We consider quantum-classical hybrid machine learning in which large-scale input channels remain classical and small-scale working channels process quantum operations conditioned on classical input data. This does not require the conversion of classical (big) data to a quantum superposed state, in contrast to recently developed approaches for quantum machine learning. We performed optical experiments to illustrate a single-bit universal machine, which can be extended to a large-bit circuit for a binary classification task. Our experimental machine exhibits quantum learning speedup of approximately $36%$, as compared with the fully classical machine. In addition, it features strong robustness against dephasing noise.
We consider quantum-classical hybrid machine learning in which large-scale input channels remain classical and small-scale working channels process quantum operations conditioned on classical input data. This does not require the conversion of classical (big) data to a quantum superposed state, in contrast to recently developed approaches for quantum machine learning. We performed optical experiments to illustrate a single-bit universal machine, which can be extended to a large-bit circuit for a binary classification task. Our experimental machine exhibits quantum learning speedup of approximately 36, as compared with the fully classical machine. In addition, it features strong robustness against dephasing noise.
The primary question in the emerging field of machine learning (QML) is: can we achieve speed-up of machine learning? and/or what and how the effects contribute to the speed-up? The positive answers to such questions have recently been provided, for example, by the support vector machine (QSVM) which classifies many data vectors exponentially fast. However, to realize such an exponential learning speed-up, it is demanded to develop full-scale computer with a specific device, so-called quantum random access memory (QRAM)---which stores the classical big data, extremely fast, in the novel form of being quantum-mechanically superposed. Therefore, another question now arises: is it possible to attain learning speed-up even with the classical training data, and hence, requiring no full-scale computer and QRAM? To seek a satisfactory answer to this question, we consider a binary classification learning where the classicality of the training data are preserved. We then focus on a small unit structure for whole classification task, in which the minimum achievable learning speed-up would be exhibited. We provide the analysis and proof-of-principle experimental validation of a certain degree of learning speed-up in such a small unit, advocating an affirmative answer to the aforementioned main question. Through further investigation, it is also shown that our QML scheme is quite robust against the decoherence.
Near-term quantum machine learning (QML) models operate in environments wherein noise is unavoidable, arising from both imperfect classical data acquisition and the limitations of noisy intermediate-scale quantum (NISQ) hardware. Although most existing studies have focused primarily on quantum circuit noise in isolation, the combined influence of corrupted classical inputs and quantum hardware noise has received comparatively little attention.
In this work, we present a systematic experimental study of the robustness of a variational quantum classifier under realistic multi-level noise conditions. Using the Titanic dataset as a benchmark, a range of dataset-level noise models-including speckle noise, impulse noise, quantization noise, and feature dropout are applied to classical features prior to quantum encoding using a ZZ feature map. In parallel, hardware-inspired quantum noise channels such as depolarizing noise, amplitude damping, phase damping, Pauli errors, and readout errors are incorporated at the circuit level using the Qiskit Aer simulator.
The experimental results indicate that noise in classical input data can significantly intensify the effects of quantum decoherence, resulting in less stable training and noticeably lower classification accuracy. Together, these observations emphasize the importance of designing and evaluating quantum machine learning pipelines with noise in mind, and highlight the need to consider classical and quantum noise simultaneously when assessing QML performance in the NISQ era
Abstract The classification of big data usually requires a mapping onto new data clusters which can then be processed by machine learning algorithms by means of more efficient and feasible linear separators. Recently, Lloyd et al. have advanced the proposal to embed classical data into quantum ones: these live in the more complex Hilbert space where they can get split into linearly separable clusters. Here, these ideas are implemented by engineering two different experimental platforms, based on quantum optics and ultra‐cold atoms, respectively, where we adapt and numerically optimize the quantum embedding protocol by deep learning methods, and test it for some trial classical data. A similar analysis is also performed on the Rigetti superconducting quantum computer. Therefore, it is found that the quantum embedding approach successfully works also at the experimental level and, in particular, we show how different platforms could work in a complementary fashion to achieve this task. These studies might pave the way for future investigations on quantum machine learning techniques especially based on hybrid quantum technologies.
Abstract Quantum machine learning is emerging as a promising application of quantum computing due to its distinct way of encoding and processing data. It is believed that large-scale quantum machine learning demonstrates substantial advantages over classical counterparts, but a reliable scale-up is hindered by the fragile nature of quantum systems. Here we present an experimentally accessible distributed quantum machine learning scheme that integrates quantum processor units via classical communication. As a demonstration, we perform data classification tasks on eight-dimensional synthetic datasets by emulating two four-qubit processors and employing quantum convolutional neural networks. Our results indicate that incorporating classical communication notably improves classification accuracy compared to schemes without communication. Furthermore, at the tested circuit depths, we observe that the accuracy with classical communication is no less than that achieved with quantum communication. Our work provides a practical path to demonstrating large-scale quantum machine learning on intermediate-scale quantum processors by leveraging classical communication that can be implemented through currently available mid-circuit measurements.
The increasing need for machine learning to run with less computer resources is causing quantum computing to be considered as a viable substitute for conventional computing. This paper presents a hybrid quantum-classical system that improves learning by means of a classical optimisation loop combined with parameterised quantum circuits. While conventional procedures handle parameter updates and convergence, the proposed approach stores and processes high-dimensional data using quantum variational circuits. Qiskit is used by us to construct this architecture and run it on conventional datasets like Iris, MNIST (binary), CIFAR-100 (subset), HIGGS, and GTEx (gene expression). Particularly for jobs with complex, nonlinear patterns, the findings indicate that the mixed approach is as accurate as or more accurate than classical baselines. Real quantum hardware also works well with the system; noise has no impact on its precision. This work demonstrates how combined quantum-classical learning models might circumvent hardware constraints and provide genuine quantum benefits for applications requiring large data processing.
Machine learning, a branch of artificial intelligence, learns from previous experience to optimize performance, which is ubiquitous in various fields such as computer sciences, financial analysis, robotics, and bioinformatics. A challenge is that machine learning with the rapidly growing "big data" could become intractable for classical computers. Recently, quantum machine learning algorithms [Lloyd, Mohseni, and Rebentrost, arXiv.1307.0411] were proposed which could offer an exponential speedup over classical algorithms. Here, we report the first experimental entanglement-based classification of two-, four-, and eight-dimensional vectors to different clusters using a small-scale photonic quantum computer, which are then used to implement supervised and unsupervised machine learning. The results demonstrate the working principle of using quantum computers to manipulate and classify high-dimensional vectors, the core mathematical routine in machine learning. The method can, in principle, be scaled to larger numbers of qubits, and may provide a new route to accelerate machine learning.