Qrisp is a framework for high-level quantum programming that bridges the gap between software engineering and quantum hardware. It provides a systematic approach to quantum algorithm development, enabling efficient implementation, maintenance, and improvement. Qrisp's key feature is its ability to compile programs to the circuit level, making them executable on existing quantum hardware. The framework introduces programming abstractions inspired by classical paradigms, focusing on quantum-specific needs. Unlike other high-level languages, Qrisp's compiler leverages algorithm structure for increased efficiency. It is implemented in Python, allowing access to scientific libraries and generating circuit representations like OpenQASM. The paper presents core concepts, programming abstractions, circuit manipulation features, and practical examples, including Shor's algorithm and Grover's algorithm. Qrisp's QuantumVariable class enables complex quantum algorithms through automated management of qubits and measurements. The framework also includes uncomputation, quantum types, and environments for controlled operations. Qrisp's compilation process optimizes resource usage, reducing qubit counts and circuit depth. It supports quantum arrays, dictionaries, and environments for modular development. The paper demonstrates Qrisp's capabilities through examples, showing its efficiency in quantum circuit compilation and execution.Qrisp is a framework for high-level quantum programming that bridges the gap between software engineering and quantum hardware. It provides a systematic approach to quantum algorithm development, enabling efficient implementation, maintenance, and improvement. Qrisp's key feature is its ability to compile programs to the circuit level, making them executable on existing quantum hardware. The framework introduces programming abstractions inspired by classical paradigms, focusing on quantum-specific needs. Unlike other high-level languages, Qrisp's compiler leverages algorithm structure for increased efficiency. It is implemented in Python, allowing access to scientific libraries and generating circuit representations like OpenQASM. The paper presents core concepts, programming abstractions, circuit manipulation features, and practical examples, including Shor's algorithm and Grover's algorithm. Qrisp's QuantumVariable class enables complex quantum algorithms through automated management of qubits and measurements. The framework also includes uncomputation, quantum types, and environments for controlled operations. Qrisp's compilation process optimizes resource usage, reducing qubit counts and circuit depth. It supports quantum arrays, dictionaries, and environments for modular development. The paper demonstrates Qrisp's capabilities through examples, showing its efficiency in quantum circuit compilation and execution.