Today, although there are studies for the active use of quantum computers, deep learning-based studies on quantum computing are few. Since the large amount of data needed by deep learning cannot be met from the literature, using deep learning in subjects such as quantum circuit optimization, and quantum circuit/algorithm generation challenges researchers. In this study, an approach is proposed to overcome the data and dataset deficiencies related to quantum circuits in the literature. The proposed approach generates datasets consisting of quantum circuits, truth tables of quantum circuits, unitary matrices, visuals, and pairs of these data. The circuits, data, and datasets produced with this approach have already been studied and are still being actively done. The performance of the proposed approach was evaluated as dependent on different variables and as elapsed time. With the proposed approach, circuits, data, and datasets with desired properties can be generated successfully. As a result, the contribution and performance of the proposed approach have been verified.
Today, although there are studies for the active use of quantum computers, deep learning-based studies on quantum computing are few. Since the large amount of data needed by deep learning cannot be met from the literature, using deep learning in subjects such as quantum circuit optimization, and quantum circuit/algorithm generation challenges researchers. In this study, an approach is proposed to overcome the data and dataset deficiencies related to quantum circuits in the literature. The proposed approach generates datasets consisting of quantum circuits, truth tables of quantum circuits, unitary matrices, visuals, and pairs of these data. The circuits, data, and datasets produced with this approach have already been studied and are still being actively done. The performance of the proposed approach was evaluated as dependent on different variables and as elapsed time. With the proposed approach, circuits, data, and datasets with desired properties can be generated successfully. As a result, the contribution and performance of the proposed approach have been verified.
Quantum computing promises remarkable computational power with minimal energy consumption. However, the complexity of developing quantum circuits and codes hinders fully exploiting this potential. The study proposes an approach based on the automatic quantum circuit and code generation based on deep learning. It enables the resynthesis of existing circuits and the creation of new ones from undefined inputs. The system transforms inputs into reversible truth tables, generates a quantum unitary matrix, corrects errors, optimizes it, and converts it into a quantum code or circuit. This approach has been implemented on circuits and codes that involve up to five variables. Rigorous evaluations include both the Deep Neural Network and the overall approach. Although the DNN output does not guarantee absolute correctness, our approach compensates with supplementary processes, ensuring the precise generation of quantum codes and circuits. Comprehensive testing confirmed the approach's effectiveness in overcoming challenges in quantum circuit and code development. • Benefiting from the low energy and high computing power potential offered by quantum computing. • Developing quantum circuits and algorithms is a challenging task. • The number of competent people in the field who can develop quantum circuits and algorithms is low. • Classical algorithms do not work on quantum computers. • Insufficient number of quantum circuits and algorithms running on quantum computers.
Deep learning is used in different research topics and draws attention with its successful results. Today, the resource need of deep learning, which requires much data and resources, is tried to be met by technologies such as parallel processors and graphic processors. In this study, the convolution process, which is one of the main causes of resource need in deep learning, is performed with quantum circuits. Thanks to the convolution process run on quantum computers, whose computation speeds are many times higher than supercomputers, the training step of deep learning can be performed much faster. In this way, the need for performance, which is one of the biggest obstacles in front of deep learning and forces researchers to work on pre-trained networks, will be met. With running deep learning networks on quantum computers, it is predicted by the scientific world that a new era for deep learning will start. Although we propose quantum circuits for the convolution process with 2, 3, and 4 window sizes, it can be used for only one iteration today. However, with obtaining structures such as quantum ram and developing sequential processing architecture for quantum computers, the proposed circuits can be used for a complete convolution process.
This paper explores the application of deep learning techniques to optimize quantum circuits. The core challenge in quantum computing lies in the intricate design and manipulation of quantum circuits to achieve desired computational outcomes. Traditional methods of quantum circuit optimization are often computationally expensive and require significant domain expertise. This research proposes a novel approach leveraging deep learning algorithms to automatically generate optimal quantum circuits. The system learns the underlying structure and characteristics of quantum circuits through training, effectively reducing the complexity associated with quantum computation. This work presents a framework for automated quantum circuit optimization, paving the way for more accessible and efficient quantum computing solutions. The key contribution is the integration of deep learning with quantum circuit optimization, offering an automated process for circuit design and potentially unlocking the full potential of quantum computers.
