Publications

Efficient Treatment of Non-Linearity in Quantum Computational Fluid Dynamics Using Hybrid Tensor Networks

Pia Siegl, Nis-Luca van Hülst, Maximilian Mandelt Buxadé, Tomohiro Hashizume, Dieter Jaksch Nonlinear terms present a fundamental challenge for quantum computational fluid dynamics, as their implementation on inherently linear quantum hardware typically requires resource-intensive workarounds that limit scalability to large-scale simulations. We present a hybrid quantum-classical tensor network algorithm that addresses this bottleneck by combining variational time-stepping with quantum tensor…

Bounded-Error Quantum Simulation via Hamiltonian and Lindbladian Learning

Tristan Kraft, Manoj K. Joshi, William T. Lam, Tobias Olsacher, Florian Kranzl, Johannes Franke, Lata Kh Joshi, Rainer Blatt, Augusto Smerzi Analog quantum simulators offer a route to exploring strongly correlated many-body dynamics beyond classical computation, but their predictive power remains limited by the absence of quantitative error estimation. Establishing rigorous uncertainty bounds is essential for elevating such devices from…

Publications, QCFD Publications 2 days ago

Iterative tensor network transformations for element-wise evaluation ofelementary and filtering functions

Xiao Wang, Tomohiro Hashizume, Pia Siegl, Dieter Jaksch Tensor networks are powerful formats for compressing large-scale data. However, their application to general data processing has been limited by the difficulty of performing nonlinear operations. Here, we introduce iterative tensor network transformations (ITNTs), a general algorithmic framework for the element-wise evaluation of elementary and nonlinear filtering functions on data encoded as…

Publications, QCFD Publications 2 days ago

How Hard Is Quantum Advantage? A Cloud Microphysics Stress Test for Variational Quantum Models

Felix Herbort, Ellen Sarauer, Daniel Ohl de Mello, Paul Christiansen, Steffen Hien, Cedric Brügmann, Dieter Jaksch, Veronika Eyring, Martin Kiffner, Mierk Schwabe Quantum machine learning (QML) could have the potential to leverage advantages of quantum over classical computing but still lacks strong evidence of actual improvements and scalability, partly due to phenomena such as barren plateaus. In this paper, we employ a hybrid quantum neural network (QNN) on a…

Publications, QCFD Publications 2 days ago

Optimizing Symmetry Informed Probabilistic Error Cancellation

Tom O'Leary, Daniel J. Egger, Dieter Jaksch We show that combining quantum error detection (QED) with probabilistic error cancellation (PEC) gives more accurate and lower-variance estimates than PEC alone, provided that the symmetry measurements required for QED are carefully chosen. Because noisy symmetry measurements can negate the benefits of the PEC+QED approach, we cast the selection of measurement configurations as…

Publications, QCFD Publications 2 days ago

Operator Learning for efficient Quantum Computation

Paul Over, Sergio Bengoechea, Leonardo Borello Busilacchi, Martin Kiffner, Thomas Rung, Alexios A. Michailidis An efficient implementation of quantum algorithms is often hindered by the lack of efficient primitives for operators and state preparation. This limits both the ability of near-term quantum hardware to simulate complex problems and the potential of fault-tolerant algorithms to achieve practical quantum advantage. To address…

Publications, QCFD Publications June 9, 2026

A Quantum Linear Systems Pathway for Solving Differential Equations

Abhishek Setty We present a systematic pathway for solving differential equations within the quantum linear systems framework by combining block encoding with Quantum Singular Value Transformation (QSVT). The approach is demonstrated on a complex tridiagonal linear system and extended to problems in computational fluid dynamics: the heat equation with mixed boundary conditions and Carleman-linearized nonlinear Burgers’ equation. Our scaling analysis…

Publications, QCFD Publications June 9, 2026

Resource-Efficient Quantum Optimization via Higher-Order Encoding

Frederik Koch, Shahram Panahiyan, Rick Mukherjee, Joseph Doetsch and Dieter Jaksch Quantum approaches to combinatorial optimization problems (COPs) are often limited by the resource demands of Quadratic Unconstrained Binary Optimization (QUBO) encodings, which enlarge circuits through penalty terms and increase qubit and gate counts. We show that Higher-Order Unconstrained Binary Optimization (HUBO) enables a more resource-efficient formulation. Our method systematically…

Publications, QCFD Publications May 18, 2026

Minimum Toffoli depth for the multi-controlled Toffoli gate via teleportation

Spyros Tserkis, Muhammad Umer, Eleftherios Mastorakis, Dimitris G. Angelakis The decomposition of complex quantum operations into experimentally feasible gate sets has been a central challenge since the early development of quantum computing. The multi-controlled Toffoli (MCT) gate is a key example, with applications across a wide range of quantum algorithms, whose decomposition into smaller gates, however, typically leads to deep…

Publications, QCFD Publications April 21, 2026

Quantum computation at the edge of chaos

Tomohiro Hashizume, Zhengjun Wang, Frank Schlawin, Dieter Jaksch A key challenge in classical machine learning is to mitigate overparameterization by selecting sparse solutions. We translate this concept to the quantum domain, introducing quantum sparsity as a principle based on minimizing quantum information shared across multiple parties. This allows us to address fundamental issues in quantum data processing and convergence issues…