arXiv subjects

Antonio Márquez Romero

Publications and source records attributed to Antonio Márquez Romero.

6 recordsLinked to original sources

arXiv2026

OpenQARP: a modular framework for quantum application research

We introduce OpenQARP, the Open Quantum Application Research Package: an open-source Python framework for quantum application research, built on a compiled C++ core. In OpenQARP, an application is assembled from an algorithm library that orchestrates three interchangeable layers: blocks describe circuits, primitives describe what to extract from them, and engines describe how they run. The release spans near-term through fault-tolerant methods, targeting problems from electronic structure to combinatorial optimization, with device-aware compilation, circuit cutting, and resource estimation over a single set of numerical conventions that the code must follow. Every numerical feature is checked against an independent oracle, and every published timing carries a correctness check. Against OpenFermion, Qiskit/Aer, PennyLane/Lightning, Qulacs, qsim, and pytket, OpenQARP is one to two orders of magnitude faster on operator algebra and among the fastest on simulation. It matches mature compilers on two-qubit gate count, and expresses a complete algorithm in the lines a framework needs rather than the plumbing a primitive stack demands.

quant-ph↗

arXiv2025

Purified phase estimation samples spectra efficiently

Quantum phase estimation (QPE) is a cornerstone algorithm for extracting Hamiltonian eigenvalues, but its standard, eigenstate-centric form relies on carefully prepared coherent inputs that are costly or impractical for many strongly correlated systems. We overcome this bottleneck via DOS-QPE, an incoherent, purification-based variant of QPE that works directly with mixed-state probes and estimates the density of states (DOS) of the Hamiltonian. By adding a purification register and simple entangling layers, we turn standard QPE into an ensemble-based DOS sampler without modifying the core phase-estimation block. Conceptually, this purification closely aligns with the recent random purification channel framework from quantum learning theory, but instantiated here as a concrete phase-estimation circuit. We further equip DOS-QPE with symmetry-adapted input ensembles and a compressed-sensing reconstruction pipeline, and demonstrate on fermionic and nuclear Hamiltonians that a single experimental setup can recover rich spectral information relevant to thermodynamics, spectroscopy, and many-body structure.

quant-ph↗

arXiv2025

A simple method for seniority-zero quantum state preparation

Quantum Phase Estimation (QPE), the quantum algorithm for estimating eigenvalues of a given Hermitian matrix and preparing its eigenvectors, is considered the most promising approach to finding the ground states and their energies of electronic systems using a quantum computer. It requires, however, to be warm--started from an initial state with sufficiently high overlap with the ground state. For strongly-correlated states, where QPE is expected to have advantage over classical methods, preparation of such initial states requires deep quantum circuits and/or expensive hybrid quantum-classical optimization. It is well-known that orbital-optimized paired Coupled Cluster Doubles (oo-pCCD) method can describe the static correlation features of many strongly correlated singlet states. We show that pCCD and its unitary counterpart, UpCCD, become equivalent in the limit of small amplitudes or if the number of large amplitudes is below 5. We demonstrate that substituting leading oo-pCCD amplitudes into the UpCCD Ansatz allows to prepare high-fidelity singlet states for models of multiple-bond dissociation in ethene, ethyne and dinitrogen, as well as for 1D Hubbard models at half-filling, with very shallow circuits. We envisage our method to be of general use for approximate preparation of singlet states for Quantum Phase Estimation and related algorithms.

quant-ph↗

arXiv2025

Comparison of variational quantum eigensolvers in light nuclei

Quantum computing is one of the most promising technologies of the near future, and the simulation of quantum many-body systems is a natural application. In this work, we present classical simulations of the ground states of light atomic nuclei within the $p$ shell, from $^{6}$He to $^{10}$B, calculated within the nuclear shell model. We compare the performance of two leading variational quantum eigensolver algorithms: the Unitary Coupled Cluster (UCC) and the Adaptive Derivative-Assembled Pseudo-Trotter (ADAPT) methods, introducing a new metric to quantify the use of quantum resources in each simulation. We find that Slater determinants are the most useful reference states for both approaches. Our analysis suggests that ADAPT is more efficient for nuclei close to magic numbers, while UCC tends to require fewer resources toward the mid shell. This work lays the groundwork for robust benchmarking of quantum algorithms in nuclear structure studies.

nucl-th↗

arXiv2025

Scaling Portfolio Diversification with Quantum Circuit Cutting Techniques

Quantum Approximate Optimization Algorithms (QAOA) have demonstrated a strong potential in addressing graph-based optimization problems. However, the execution of large-scale quantum circuits remains constrained by the limitations of current quantum hardware. In this work, we introduce QuantCut, an automatic framework for circuit cutting that enables efficient execution of large quantum circuits by decomposing entangling two-qubit gates into manageable sub-circuits. Specifically, we focus on gate-cutting techniques. We apply QuantCut to a 71-qubit QAOA circuit ansatz for portfolio diversification in the S&P 500 stock market, aiming to maximize asset diversification. Our approach iteratively optimizes the expectation value while leveraging circuit-cutting strategies to reduce the qubit register size. To validate our framework, we first conduct experiments on a toy model using quantum noise simulations for the Max-Cut problem, analyzing performance improvements with an increasing number of layers. Subsequently, we extend our methodology to a real-world financial optimization scenario, showing competitive results. The results suggest that QuantCut effectively facilitates large-scale quantum computations with circuit-cutting technologies.

quant-ph↗

arXiv2024

Survey on Computational Applications of Tensor Network Simulations

Tensor networks are a popular and computationally efficient approach to simulate general quantum systems on classical computers and, in a broader sense, a framework for dealing with high-dimensional numerical problems. This paper presents a broad literature review of state-of-the-art applications of tensor networks and related topics across many research domains including: machine learning, mathematical optimisation, materials science, quantum chemistry and quantum circuit simulation. This review aims to clarify which classes of relevant applications have been proposed for which class of tensor networks, and how these perform compared with other classical or quantum simulation methods. We intend this review to be a high-level tour on tensor network applications which is easy to read by non-experts, focusing on key results and limitations rather than low-level technical details of tensor networks.

quant-ph↗