Alessandro Ferraro, Artur Garcia-Saez, Antonio Acin
Feb 28, 2011·quant-ph·PDF We consider the concept of temperature in a setting beyond the standard thermodynamics prescriptions. Namely, rather than restricting to standard coarse-grained measurements, we consider observers able to master any possible quantum measurement--a scenario that might be relevant at nanoscopic scales. In this setting, we focus on quantum systems of coupled harmonic oscillators and study the question of whether the temperature is an intensive quantity, in the sense that a block of a thermal state can be approximated by an effective thermal state at the same temperature as the whole system. Using the quantum fidelity as figure of merit, we identify instances in which this approximation is not valid, as the block state and the reference thermal state are distinguishable for refined measurements. Actually, there are situation in which this distinguishability even increases with the block size. However, we also show that the two states do become less distinguishable with the block size for coarse-grained measurements--thus recovering the standard picture. We then go further and construct an effective thermal state which provides a good approximation of the block state for any observables and sizes. Finally, we point out the role entanglement plays in this scenario by showing that, in general, the thermodynamic paradigm of local intensive temperature applies whenever entanglement is not present in the system.
Sergio Sanchez-Ramirez, Jofre Vallès-Muns, Artur Garcia-Saez
Mar 26, 2024·quant-ph·PDF Tensor Networks are graph representations of summation expressions in which vertices represent tensors and edges represent tensor indices or vector spaces. In this work, we present EinExprs.jl, a Julia package for contraction path optimization that offers state-of-art optimizers. We propose a representation of the contraction path of a Tensor Network based on symbolic expressions. Using this package the user may choose among a collection of different methods such as Greedy algorithms, or an approach based on the hypergraph partitioning problem. We benchmark this library with examples obtained from the simulation of Random Quantum Circuits (RQC), a well known example where Tensor Networks provide state-of-the-art methods.
Ema Puljak, Maurizio Pierini, Artur Garcia-Saez
The pursuit of discovering new phenomena at the Large Hadron Collider (LHC) demands constant innovation in algorithms and technologies. Tensor networks are mathematical models on the intersection of classical and quantum machine learning, which present a promising and efficient alternative for tackling these challenges. In this work, we propose a tensor network-based strategy for anomaly detection at the LHC and demonstrate its superior performance in identifying new phenomena compared to established quantum methods. Our model is a parametrized Matrix Product State with an isometric feature map, processing a latent representation of simulated LHC data generated by an autoencoder. Our results highlight the potential of tensor networks to enhance new-physics discovery.
Sergi Masot-Llima, Piotr Sierant, Paolo Stornati, Artur Garcia-Saez
Feb 17, 2026·quant-ph·PDF Tensor network methods leverage the limited entanglement of quantum states to efficiently simulate many-body systems. Alternatively, Clifford circuits provide a framework for handling highly entangled stabilizer states, which have low magic and are thus also classically tractable. Clifford tensor networks combine the benefits of both approaches, exploiting Clifford circuits to reduce the classical complexity of the tensor network description of states, with promising effects on simulation approaches. We study the disentangling power of Clifford transformations acting on tensor networks, with a particular emphasis on entanglement cooling strategies. We identify regimes where exact or heuristic Clifford disentanglers are effective, explain the link between the two approaches, and characterize their breakdown as non-Clifford resources accumulate. Additionally, we prove that, beyond stabilizer settings, no Clifford operation can universally disentangle even a single qubit from an arbitrary non-Clifford rotation. Our results clarify both the capabilities and fundamental limitations of Clifford-based simulation methods.
Pau Escofet, Abhijit Das, Sahar Ben Rached, Santiago Rodrigo, Jordi Domingo, Fabio Sebastiano, Masoud Babaie, Batuhan Keskin, Edoardo Charbon, Peter Haring Bolívar, Maurizio Palesi, Elena Blokhina, Bogdan Staszewski, Avishek Nag, Artur Garcia-Sáez, Sergi Abadal, Eduard Alarcón, Carmen G. Almudéver
Jul 11, 2025·quant-ph·PDF Modular architectures are a promising approach to scaling quantum computers beyond the limits of monolithic designs. However, non-local communications between different quantum processors might significantly impact overall system performance. In this work, we investigate the role of the network infrastructure in modular quantum computing architectures, focusing on coherence loss due to communication constraints. We analyze the impact of classical network latency on quantum teleportation and identify conditions under which it becomes a bottleneck. Additionally, we study different network topologies and assess how communication resources affect the number and parallelization of inter-core communications. Finally, we conduct a full-stack evaluation of the architecture under varying communication parameters, demonstrating how these factors influence the overall system performance. The results show that classical communication does not become a bottleneck for systems exceeding one million qubits, given current technology assumptions, even with modest clock frequencies and parallel wired interconnects. Additionally, increasing quantum communication resources generally shortens execution time, although it may introduce additional communication overhead. The optimal number of quantum links between QCores depends on both the algorithm being executed and the chosen inter-core topology. Our findings offer valuable guidance for designing modular architectures, enabling scalable quantum computing.
