arXiv subjects

Florian Krötz

Publications and source records attributed to Florian Krötz.

4 recordsLinked to original sources

arXiv2026

Evaluating System-Level Fidelity with Peaked Random Circuits

Quantum computing is progressing beyond experimental prototypes toward cloud-based services and commercially deployed turnkey systems. Evaluating and comparing platform performance requires robust, standardized measures, yet vendors frequently report heterogeneous metrics that are not directly comparable or may not capture whole-system performance. Peaked Random Circuits (PRCs) have recently been proposed as a viable path to demonstrate quantum advantage: a quantum processor can reliably identify a single known high-probability outcome while the circuits' inherent complexity may render classical simulation infeasible. To support robust, system-level evaluation and comparison, we propose a novel benchmarking protocol based on PRCs. By successively running a set of PRCs with varying qubit counts and circuit depths, we quantify a system's ability to identify the deterministic peak despite cumulative noise, gate errors, and connectivity constraints. We demonstrate the benchmark experimentally on IQM's superconducting and AQT's trapped-ion architectures. Our results show that the PRC benchmark provides a holistic yet high-precision view of hardware performance and offers researchers and industry stakeholders a practical and scalable framework for the continuous comparison and evaluation of current and future quantum systems.

quant-ph↗

arXiv2026

PauLIB: A High-Performance Library for Processing Pauli Strings

Processing large Pauli sums is a significant bottleneck in quantum chemistry, Pauli propagation, and Pauli-based compilation. Existing frameworks often suffer from Python interpreter overhead or utilize hash-map data structures that hinder SIMD vectorization and complicate multi-threaded merging. We present PauLIB, a header-only C++20 library designed to eliminate these bottlenecks through three key architectural choices. A bit-packed binary symplectic representation that encodes each qubit in two bits, reducing Pauli multiplication to a bitwise XOR and a population count; a sorted array layout that replaces hash maps to enable branch-predictable SIMD bulk operations; and a struct-of-arrays (SoA) memory layout that exposes contiguous word arrays for explicit SIMD vectorization. Benchmarks at 500 qubits show that single Pauli string multiplication runs at 25ns per operation-14 times faster than PauliEngine and 660 times faster than Qiskit-flat across all pair counts tested. Hamiltonian outer-product multiplication is approximately 10 times faster than PauliEngine and 45 times faster than Qiskit at all tested sizes. Greedy commutation grouping, the dominant preprocessing cost in variational algorithms, achieves up to 21,000 times speedup over PennyLane, driven by the compact bit-packed representation. The compact layout reduces the memory footprint of a one-million-term Hamiltonian at 500 qubits from 1,036MB (Qiskit) to 142MB, a 7.3 times reduction that directly enables larger problem sizes within a fixed memory budget. PauLIB is open source and provides C++ and Python interfaces.

quant-ph↗

arXiv2025

Teaching quantum computing to computer science students: Review of a hands-on quantum circuit simulation practical

We present a practical course targeting graduate students with prior knowledge of the basics of quantum computing. The practical aims to deepen students' understanding of fundamental concepts in quantum computing by implementing quantum circuit simulators. Through hands-on experience, students learn about different methods to simulate quantum computing, including state vectors, density matrices, the stabilizer formalism, and matrix product states. By implementing the simulation methods themselves, students develop a more in-depth understanding of fundamental concepts in quantum computing, including superposition, entanglement, and the effects of noise on quantum systems. This hands-on experience prepares students to do research in the field of quantum computing and equips them with the knowledge and skills necessary to tackle complex research projects in the field. In this work, we describe our teaching approach and the structure of our practical, and we discuss evaluations and lessons learned.

physics.ed-ph↗

arXiv2025

Q-BEAST: A Practical Course on Experimental Evaluation and Characterization of Quantum Computing Systems

Quantum computing (QC) promises to be a transformative technology with impact on various application domains, such as optimization, cryptography, and material science. However, the technology has a sharp learning curve, and practical evaluation and characterization of quantum systems remains complex and challenging, particularly for students and newcomers from computer science to the field of quantum computing. To address this educational gap, we introduce Q-BEAST, a practical course designed to provide structured training in the experimental analysis of quantum computing systems. Q-BEAST offers a curriculum that combines foundational concepts in quantum computing with practical methodologies and use cases for benchmarking and performance evaluation on actual quantum systems. Through theoretical instruction and hands-on experimentation, students gain experience in assessing the advantages and limitations of real quantum technologies. With that, Q-BEAST supports the education of a future generation of quantum computing users and developers. Furthermore, it also explicitly promotes a deeper integration of High Performance Computing (HPC) and QC in research and education.

physics.ed-ph↗