Hyperparameter optimization of hybrid quantum neural networks for car classification


Terra Quantum pioneers a range of quantum technologies with the mission of leading the quantum revolution from meaningful solutions today to a more prosperous future tomorrow. The development of quantum technologies will disrupt many industries, creating new opportunities and at the same time, new risks. Currently, there is considerable concern about the nefarious use of quantum technologies for illegal hacking. It is expected that in the near future, quantum computers will develop to such a point that they pose a significant threat to our current information security protocols, allowing hackers access to sensitive information globally by brute-forcing security problems previously uncrackable by classical computers. While companies in the financial and healthcare spaces are especially concerned with protecting confidential and sensitive information, all industries are aware of the financial losses and reputational damages that result from data breaches and should be proactive in addressing this threat. This business white paper discusses the nature of the coming threat and details our comprehensive offerings and solutions for secure communication in the quantum age. These include Terra Quantum’s novel Quantum Key Distribution (QKD) protocol, Quantum Random Number Generators (QRNGs) and Post-Quantum Library.


Image recognition is one of the primary applications of machine learning algorithms. Nevertheless, machine learning models used in modern image recognition systems consist of millions of parameters that usually require significant computational time to be adjusted. Moreover, adjustment of model hyperparameters leads to additional overhead. Because of this, new developments in machine learning models and hyperparameter optimization techniques are required. This paper presents a quantum-inspired hyperparameter optimization technique and a hybrid quantum-classical machine learning model for supervised learning. We benchmark our hyperparameter optimization method over standard black-box objective functions and observe performance improvements in the form of reduced expected run times and fitness in response to the growth in the size of the search space. We test our approaches in a car image classification task, and demonstrate a full-scale implementation of the hybrid quantum neural network model with the tensor train hyperparameter optimization. Our tests show a qualitative and quantitative advantage over the corresponding standard classical tabular grid search approach used with a deep neural network ResNet34. A classification accuracy of 0.97 was obtained by the hybrid model after 18 iterations, whereas the classical model achieved an accuracy of 0.92 after 75 iterations.

The field of quantum computing has seen large leaps in building usable quantum hardware during the past decade. As one of the first vendors, D-Wave provided access to a quantum device that can solve specific types of optimization problems [1]. Motivated by this, quantum computing has not only received much attention in the research community, but was also started to be perceived as a valuable technology in industry.

Keywords: quantum computing; terra quantum

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Terra Quantum is committed to building quantum technology for a better future, breaking down the barriers between science and industry, and laying the foundations of a real quantum tech ecosystem and value chain.

Terra Quantum value the intrinsic connection between our planet and quantum technologies, putting sustainability at the core of our business and culture.

Standing at the very beginning of this revolution Terra Quantum feels the excitement – and responsibility – for the applications and tools they are developing.