Unlike most classical algorithms that take an input and give the solution directly as an output, quantum algorithms produce a quantum circuit that works as an indirect solution to computationally hard problems. In the full quantum computing workflow, most data processing remains in the classical domain except for running the quantum circuit in the quantum processor. This leaves massive opportunities for classical automation and optimization toward future utilization of quantum computing. We kickstart the first step in this direction by introducing Q-gen, a high-level, parameterized quantum circuit generator incorporating 15 realistic quantum algorithms. Each customized generation function comes with algorithmspecific parameters beyond the number of qubits, providing a large generation volume with high circuit variability. To demonstrate the functionality of Q-gen, we organize the algorithms into 5 hierarchical systems and generate a quantum circuit dataset accompanied by their measurement histograms and state vectors. This dataset enables researchers to statistically analyze the structure, complexity, and performance of large-scale quantum circuits, or quickly train novel machine learning models without worrying about the exponentially growing simulation time. Q-gen is an open-source and multipurpose project that serves as the entrance for users with a classical computer science background to dive into the world of quantum computing.
There is increasing interest in the area of quantum computing, but using and developing new quantum algorithms, often represented as quantum circuits, is arduous and requires designers with a wide area of experience. The exploration of techniques for the automatic design of quantum algorithms, i.e., quantum architecture search, is therefore of high importance. This work explores the use of neural networks for quantum architecture search, since neural networks have proven quite successful in various complex search domains, and current approaches in quantum architecture search make very restricted use of them. There are many different domains in which quantum architecture search can be applied. They can be used to compile unitaries into quantum circuits, develop ansätze for variational quantum circuits, or develop new quantum algorithms by applying it to input output pairs of one task one wishes to solve. The focus in this thesis is on the task of quantum state preparation, a very important task in the field of quantum computing, as it is a subroutine in a large number of quantum algorithms like quantum machine learning and Hamiltonian simulation. This thesis approaches the task of quantum architecture search first by establishing the importance of the structure of quantum circuits. This is done with a large-scale study of the influence different circuit structure parameters, such as qubits and depth, have on the performance on variational quantum machine learning. During this research, four guidelines for designing circuits for variational quantum machine learning were established. DeepQPrep, a neural network-augmented deductive search for the automatic design of quantum circuits for quantum state preparation, was proposed. It is able to create non-parameterized quantum circuits to solve 76.9% of a restrictive set of quantum states with quantum circuits that were much shallower than the ones found in the ground-truth data. The model was trained on a dataset created in this thesis as none were available at the time. During the exploration of extensions of DeepQPrep to enable it to solve more arbitrary quantum state preparation problems without the need for labeled data, different challenges became apparent. A study on the difficulty of training neural networks to create parameterized quantum circuits using reinforcement learning found that training is hampered by the barren plateau problem of quantum computing. It shows that the variance of the gradient of the quantum circuit output with respect to its parameters goes exponentially to zero. A study showed that this problem makes the application of neural networks to encode quantum circuits highly challenging when no intermediate quantum state information is used. This culminated in DeepQPrepGrad, which combines parameterized circuits, the inductive search of DeepQPrep, and the reinforcement learning-based approach with more compact representations. By combining these parts with the gradients of quantum circuits, an approach that is able to create parameterized quantum circuits for arbitrary quantum states was obtained. The approach was evaluated on 20000 arbitrary 3-qubit quantum states without the need for any labeled data, resulting in a mean minimal similarity of 0.97 alleviated the found challenges to a large degree. This thesis explored key challenges faced when training neural networks to encode quantum circuits. These challenges are of paramount importance as they potentially affect a wide range of tasks in the field of quantum computing to which neural networks could be applied. Lastly, DeepQPrepGrad provides a significant step forward for the creation of quantum circuits using neural networks. DeepQPrepGrad makes great progress in solving the challenges highlighted above and has the potential to be applicable in a wide range of quantum architecture search tasks, such as variational eigensolver and variational quantum machine learning.
Large language models (LLMs), based on deep learning techniques, are trained on vast amounts of textual data and possess the ability to understand and generate natural language.They have found widespread application in tasks such as machine translation, text generation, and question answering.This work proposes a novel approach for code-level quantum circuit generation using LLM. By fine-tuning existing LLM, we enable it to generate quantum circuit code tailored to user specifications, offering an efficient and high-performance solution for quantum circuit design. Simulation results demonstrate that this method achieves high accuracy in generating quantum circuit codes with only a small set of training data. This work provides a new perspective on quantum circuit design. It highlights the potential of LLMs in quantum programming and algorithm automation, laying the foundation for future research and practical applications in quantum artificial intelligence.