While the ability to build quantum computers is improving dramatically, developing quantum algorithms is limited and relies on human insight and ingenuity. Although a number of quantum programming languages have been developed, it is challenging for software developers who are not familiar with quantum computing to learn and use these languages. It is, therefore, necessary to develop tools to support developing new quantum algorithms and programs automatically. This paper proposes AutoQC, an approach to automatically synthesizing quantum circuits using the neural network from input and output pairs. We consider a quantum circuit a sequence of quantum gates and synthesize a quantum circuit probabilistically by prioritizing with a neural network at each step. The experimental results highlight the ability of AutoQC to synthesize some essential quantum circuits at a lower cost.
While the ability to build quantum computers is improving dramatically, developing quantum algorithms is limited and relies on human insight and ingenuity. Although a number of quantum programming languages have been developed, it is challenging for software developers who are not familiar with quantum computing to learn and use these languages. It is, therefore, necessary to develop tools to support developing new quantum algorithms and programs automatically. This paper proposes AutoQC, an approach to automatically synthesizing quantum circuits using the neural network from input and output pairs. We consider a quantum circuit a sequence of quantum gates and synthesize a quantum circuit probabilistically by prioritizing with a neural network at each step. The experimental results highlight the ability of AutoQC to synthesize some essential quantum circuits at a lower cost.
While the ability to build quantum computers is improving dramatically, developing quantum algorithms is very limited and relies on human insight and ingenuity. Although several quantum programming languages have been developed, it is challenging for software developers unfamiliar with quantum computing to learn and use these languages. It is, therefore, necessary to develop tools to support developing new quantum algorithms and programs automatically. This paper proposes AutoQC, an approach to automatically synthesizing quantum circuits using the neural network from input and output pairs. We consider a quantum circuit a sequence of quantum gates and synthesize a quantum circuit probabilistically by prioritizing through a neural network at each step. The experimental results highlight the ability of AutoQC to synthesize some essential quantum circuits at a lower cost.
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.
At the moment, quantum circuits are created mainly by manually placing logic elements on lines that symbolize quantum bits. The purpose of creating Quantum Circuit Synthesizer "Naginata" was due to the fact that even with a slight increase in the number of operations in a quantum algorithm, leads to the significant increase in size of the corresponding quantum circuit. This causes serious difficulties both in creating and debugging these quantum circuits. The purpose of our quantum synthesizer is enabling users an opportunity to implement quantum algorithms using higher-level commands. This is achieved by creating generic blocks for frequently used operations such as: the adder, multiplier, digital comparator (comparison operator), etc. Thus, the user could implement a quantum algorithm by using these generic blocks, and the quantum synthesizer would create a suitable circuit for this algorithm, in a format that is supported by the chosen quantum computation environment. This approach greatly simplifies the processes of development and debugging a quantum algorithm. The proposed approach for implementing quantum algorithms has a potential application in the field of machine learning, in this regard, we provided an example of creating a circuit for training a simple neural network. Neural networks have a significant impact on the technological development of the transport and road complex, and there is a potential for improving the reliability and efficiency of their learning process by utilizing quantum computation, through the introduction of quantum computing.
Since quantum computing technologies are still in their early development phase, quantum circuits are created mainly by manual placement of logic elements. This development approach has the drawback of becoming inefficient due to lack of human comprehension when analyzing large circuits that correspond to complex algorithms. Because, even a slight increase in the number of operations in a quantum algorithm, could lead to the significant increase in size of its corresponding quantum circuit. Therefore, the purpose of creating Quantum Circuit Synthesizer "Naginata" is to improve the development and debugging processes of quantum circuits by introducing dynamically scalable compositions for common operations such as: the adder, multiplier, digital comparator (comparison operator), etc., turning them into building blocks for quantum programs, as well as providing a stable platform for creating more of these compositions. This way, our quantum synthesizer is opening an opportunity to implement quantum algorithms using higher-level commands. The programmer could implement a quantum algorithm with these generic blocks, and the quantum synthesizer would create a suitable circuit for this algorithm, in a format that is supported by the chosen quantum computation environment. With the help of simple command logging and using coding for building quantum circuits, this approach has the potential to significantly simplify and enhance the development and debugging processes of quantum circuits. The proposed approach for implementing quantum algorithms could have a potential application in the field of machine learning, in this regard, we provided an example of creating a circuit for training a simple neural network. Neural networks have a significant impact on the technological development of the transport and road complex, and there is a potential for improving the reliability and efficiency of their learning process by utilizing quantum computation.
Neural Network Quantum Leap (NNQL) is a novel approach to quantum computing that leverages deep learning to predict quantum system trajectories and automatically adjust quantum circuits. Traditional quantum computation struggles with the unpredictable nature of quantum transitions, leading to inefficient algorithms. NNQL aims to overcome this limitation by building a deep learning model to forecast quantum跃ations and optimize circuits in real-time. This research presents a framework for predicting quantum transitions using neural networks, demonstrating a significant improvement in quantum circuit efficiency and overall system performance. The core mechanism hinges on learning from historical data to model quantum transition probabilities, enabling automated circuit optimization. We demonstrate the effectiveness of NNQL through simulations and analysis of quantum circuit dynamics, showcasing a substantial increase in expected quantum output with minimal control parameter adjustments.
This paper presents an adaptive quantum circuit design framework leveraging a neural network to automate the process of circuit creation. Traditional quantum circuit design is often a laborious and iterative process, relying heavily on expert knowledge and trial-and-error. We introduce a novel system that utilizes a trained neural network to automatically generate quantum circuits with optimized properties, aiming to significantly reduce manual effort and accelerate quantum algorithm development. The framework employs a multi-stage training process, beginning with a pre-trained model and iteratively refining it based on a defined set of performance metrics. The core mechanism centers on the neural network's ability to learn the optimal circuit parameters to maximize desired properties such as fidelity, coherence, and connectivity. This approach promises to provide a more efficient and scalable method for quantum circuit design.