Quantum circuit simulators play a crucial role in the design, validation, and testing of quantum algorithms on classical hardware. Although numerous simulators exist in the literature, creating complex quantum circuits, especially those with highly controlled and specialized gates, typically requires significant implementation effort and low-level programming. This study introduces KuadroSim, a developer-centric quantum circuit simulator designed to simplify and modularize the quantum circuit construction process. KuadroSim is based on statevector simulation using NumPy and employs procedural algorithms for automated multi-controlled gate synthesis, modular circuit composition, and direct custom gate integration via TFC (Toffoli–Fredkin–CNOT) modules. Rather than targeting improvements in asymptotic simulation complexity or execution speed, KuadroSim focuses on reducing circuit implementation complexity and improving developer efficiency. The effectiveness of the proposed approach is evaluated through a comparative experimental analysis, in which identical quantum circuits from the literature are implemented using KuadroSim and a conventional simulator. The results demonstrate a substantial reduction in code length and implementation effort when using KuadroSim, highlighting its advantages in terms of modularity, expressiveness, and workflow efficiency. These findings indicate that KuadroSim serves as a complementary tool to existing simulators, particularly for rapid prototyping, education, and early-stage quantum algorithm development.
Quantum circuit simulators play a crucial role in the design, validation, and testing of quantum algorithms on classical hardware. Although numerous simulators exist in the literature, creating complex quantum circuits, especially those with highly controlled and specialized gates, typically requires significant implementation effort and low-level programming. This study introduces KuadroSim, a developer-centric quantum circuit simulator designed to simplify and modularize the quantum circuit construction process. KuadroSim is based on statevector simulation using NumPy and employs procedural algorithms for automated multi-controlled gate synthesis, modular circuit composition, and direct custom gate integration via TFC (Toffoli–Fredkin–CNOT) modules. Rather than targeting improvements in asymptotic simulation complexity or execution speed, KuadroSim focuses on reducing circuit implementation complexity and improving developer efficiency. The effectiveness of the proposed approach is evaluated through a comparative experimental analysis, in which identical quantum circuits from the literature are implemented using KuadroSim and a conventional simulator. The results demonstrate a substantial reduction in code length and implementation effort when using KuadroSim, highlighting its advantages in terms of modularity, expressiveness, and workflow efficiency. These findings indicate that KuadroSim serves as a complementary tool to existing simulators, particularly for rapid prototyping, education, and early-stage quantum algorithm development.
Quantum circuit simulators play a crucial role in the design, validation, and testing of quantum algorithms on classical hardware. Although numerous simulators exist in the literature, creating complex quantum circuits, especially those with highly controlled and specialized gates, typically requires significant implementation effort and low-level programming. This study introduces KuadroSim, a developer-centric quantum circuit simulator designed to simplify and modularize the quantum circuit construction process. KuadroSim is based on statevector simulation using NumPy and employs procedural algorithms for automated multi-controlled gate synthesis, modular circuit composition, and direct custom gate integration via TFC (Toffoli–Fredkin–CNOT) modules. Rather than targeting improvements in asymptotic simulation complexity or execution speed, KuadroSim focuses on reducing circuit implementation complexity and improving developer efficiency. The effectiveness of the proposed approach is evaluated through a comparative experimental analysis, in which identical quantum circuits from the literature are implemented using KuadroSim and a conventional simulator. The results demonstrate a substantial reduction in code length and implementation effort when using KuadroSim, highlighting its advantages in terms of modularity, expressiveness, and workflow efficiency. These findings indicate that KuadroSim serves as a complementary tool to existing simulators, particularly for rapid prototyping, education, and early-stage quantum algorithm development.
This work contains the analysis of results received after running synthesized quantum circuits for training perceptron neural networks. The training is performed by creating a Grover’s algorithm with a custom oracle function. The concept of synthesizing quantum circuits was showcased in the process of generating training circuits for three perceptron topologies, which were designed to test the accuracy of the synthesis process. The test circuits serve to prove that the proposed synthesis approach could be scaled to utilize more complex quantum computing systems and to solve more practical tasks. IBM’s 100-qubit cloud quantum simulator was used as the debugging environment. Quantum circuits for described algorithms are generated by the "Naginata" quantum synthesizer, its source code is published and further documented on GitHub along with the code for the provided example algorithms. The article describes the processes behind the algorithm for synthesizing quantum circuits that perform the training process of single-layer perceptrons by finding their weights by filtering all possible input values through a predefined accuracy criterion. Since quantum computing is still in its early development phase, quantum circuits are created mainly by manual placement of logic elements. Implementing quantum algorithms, especially more use-case specific ones, directly on the quantum circuit level could lead to the circuit easily becoming too complex for human comprehension. Quantum Circuit Synthesizer "Naginata" was created to simplify the development and debugging process of quantum algorithms, by adding better clarity to their development process. In our case, better clarity for the development process is achieved by composing functions for commonly used operations performed in the implemented quantum algorithm. The programmer could now implement the quantum algorithm as a set of functions, instead of manually creating a circuit from single logic elements. After this, the synthesizer would handle the task of creating the data for placing logic elements on the circuit. This enables an opportunity of implementing quantum algorithms with higher-level commands. In the scope of this work, parametrically generated generic blocks for frequently used operations such as: the adder, multiplier and digital comparator were created and utilized to form the training circuits. The test results, proved that with the help of the proposed quantum synthesizer, these compositions could be used efficiently as building blocks for implementing quantum algorithms. And by visually comparing sizes of both code and circuit representations of the synthesized circuits, to the code examples used to synthesize these circuits, it is determined that the proposed approach for implementing quantum circuits greatly simplifies the processes of development and debugging a quantum algorithm.
