arXiv subjects

Fabio Tarocco

Publications and source records attributed to Fabio Tarocco.

5 recordsLinked to original sources

arXiv2026

Scalable quantum circuit generation for iterative ground state approximation using Majorana Propagation

We introduce the Adaptive Derivative-Assembled Pseudo-Trotter ansatz Variational Majorana Propagation Eigensolver (ADAPT-VMPE), a quantum-inspired classical algorithm that exploits Majorana Propagation (MP) to produce circuits for approximating the ground state of molecular Hamiltonians. Equipped with the theoretical guarantees of MP, which provide controllable bounds on the approximation error, ADAPT-VMPE offers an efficient and scalable approach for iterative ansatz construction. A theoretical analysis of the computational complexity demonstrates that it is polynomial in both the number of qubits and the number of iterations. We present an in-depth analysis of circuit construction strategies, analyzing their impact on convergence and provide practical guidance for efficient ansatz generation. Using ADAPT-VMPE, we construct up to 100-qubit ansätze for a strongly correlated photosensitizer currently undergoing human clinical trials for cancer treatment. Our results demonstrate that constant overlap with the ground state across system sizes can be reached in polynomial time with polynomially sized circuits.

quant-ph↗

arXiv2025

Multi-QIDA method for VQE state preparation in molecular systems

The development of quantum algorithms and their application to quantum chemistry has introduced new opportunities for solving complex molecular problems that are computationally infeasible for classical methods. In quantum chemistry, the Variational Quantum Eigensolver (VQE) is a hybrid quantum-classical algorithm designed to estimate ground-state energies of molecular systems. Despite its promise, VQE faces challenges such as scalability issues, high circuit depths, and barren plateaus that make the optimization of the variational wavefunction. To mitigate these challenges, the Quantum Information Driven Ansatz (QIDA) leverages Quantum Mutual Information (QMI) to construct compact, correlation-driven circuits. In this work, we go back to the original field of application of QIDA, by applying the already defined Multi-Threshold Quantum Information Driven Ansatz (Multi-QIDA) methodology on Molecular Systems. to systematically construct shallow, layered quantum circuits starting from approximate QMI matrices obtained by Quantum Chemistry calculations. The Multi-QIDA approach combines efficient creation of the QMI map, reduction of the number of correlators required by exploiting Minimum/Maximum spanning tress, and an iterative layer-wise VQE optimization routine. These enhancements allow the method to recover missing correlations in molecular systems while maintaining computational efficiency. Additionally, the approach incorporates alternative gate constructions, such as SO(4) correlators, to enhance the circuit expressibility without significantly increasing the circuit complexity. We benchmark Multi-QIDA on systems ranging from small molecules like H2O, BeH2, and NH3 in Iterative Natural Orbitals (INOs) basis set, to active-space models such as H2O-6-31G-CAS(4,4) and N2-cc-pVTZ-CAS(6,6), comparing it to traditional hardware-efficient ansatze.

quant-ph↗

arXiv2025

AEGISS -- Atomic orbital and Entropy-based Guided Inference for Space Selection -- A novel semi-automated active space selection workflow for quantum chemistry and quantum computing applications

The selection of a balanced active space is a critical step in multi-reference quantum chemistry calculations, particularly for systems with strong electron correlation. Likewise, active space selection is a key to unlock the potential of contemporary quantum computing in quantum chemistry. Albeit recent progress, there remains a lack of a unified, robust, and fully automated framework for active space selection that performs reliably across a wide range of molecular systems. In this work, we present a novel approach inspired by both the AVAS (Atomic Valence Active Space) and AutoCAS methods. Our method unifies orbital entropy analysis with atomic orbital projections to guide the construction of chemically and physically meaningful active spaces. This integrated scheme enables a more consistent and flexible selection of active orbitals while retaining automation and scalability. We validate our approach on a set of molecular systems relevant to photodynamic therapy, in particular a set of Ru(II)-complexes, selected to span increasing levels of electron correlation and structural complexity. These molecules serve as challenging test cases due to the presence of strong static correlation and the need for highly accurate electronic structure descriptions. Our results demonstrate that the method can reliably identify compact, chemically intuitive active spaces that capture the essential physics, making it suitable for both classical and quantum computational frameworks. Furthermore, we have developed this approach in a package that is intuitive to use for users and can be interfaced with both standard quantum chemistry and quantum computing applications, making it accessible to a broad research community.

physics.chem-ph↗

arXiv2024

Compact Multi-Threshold Quantum Information Driven Ansatz For Strongly Interactive Lattice Spin Models

Quantum algorithms based on the variational principle have found applications in diverse areas with a huge flexibility. But as the circuit size increases the variational landscapes become flattened, causing the so-called Barren plateau phenomena. This will lead to an increased difficulty in the optimization phase, due to the reduction of the cost function parameters gradient. One of the possible solutions is to employ shallower circuits or adaptive ansätze. We introduce a systematic procedure for ansatz building based on approximate Quantum Mutual Information (QMI) with improvement on each layer based on the previous Quantum Information Driven Ansatz (QIDA) approach. Our approach generates a layered-structured ansatz, where each layer's qubit pairs are selected based on their QMI values, resulting in more efficient state preparation and optimization routines. We benchmarked our approach on various configurations of the Heisenberg model Hamiltonian, demonstrating significant improvements in the accuracy of the ground state energy calculations compared to traditional heuristic ansatz methods. Our results show that the Multi-QIDA method reduces the computational complexity while maintaining high precision, making it a promising tool for quantum simulations in lattice spin models.

quant-ph↗

arXiv2022

Computing Graph Edit Distance with Algorithms on Quantum Devices

Distance measures provide the foundation for many popular algorithms in Machine Learning and Pattern Recognition. Different notions of distance can be used depending on the types of the data the algorithm is working on. For graph-shaped data, an important notion is the Graph Edit Distance (GED) that measures the degree of (dis)similarity between two graphs in terms of the operations needed to make them identical. As the complexity of computing GED is the same as NP-hard problems, it is reasonable to consider approximate solutions. In this paper we present a QUBO formulation of the GED problem. This allows us to implement two different approaches, namely quantum annealing and variational quantum algorithms that run on the two types of quantum hardware currently available: quantum annealer and gate-based quantum computer, respectively. Considering the current state of noisy intermediate-scale quantum computers, we base our study on proof-of-principle tests of their performance.

quant-ph↗