Ema Puljak, Sergio Sanchez-Ramirez, Sergi Masot-Llima, Jofre Vallès-Muns, Artur Garcia-Saez, Maurizio Pierini
Tensor Networks have emerged as a prominent alternative to neural networks for addressing Machine Learning challenges in foundational sciences, paving the way for their applications to real-life problems. This paper introduces tn4ml, a novel library designed to seamlessly integrate Tensor Networks into optimization pipelines for Machine Learning tasks. Inspired by existing Machine Learning frameworks, the library offers a user-friendly structure with modules for data embedding, objective function definition, and model training using diverse optimization strategies. We demonstrate its versatility through two examples: supervised learning on tabular data and unsupervised learning on an image dataset. Additionally, we analyze how customizing the parts of the Machine Learning pipeline for Tensor Networks influences performance metrics.
Emanuele Costa, Axel Perez-Obiol, Javier Menendez, Arnau Rios, Artur Garcia-Saez, Bruno Julia-Diaz
Nov 11, 2024·quant-ph·PDF The nuclear shell model accurately describes the structure and dynamics of atomic nuclei. However, the exponential scaling of the basis size with the number of degrees of freedom hampers a direct numerical solution for heavy nuclei. In this work, we present a quantum annealing protocol to obtain nuclear ground states. We propose a tailored driver Hamiltonian that preserves a large gap and validate our approach in a dozen nuclei with basis sizes up to $10^5$ using classical simulations of the annealing evolution. We explore the relation between the spectral gap and the total time of the annealing protocol, assessing its accuracy by comparing the fidelity and energy relative error to classical benchmarks. While the nuclear Hamiltonian is non-local and thus challenging to implement in current setups, the estimated computational cost of our annealing protocol on quantum circuits is polynomial in the single particle basis size, paving the way to study heavier nuclei.
Adrián Pérez-Salinas, David López-Núñez, Artur García-Sáez, P. Forn-Díaz, José I. Latorre
A single-qubit circuit can approximate any bounded complex function stored in the degrees of freedom defining its quantum gates. The single-qubit approximant presented in this work is operated through a series of gates that take as their parameterization the independent variable of the target function and an additional set of adjustable parameters. The independent variable is re-uploaded in every gate while the parameters are optimized for each target function. The output state of this quantum circuit becomes more accurate as the number of re-uploadings of the independent variable increases, i. e., as more layers of gates parameterized with the independent variable are applied. In this work, we provide two different proofs of this claim related to both the Fourier series and the Universal Approximation Theorem for Neural Networks, and we benchmark both methods against their classical counterparts. We further implement a single-qubit approximant in a real superconducting qubit device, demonstrating how the ability to describe a set of functions improves with the depth of the quantum circuit. This work shows the robustness of the re-uploading technique on Quantum Machine Learning.
Miguel Carrasco-Arango, Rosa M. Badia, Artur Garcia-Saez
Apr 16, 2026·quant-ph·PDF The Metropolis-Hastings algorithm is a cornerstone of Markov Chain Monte Carlo methods, underpinning a wide range of applications in computational physics, Bayesian inference, and machine learning. Quantum variants of Metropolis-Hastings promise accelerated mixing through quantum walks, but their practical realisation remains challenging. In this work, we construct and simulate an explicit circuit level implementation of a quantum Metropolis-Hastings algorithm based on the framework introduced by Claudon \emph{et al.} (arXiv:2506.11576). We present the full quantum workflow required to prepare a stationary distribution, including a number of modifications required to make the algorithm implementable in a realistic quantum circuit model. Our results demonstrate that these modifications are essential to recover the correct stationary behaviour and highlight both the potential and current limitations of quantum Metropolis-Hastings algorithms, which are expected to become practically relevant in the fault tolerant quantum computing regime.
Sergio Sánchez-Ramírez, Javier Conejero, Francesc Lordan, Anna Queralt, Toni Cortes, Rosa M Badia, Artur Garcia-Saez
Jan 17, 2022·quant-ph·PDF With the advent of more powerful Quantum Computers, the need for larger Quantum Simulations has boosted. As the amount of resources grows exponentially with size of the target system Tensor Networks emerge as an optimal framework with which we represent Quantum States in tensor factorizations. As the extent of a tensor network increases, so does the size of intermediate tensors requiring HPC tools for their manipulation. Simulations of medium-sized circuits cannot fit on local memory, and solutions for distributed contraction of tensors are scarce. In this work we present RosneT, a library for distributed, out-of-core block tensor algebra. We use the PyCOMPSs programming model to transform tensor operations into a collection of tasks handled by the COMPSs runtime, targeting executions in existing and upcoming Exascale supercomputers. We report results validating our approach showing good scalability in simulations of Quantum circuits of up to 53 qubits.
