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IQM and Fraunhofer FOKUS Milestone: Qrisp 0.8 Compiles 2048-bit Shor’s Algorithm
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IQM and Fraunhofer FOKUS Milestone: Qrisp 0.8 Compiles 2048-bit Shor’s Algorithm

IQM and Fraunhofer FOKUS Milestone: Qrisp 0.8 Compiles 2048-bit Shor’s Algorithm IQM Quantum Computers, in collaboration with Fraunhofer FOKUS, has announced a major update to the Eclipse Qrisp framework, achieving the first full gate-level compilation of Shor’s algorithm at a cryptographically relevant scale of 2048-bit keys. While previous resource estimates for breaking RSA-2048 relied on symbolic extrapolation or theoretical models, Qrisp 0.8 produced a concrete gate-by-gate assembly and exact qubit budget. Utilizing a parallelized resource estimation loop, the compiler reached a processing rate of approximately 109 gates per second, turning long-standing theoretical benchmarks into precise engineering targets for future fault-tolerant systems. The 0.8 release marks a shift toward Quantum Linear Algebra through the introduction of the BlockEncoding class. This provides a “NumPy-like” interface for non-unitary operations, allowing developers to perform complex matrix arithmetic—including addition, multiplication, and inversion—using standard Python operators. By embedding non-unitary operators into the upper-left block of a larger unitary matrix, Qrisp automates the underlying circuit construction and ancilla management. This is supported by a Generalized Quantum Signal Processing (GQSP) module, which implements advanced techniques like the Quantum Eigenvalue Transform for Hamiltonian simulations and matrix inversion with O(poly(log(1/ϵ))) complexity. To bridge the gap between high-level research and industrial-grade software engineering, Qrisp now includes a native MLIR (Multi-Level Intermediate Representation) quantum dialect. This connects quantum compilation to the same optimization infrastructure used in high-performance classical computing. Additionally, the framework introduces Stim integration, allowing developers to extract error correction circuits directly from high-level programs. By combining these “utility-scale” tools with a suite of advanced algor

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