arXiv subjects

Dario Melegari

Publications and source records attributed to Dario Melegari.

3 recordsLinked to original sources

arXiv2026

Exploiting overcompleteness of Platonic-solid POVMs for shadow estimation

Accurately estimating expectation values of observables from a finite number of measurement shots is a central challenge in quantum information science. Informationally overcomplete measurements provide a route to reduce estimation variance through optimized classical post-processing. However, the interplay between measurement geometry and dual-frame construction remains largely unexplored. In this work, we study Platonic solid POVMs ---highly symmetric, overcomplete single-qubit measurements whose effects correspond to the vertices of the five Platonic solids on the Bloch sphere--- for the estimation of molecular Hamiltonians. Using $k$-locally optimal dual frames, we show that the geometry of the POVM can have a non-trivial and non-monotonic effect on the estimation variance. We further propose a joint optimization of the POVM orientation and effect weights using a classical proxy state, either a product state or a Matrix Product State (MPS) approximation. We demonstrate that optimized Platonic solid POVMs can outperform standard randomized Pauli measurements, provided the MPS bond dimension is sufficient to faithfully represent the target state. These results reveal a trade-off between classical preprocessing and estimation accuracy, suggesting a practical route to improved observable estimation on near-term quantum hardware.

quant-ph↗

arXiv2025

A Novel Approach to Reduce Derivative Costs in Variational Quantum Algorithms

We present a detailed numerical study of an alternative approach, named Quantum Non-Demolition Measurement (QNDM), to efficiently estimate the gradients or the Hessians of a quantum observable. This is a key step and a resource-demanding task when we want to minimize the cost function associated with a quantum observable. In our detailed analysis, we account for all the resources needed to implement the QNDM approach with a fixed accuracy and compare them to the current state-of-the-art method. We find that the QNDM approach is more efficient, i.e. it needs fewer resources, in evaluating the derivatives of a cost function. These advantages are already clear in small dimensional systems and are likely to increase for practical implementations and more realistic situations. A significant outcome of our study is the implementation of the QNDM method in Python, provided in the supplementary material. Given that most Variational Quantum Algorithms can be formulated within this framework, our results can have significant implications in quantum optimization algorithms and make the QNDM approach a valuable alternative to implement Variational Quantum Algorithms on near-term quantum computers.

quant-ph↗

arXiv2025

Resource reduction for variational quantum algorithms by non-demolition measurements

We present a comparative study of two implementations of a variational quantum algorithm aimed at minimizing the energy of a complex quantum system. In one implementation, we extract the information of the energy gradient by projective measurements. In the second implementation, called the Non-Demolition approach, the gradient information is stored in a quantum detector, which is eventually measured. As prototypical examples, we study the energy minimization of the Lithium-based molecules and then extend the analysis systems with increased complexity. We find that, while both approaches are able to identify the energy minimum, the Non-Demolition approach has a clear advantage in terms of the overall computational resources needed. This advantage increases linearly with the complexity of the quantum system, making the Non-Demolition approach the ideal candidate to implement such variational quantum algorithms.

quant-ph↗