Quantum state preparation—the process of encoding classical descriptions into quantum circuits—faces significant challenges due to the exponentially increasing circuit depths required by conventional methods. In this work, we introduce Tucker Iterative Quantum State Preparation (Q-Tucker), a novel method that adaptively constructs shallow, deterministic quantum circuits by exploiting the global entanglement structure of target states. Building upon the Tucker decomposition, our method factors the target quantum state into a core tensor and mode-specific operators, enabling direct decompositions across multiple subsystems.

As a co-sponsor of this workshop, we also present Q-Alchemy, a software-as-a-service platform for scalable quantum state preparation in systems with >20 qubits. Our focus are commercial scales with up to 256 GB for a 34 qubit state or even and 16 TB for 40 qubits. Q-Alchemy also supports lossless encoding of sparse, high-qubit states (> 50 qubits). This SaaS enables practical, large-scale quantum simulations and near-term feasibility studies.

Carsten Blank earned his PhD from the Karlsruhe Institute of Technology in 2010 in non-linear partial differential equations. After graduation, he became an entrepreneur, working in digitalization and automation within the fields of in logistics and ecommerce, vehicle quality control and control of energy storage on energy markets.

In 2016, he started research in quantum computing, collaborating with names such as Maria Schuld, Amira Abbas, Francesco Petruccione among others and contributed to PennyLane and other open source projects. He still collaborates with Daniel Park (Yonsei University), Adenilton da Silva (Universidade de Pernambuco), Mile Gu (Nanyang Technological University) and Giuseppe Sergioli (University of Cagliari) mostly in quantum machine learning and quantum Monte Carlo methods.

Furthermore, he is the managing director and co-founder of data cybernetics ssc GmbH (founded in 2021), which is a technology consultancy with a focus on quantum computing and energy markets for renewable energy sources. In particular the free offering Q-Alchemy supports development of quantum algorithms by encoding classical data into a quantum state with a low depth. The team of data cybernetics comprises nine employees, working on government projects like the Rymax-One project of the ministry of education and science, or as sub-contractors within the Quant²AI (DLR) and the QISS (Bundesdruckerei) projects. The company has also contracted with private firms.