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Daniel Jaroszewski

Publications and source records attributed to Daniel Jaroszewski.

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A Quantum Collocation Approach to One-Dimensional Boundary Value Problems with Coherent Amplitude Amplification

We propose a quantum collocation framework for approximating solutions of one-dimensional linear and nonlinear boundary value problems. The method formulates the search for admissible solutions as a residual-based quantum search over a discretized ansatz space, where candidate solutions are evaluated through residual conditions imposed at collocation points. A residual-threshold oracle is constructed that acts jointly on spatial and parameter registers. This joint oracle structure leads to amplification dynamics that decompose into a coherent superposition of spatially conditioned amplitude-amplification processes rather than a single global amplification mechanism. We derive the corresponding amplification geometry and show that the success probability is governed by a weighted combination of spatially dependent amplification angles. Furthermore, we prove that the reversible residual oracle can be implemented with gate complexity polynomial in the logarithm of the number of collocation points, while retaining the quadratic search acceleration associated with amplitude amplification in the parameter space. We analyze how the spatially dependent oracle structure influences the amplification dynamics and corresponding success probabilities. Furthermore, we investigate how discretization, ansatz expressivity, oracle tolerance, and finite-precision effects influence both approximation quality and amplification behavior. Numerical experiments validate the theoretical predictions and illustrate the resulting search dynamics across different discretization and precision regimes.

quant-ph

Curve fitting on a quantum annealer for an advanced navigation method

We explore the applicability of quantum annealing to the approximation task of curve fitting. To this end, we consider a function that shall approximate a given set of data points and is written as a finite linear combination of standardized functions, e.g., orthogonal polynomials. Consequently, the decision variables subject to optimization are the coefficients of that expansion. Although this task can be accomplished classically, it can also be formulated as a quadratic unconstrained binary optimization problem, which is suited to be solved with quantum annealing. Given the size of the problem stays below a certain threshold, we find that quantum annealing yields comparable results to the classical solution. Regarding a real-world use case, we discuss the problem to find an optimized speed profile for a vessel using the framework of dynamic programming and outline how the aforementioned approximation task can be put into play. Similar to the curve fitting task, our findings indicate that quantum annealing is currently only feasible if the routing problem is modeled sufficiently small and sparse.

math.OC

Ising formulations of routing optimization problems

We formulate binary optimization functions for single-vehicle routing, travelling salesperson and collision-free multi-vehicle routing with significant improvements in the number of variables over existing formulations. The provided functions are readily implemented on gate-based quantum computers using variational algorithms and on adiabatic quantum hardware.

quant-ph