The Power of One Qubit in Machine Learning

Ghobadi, Roohollah; Oberoi, Jaspreet S.; Zahedinejhad, Ehsan

doi:10.48550/arxiv.1905.01390

Cited by 8 publications

(15 citation statements)

References 30 publications

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“…The problem we address here is not related to a large amount of data. It is thus possible to consider a quantum circuit where all data are loaded in the coefficients of the initial wave function [6,8,9,12,13]. In the simplest of cases, data are uploaded as rotations of qubits in the computational basis.…”

Section: A Re-uploading Classical Informationmentioning

confidence: 99%

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Data re-uploading for a universal quantum classifier

Pérez-Salinas

Cervera-Lierta

Gil-Fuster

et al. 2020

Quantum

360

338

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A single qubit provides sufficient computational capabilities to construct a universal quantum classifier. This fact may be surprising since a single qubit only offers a simple superposition of two states and single qubit gates only make a rotation in the Bloch sphere. The key ingredient to circumvent these limitations is to allow for multiple data re-uploading. A quantum circuit can then be organized as a series of data re-uploading and single-qubit processing units. Furthermore, both data re-uploading and measurements can accommodate multiple dimensions in the input and multiple categories in the output, so as to conform a universal quantum classifier. The extension of this idea to several qubits enhances the efficiency of the strategy as entanglement expands the superpositions carried along the classification. Extensive benchmarking on different examples of the single-and multi-qubit quantum classifier validates its ability to describe and classify complex data.

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Section: A Re-uploading Classical Informationmentioning

confidence: 99%

“…The single-qubit classifier illustrates the computational power that a single qubit can handle. This proposal is to be added to other few-qubit benchmarks in machine learning [12]. The input redundancy has also been proposed to construct complex encoding in parametrized quantum circuits [13].…”

Section: Introductionmentioning

confidence: 99%

Data re-uploading for a universal quantum classifier

Pérez-Salinas

Cervera-Lierta

Gil-Fuster

et al. 2020

Quantum

360

338

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“…A hybrid classifier that makes use of kernels is given in (Schuld et al, 2019). (Ghobadi et al, 2019) describe classically intractable kernels for use even on NISQ machines.…”

Section: Classificationmentioning

confidence: 99%

Quantum in the Cloud: Application Potentials and Research Opportunities

Leymann

Barzen

Falkenthal

et al. 2020

Proceedings of the 10th International Conference on Cloud Computing and Services Science

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Quantum computers are becoming real, and they have the inherent potential to significantly impact many application domains. We sketch the basics about programming quantum computers, showing that quantum programs are typically hybrid consisting of a mixture of classical parts and quantum parts. With the advent of quantum computers in the cloud, the cloud is a fine environment for performing quantum programs. The tool chain available for creating and running such programs is sketched. As an exemplary problem we discuss efforts to implement quantum programs that are hardware independent. A use case from machine learning is outlined. Finally, a collaborative platform for solving problems with quantum computers that is currently under construction is presented.

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“…Therefore, discovering the representative power of qubits in quantum based learning system is extremely important, as not only does it allow near-term devices to tackle more complex learning problems, but also it eases the complexity of the quantum state exponentially. However, to tackle the topic of low-qubit counts of current quantum machines is rather sparse: to the best of our knowledge, there is only one paper for the problem of the power of one qubit (Ghobadi et al, 2019). Within this domain, the learning potential of qubits are under-investigated.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid System for Learning Classical Data in Quantum States

Stein¹,

L'Abbate²,

Mu³

et al. 2020

Preprint

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Deep neural network-powered artificial intelligence has rapidly changed our daily life with various applications. However, as one of the essential steps of deep neural networks, training a heavily-weighted network requires a tremendous amount of computing resources. Especially in the post Moore's Law era, the limit of semiconductor fabrication technology has restricted the development of learning algorithms to cope with the increasing high intensity training data. Meanwhile, quantum computing has exhibited its significant potential in terms of speeding up the traditionally compute-intensive workloads. For example, Google illustrates quantum supremacy by completing a sampling calculation task in 200 seconds, which is otherwise impracticable on the world's largest supercomputers. To this end, quantum-based learning becomes an area of interest, with the promising of a quantum speedup. In this paper, we propose GenQu, a hybrid and general-purpose quantum framework for learning classical data through quantum states. We evaluate GenQu with real datasets and conduct experiments on both simulations and real quantum computer IBM-Q. Our evaluation demonstrates that, comparing with classical solutions, the proposed models running on GenQu framework achieve similar accuracy with a much smaller number of qubits, while significantly reducing the parameter size by up to 95.86% and converging speedup by 66.67% faster.

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The Power of One Qubit in Machine Learning

Cited by 8 publications

References 30 publications

Data re-uploading for a universal quantum classifier

Data re-uploading for a universal quantum classifier

Quantum in the Cloud: Application Potentials and Research Opportunities

A Hybrid System for Learning Classical Data in Quantum States

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