This paper presents a novel approach to the K-Means clustering algorithm using continuous variable (CV) quantum computing, specifically leveraging Gaussian states on quantum photonic hardware. We implement the algorithm uti-lizing Strawberry Fields, a platform for simulating and executing photonic quantum computations. The theoretical framework integrates Gaussian state preparation, quantum interference, and homodyne detection to perform the clustering tasks. We evaluate the performance of the CV quantum K-Means algorithm on var-ious datasets, focusing on clustering accuracy and compactness compared to classical K-Means. Our findings provide valuable insights into the potential of using CV quantum methods for clustering and highlight the feasibility of CV quantum algorithms in machine learning. These results suggest promising directions for future research to further enhance their performance.
This paper presents a novel approach to the K-Means clustering algorithm using continuous variable (CV) quantum computing, specifically leveraging Gaussian states on quantum photonic hardware. We implement the algorithm uti-lizing Strawberry Fields, a platform for simulating and executing photonic quantum computations. The theoretical framework integrates Gaussian state preparation, quantum interference, and homodyne detection to perform the clustering tasks. We evaluate the performance of the CV quantum K-Means algorithm on var-ious datasets, focusing on clustering accuracy and compactness compared to classical K-Means. Our findings provide valuable insights into the potential of using CV quantum methods for clustering and highlight the feasibility of CV quantum algorithms in machine learning. These results suggest promising directions for future research to further enhance their performance.
In recent years quantum computing has evolved from a research concept subject to reality. Companies and scientists around the world are creating physical quantum systems using different quantum technologies to advance the field. Although most of this focus is on superconducting, qubit-based implementations, recently there has been work in the development of boson-based quantum computers. The basic processing unit for these devices are continuous variable (CV), multi-photonic modes in Gaussian states. Due to the novel nature of this technology there is a lack of work on analyzing the compilation process of current photonic devices. One of the leading companies in this field developed a cross-platform Python library for simulating and executing programs on quantum photonic hardware, Strawberry Fields (SF). This paper will focus on analyzing the SF compilers, and proposing an additional compilation step that will enable future, more flexible hardware implementations.
Quantum photonic simulators have emerged as indispensable tools for modeling and optimizing quantum photonic circuits, bridging the gap between theoretical models and experimental implementations. This review explores the landscape of photonic quantum simulation, focusing on the transition from Gaussian to non-Gaussian models and the computational challenges associated with simulating large-scale photonic systems. Gaussian states and operations, which enable efficient simulations through covariance matrices and phase-space representations, serve as the foundation for photonic quantum computing. However, non-Gaussian states crucial for universal quantum computation introduce significant computational complexity, requiring advanced numerical techniques such as tensor networks and high-performance GPU acceleration. We evaluate the leading photonic quantum simulators, including Strawberry Fields, Piquasso, QuTiP SimulaQron, Perceval, and QuantumOPtics.jl analyzing their capabilities in handling continuous-variable (CV) and discrete-variable (DV) quantum systems. Special attention is given to hardware-accelerated methods, including GPU-based tensor network approaches, machine learning integration, and hybrid quantum-classical workflows. Furthermore, we investigate noise modeling techniques, such as photon loss and dark counts, and their impact on simulation accuracy. As photonic quantum computing moves toward practical implementations, advancements in high-performance computing (HPC) architectures, such as tensor processing units (TPUs) and system-on-a-chip (SoC) solutions, are accelerating the field. This review highlights emerging trends, challenges, and future directions for developing scalable and efficient photonic quantum simulators.
Continuous-variable (CV) quantum computing offers a promising framework for scalable quantum machine learning, leveraging optical systems with infinite-dimensional Hilbert spaces. While discrete-variable (DV) quantum neural networks have shown remarkable progress in various computer vision tasks, CV quantum models remain comparatively underexplored. In this work, we present a feasibility study of continuous-variable quantum neural networks (CV-QCNNs) applied to biomedical image classification. Utilizing photonic circuit simulation frameworks, we construct CV quantum circuits composed of Gaussian gates, such as displacement, squeezing, rotation, and beamsplitters to emulate convolutional behavior. Our experiments are conducted on the MedMNIST dataset collection, a set of annotated medical image benchmarks for multiple diagnostic tasks. We evaluate CV-QCNN's performance in terms of classification accuracy, model expressiveness, and resilience to Gaussian noise, comparing against classical CNNs and equivalent DV quantum circuits. This study aims to identify trade-offs between DV and CV paradigms for quantum-enhanced medical imaging. Our results highlight the potential of continuous-variable models and their viability for future computer-aided diagnosis systems.
Quantum computing, based on quantum theory, holds great promise as an advanced computational paradigm for achieving fast computations. Quantum algorithms are expected to surpass their classical counterparts in terms of computational complexity for certain tasks, including machine learning. In this paper, we design, implement, and evaluate three hybrid quantum k-Means algorithms, exploiting different degrees of parallelism. Indeed, each algorithm incrementally leverages quantum parallelism to reduce the complexity of the cluster assignment step up to a constant cost. In particular, we exploit quantum phenomena to speed up the computation of distances. The core idea is that the computation of distances between records and centroids can be executed simultaneously, thus saving time, especially for big datasets. We show that our hybrid quantum k-Means algorithms are theoretically faster than the classical algorithm, while experiments suggest that it is possible to obtain comparable clustering results.
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.
Implementing quantum algorithms is essential for quantum computation. We study the implementation of three quantum algorithms on a two-dimensional temporal continuous-variable cluster state. We first review the generation of temporal cluster states and the implementation of gates using the measurement-based model. Alongside this we discuss methods to introduce non-Gaussianity into the cluster states. The first algorithm we consider is Gaussian boson sampling in which only Gaussian unitaries need to be implemented. Taking into account the fact that input states are also Gaussian, the errors due to the effect of finite squeezing can be partially corrected and a high fidelity can be achieved, provided a moderate amount of online squeezing is available. This helps to construct a large Gaussian boson sampling machine. The second algorithm is the continuous-variable instantaneous quantum polynomial circuit in which one needs to implement non-Gaussian gates, such as the cubic phase gate. We discuss several methods of implementing the cubic phase gate and fit them into the temporal cluster state architecture. The third algorithm is the continuous-variable version of Grover's search algorithm, the main challenge of which is the implementation of the inversion operator. We propose a method to implement the inversion operator by injecting a resource state into a teleportation circuit. The resource state is simulated using the Strawberry Fields quantum software package.