The rapid advancement of Noisy Intermediate-Scale Quantum (NISQ) devices has driven the need for efficient design automation tools that can translate complex engineering models into executable quantum circuits. This study explores the development of automated quantum circuit design frameworks focused on gate optimization and noise-aware compilation, enabling practical quantum computation for engineering applications such as optimization, simulation, and control systems. Traditional circuit design methods often fail to account for hardware constraints, decoherence, and gate infidelity, which significantly affect computational accuracy on NISQ devices. To address these challenges, this research introduces a hybrid automation approach combining quantum transpilation, variational circuit pruning, and error-aware synthesis algorithms to enhance circuit reliability and scalability. The framework integrates quantum-classical co-design principles to optimize both logical and physical circuit layers, leveraging hardware-specific calibration data to adapt compilation strategies dynamically. Through simulations and benchmark analysis, the proposed system demonstrates significant improvements in quantum resource utilization, error mitigation, and gate depth reduction compared to conventional compilation methods. This work underscores the growing importance of design automation in quantum engineering, where algorithmic efficiency must be harmonized with physical constraints to realize functional and scalable quantum workflows. By establishing a methodology for gate-level optimization and noise-adaptive design, the study contributes to bridging the gap between theoretical quantum algorithms and real-world engineering implementations within the NISQ computing era.
We present a new software package for efficient quantum circuit generation, designed to achieve optimal runtime performance. Despite being in an early stage of development, our implementation demonstrates significant advantages over existing tools. Using the quantum Fourier transform (QFT) as a benchmark, we show that our backend can generate circuits for systems with up to 2000 qubits faster than widely used frameworks such as Qiskit and Q#. This improvement is particularly relevant for applications where classical preprocessing time, including circuit generation, must be minimized to not diminish any potential quantum advantage - for example, in combinatorial optimization tasks. Additionally, our software provides high-level primitives for bit- and integer-level manipulations, offering a simplified interface for integration with high-level quantum programming languages.
A circuit design interface for simulation of quantum circuits is presented. This simple but powerful tool aims to enable and improve experimentation in the field of quantum computing, either for professional or amateur usage. It provides an intuitive, automated, easy-to-use environment for building algorithms using the circuit model of quantum computation, while freeing the experimenter from technical procedures, such as coding or program installations. It provides a wide range of tools to the user, from a universal gate-set, to custom gate definitions, post-selected measurements, complex compositions of sub-circuits in a recursive way, good modularity and nice output results. Moreover, it is combined with popular backend simulators due to its circuit-parsing capabilities.
Quantum circuit simulation is essential for researchers to develop and evaluate novel quantum algorithms with classical computers. Although full-state quantum circuit simulations are widely utilized for prototyping and debugging, they are computationally intensive due to the exponential growth in simulation time as the system size grows. To address the performance bottlenecks associated with conventional gate-by-gate simulation, this work refines the block-by-block simulation paradigm through blocking optimization, taking into account the architectural characteristics of hardware. The primary contribution of this research is the gate block search algorithm (GBSA), which efficiently groups the maximum number of quantum gates into gate blocks. This grouping technique enhances data reuse and reduces redundant memory accesses and I/O overhead during gate application. The proposed method is evaluated using both gate-level and circuit-level benchmarks on an AMD 64-core processor. Compared to conventional gate-by-gate simulators, such as QuEST and AerSimulator, the proposed approach achieves up to a 4.7× speedup in CPU execution and a 2.9× speedup in SSD-based scenarios. Furthermore, it can lead to a remarkable 46× reduction in memory and I/O traffic. These results indicate that the proposed block-based simulation strategy substantially improves the efficiency of quantum circuit simulation and can be readily adapted to multi-device environments, thereby supporting the rapid development of next-generation quantum algorithms.