriguing yet challenging to deliver a logic gate design by exploiting a genuine fewphoton nonlinearity. In this dissertation, we study a particular one-dimensional quantum nanophotonic system that offers the desired few-photon nonlinearity through light-matter interactions in the quantum limit (i.e., between light quanta of photons and individual material formation of atoms). Towards this end, we study the exotic photonic trimer state generation to frst demonstrate few-photon nonlinearity. Then we report the breakdown of the non-Hermitian Hamiltonian for correlated multi-photon process to unveil the non-trivial effects of few-photon nonlinearity. Finally by exploiting such a nonlinearity, we present a deterministic two-photon controlled-phase gate proposal, and further showcase universal quantum logic gate designs in quantum nanophotonic systems.
Abstract Recent progress in nonlinear optical materials and microresonators has brought quantum computing with bulk optical nonlinearities into the realm of possibility. This platform is of great interest, not only because photonics is an obvious choice for quantum networks, but also as a promising route to quantum information processing at room temperature. We propose an approach for reprogrammable room-temperature photonic quantum logic that significantly simplifies the realization of various quantum circuits, and in particular, of error correction. The key element is the programmable photonic multi-mode resonator that implements reprogrammable bosonic quantum logic gates, while using only the bulk χ(2) nonlinear susceptibility. We theoretically demonstrate that just two of these elements suffice for a complete, compact error-correction circuit on a bosonic code, without the need for measurement or feed-forward control. Encoding and logical operations on the code are also easily achieved with these reprogrammable quantum photonic processors. An extrapolation of current progress in nonlinear optical materials and photonic circuits indicates that such circuitry should be achievable within the next decade.
Universal quantum computing requires an architecture that supports both linear circuits and, crucially, strong nonlinear resources. For quantum photonic systems, integrating such nonlinearities with scalable linear circuitry has been a major bottleneck, leaving most optical experiments without nonlinear operations and, consequently, incapable of achieving universality. Here, we report an extensible photonic computer that supports a universal gate set by seamlessly combining fully programmable, scalable linear optical networks with integrated nonlinear modules. This platform enables a broad range of quantum computing and simulation tasks. We demonstrate the quasi-deterministic generation of optical Gottesman-Kitaev-Preskill states, which are essential resources for bosonic error correction, yet had previously been realized only probabilistically. Furthermore, we simulate complex many-body quantum dynamics, exemplified by the Bose-Hubbard model. Such quantum simulation tasks have long been considered beyond the reach of photonic hardware limited to linear operations. These capabilities, enabled by our extensible architecture, establish a viable route towards photonic quantum simulation and fault-tolerant quantum computing.
Continuous-variables (CV) quantum optics is a natural formalism for neural networks (NNs) due to its ability to reproduce the information processing of such trainable interconnected systems. In quantum optics, Gaussian operators induce affine mappings on the quadratures of optical modes while non-Gaussian resources (the challenging piece for physical implementation) originate the nonlinear effects, unlocking quantum analogs of an artificial neuron. This work presents a novel experimentally-feasible framework for continuous-variable quantum optical neural networks (QONNs) developed with available photonic components: coherent states as input encoding, a general Gaussian transformation followed by multi-mode photon subtractions as the processing layer, and homodyne detection as outputs readout. The closed-form expressions of such architecture are derived demonstrating the family of adaptive activations and the quantum-optical neurons that emerge from the amount of photon-subtracted modes, proving that the proposed design satisfies the Universal Approximation Theorem within a single layer. To classically simulate the QONN training, the high-performance QuaNNTO library has been developed based on Wick-Isserlis expansion and Bogoliubov transformations, allowing multi-layer exact expectation values of non-Gaussian states without truncating the infinite-dimensional Hilbert space. Experiments on supervised learning and state-preparation tasks show balanced-resource efficiency with strong expressivity and generalization capabilities, illustrating the potential of the architecture for scalable photonic quantum machine learning and for quantum applications such as complex non-Gaussian gate synthesis.