Experimental pairwise entanglement estimation for an N-qubit system

Thompson, Nathan; Nguyen, Nam; Behrman, Elizabeth; Steck, James E.

doi:10.1007/s11128-020-02877-1

Cited by 2 publications

(1 citation statement)

References 27 publications

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“…What advantage does the variational quantumclassical algorithm offer over the classical machine learning methods for prediction tasks in terms of performance? Thompson (2020) was motivated by a desire to learn more about this problem by using qubits as neurons in an artificial neural network, with the goal of eventually building quantum artificial neural networks using interconnected qubits as the hardware. As a result, quantum dots were proposed as the first piece of hardware to study.…”

Section: Quantum Advantagementioning

confidence: 99%

Variational Quantum-Classical Algorithms: A Review of Theory, Applications, and Opportunities

Adebayo,

Basaky,

Osaghae

2023

USci

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Variational Quantum-Classical Algorithm (VQCA) is a potential tool for machine learning (ML) prediction tasks, but its efficacy, adaptability to big datasets, and optimization for noise reduction on quantum hardware are not clear. We aim to accomplish three study goals in this literature review. We begin by reviewing the justifications for ML practitioners' use of VQCA. Second, we compare the accuracy and effectiveness of VQCA in diverse domains to see whether it has a performance advantage over other ML methods. Finally, we evaluate VQCA's immediate and long-term effects on quantum ML and how well it performs compared to ML techniques for prediction tasks across various applications or domains. Our findings show that VQCA can be significantly more accurate and efficient than conventional algorithms. We also compare traditional ML algorithms with VQCA on various datasets and examine their theoretical guarantees. We equally look into how VQCA might be used practically to address problems in a variety of industries, including banking, healthcare, and energy. In various datasets, we assess the performance and efficacy of VQCA for unsupervised learning tasks. Finally, we go through ways to improve VQCA, particularly for big and complicated problems, to lessen the effect of noise and other sources of error in quantum hardware. Overall, we looked at VQCA’s advantages and disadvantages for ML prediction tasks, including possible directions for future study. Our findings show that VQCA has the potential to completely transform the ML industry, particularly in this emerging era of quantum computing.

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Section: Quantum Advantagementioning

confidence: 99%

Variational Quantum-Classical Algorithms: A Review of Theory, Applications, and Opportunities

Adebayo,

Basaky,

Osaghae

2023

USci

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A non-algorithmic approach to “programming” quantum computers via machine learning

Thompson

Steck

Behrman

2020

2020 IEEE International Conference on Quantum Computing and Engineering (QCE)

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Major obstacles remain to the implementation of macroscopic quantum computing: hardware problems of noise, decoherence, and scaling; software problems of error correction; and, most important, algorithm construction. Finding truly quantum algorithms is quite difficult, and many of these genuine quantum algorithms, like Shor's prime factoring or phase estimation, require extremely long circuit depth for any practical application, which necessitates error correction. In contrast, we show that machine learning can be used as a systematic method to construct algorithms, that is, to non-algorithmically "program" quantum computers. Quantum machine learning enables us to perform computations without breaking down an algorithm into its gate "building blocks", eliminating that difficult step and potentially increasing efficiency by simplifying and reducing unnecessary complexity. In addition, our non-algorithmic machine learning approach is robust to both noise and to decoherence, which is ideal for running on inherently noisy NISQ devices which are limited in the number of qubits available for error correction. We demonstrate this using a fundamentally nonclassical calculation: experimentally estimating the entanglement of an unknown quantum state. Results from this have been successfully ported to the IBM hardware and trained using a hybrid reinforcement learning method.

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Experimental pairwise entanglement estimation for an N-qubit system

Cited by 2 publications

References 27 publications

Variational Quantum-Classical Algorithms: A Review of Theory, Applications, and Opportunities

Variational Quantum-Classical Algorithms: A Review of Theory, Applications, and Opportunities

A non-algorithmic approach to “programming” quantum computers via machine learning

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