Matthias Werner, Artur García-Sáez, Marta P. Estarellas
Jan 31, 2023·quant-ph·PDF In the context of adiabatic quantum computation (AQC), it has been argued that first-order quantum phase transitions (QPTs) due to localisation phenomena cause AQC to fail by exponentially decreasing the minimal spectral gap of the Hamiltonian along the annealing path as a function of the qubit number. The vanishing of the spectral gap is often linked to the localisation of the ground state in a local minimum, requiring the system to tunnel into the global minimum at a later stage of the annealing. Recent methods have been proposed to avoid this phenomenon by carefully designing the involved Hamiltonians. However, it remains a challenge to formulate a comprehensive theory of the effect of the various parameters and the conditions under which QPTs make the AQC algorithm fail. Equipped with concepts from graph theory, in this work we link graph quantities associated to the Hamiltonians along the annealing path with the occurrence of QPTs. These links allow us to derive bounds on the location of the minimal spectral gap along the annealing path, augmenting the toolbox for the analysis of strategies to improve the runtime of AQC algorithms.
Sergi Masot-Llima, Artur Garcia-Saez
Mar 13, 2024·quant-ph·PDF Efficient simulation of quantum computers relies on understanding and exploiting the properties of quantum states. This is the case for methods such as tensor networks, based on entanglement, and the tableau formalism, which represents stabilizer states. In this work, we integrate these two approaches to present a generalization of the tableau formalism used for Clifford circuit simulation. We explicitly prove how to update our formalism with Clifford gates, non-Clifford gates, and measurements, enabling universal circuit simulation. We also discuss how the framework allows for efficient simulation of more states, raising some interesting questions on the representation power of tensor networks and the quantum properties of resources such as entanglement and magic, and support our claims with simulations.
Colin West, Artur Garcia-Saez, Tzu-Chieh Wei
We present a numerical scheme for efficiently extracting the higher-order moments and cumulants of various operators on spin systems represented as tensor product states, for both finite and infinite systems, and present several applications for such quantities. For example, the second cumulant of the energy of a state, $\langle ΔH^2 \rangle$, gives a straightforward method to check the convergence of numerical ground-state approximation algorithms. Additionally, we discuss the use of moments and cumulants in the study of phase transitions. Of particular interest is the application of our method to calculate the so-called Binder's cumulant, which we use to detect critical points and study the critical exponent of the correlation length with only small finite numerical calculations. We apply these methods to study the behavior of a family of one-dimensional models (the transverse Ising model, the spin-1 Ising model, and the spin-1 Ising model in a crystal field), as well as the two-dimensional Ising model on a square lattice. Our results show that in one dimension, cumulant-based methods can produce precise estimates of the critical points at a low computational cost, and show promise for two-dimensional systems as well.
Joan Triadú-Galí, Artur Garcia-Saez, Bruno Juliá-Díaz, Axel Pérez-Obiol
We investigate the dielectric breakdown of mesoscopic Mott insulators, a phenomenon where a strong electric field destabilizes the insulating state, resulting in a transition to a metallic phase. Using the Landau-Zener formalism, which models the excitation of a two-level system, we derive a theoretical expression for the threshold value of the field. To validate our predictions, we present an efficient protocol for estimating the charge gap and threshold field via non-equilibrium current oscillations, overcoming the computational limitations of exact diagonalization. Our simulations demonstrate the accuracy of our theoretical formula for systems with small gaps. Moreover, our findings are directly testable in ultracold atomic experiments with ring geometries and artificial gauge fields, as our method uses measurable quantities and relies on already available technologies. This work aims to bridge the gap between theoretical models and experimentally realizable protocols, providing tools to explore non-equilibrium mesoscopic phenomena in strongly correlated quantum systems.
Pau Escofet, Santiago Rodrigo, Artur Garcia-Sáez, Eduard Alarcón, Sergi Abadal, Carmen G. Almudéver
Fidelity is one of the most valuable and commonly used metrics for assessing the performance of quantum circuits on error-prone quantum processors. Several approaches have been proposed to estimate circuit fidelity without executing it on quantum hardware, but they often face limitations in scalability or accuracy. In this work, we present a comprehensive theoretical framework to predict the fidelity of quantum circuits under depolarizing noise. Building on theoretical results, we propose an efficient fidelity estimation algorithm based on device calibration data. The method is thoroughly validated through simulation and execution on real hardware, demonstrating improved accuracy compared to state-of-the-art alternatives, with enhancements in prediction $R^2$ ranging from 4.96\% to 213.54\%.. The proposed approach provides a scalable and practical tool for benchmarking quantum hardware, comparing quantum software techniques such as compilation methods, obtaining computation bounds for quantum systems, and guiding hardware design decisions, making it a critical resource for developing and evaluating quantum computing technologies.
A. Garcia-Saez, J. I. Latorre
May 16, 2011·quant-ph·PDF We construct a tensor network that delivers an unnormalized quantum state whose coefficients are the solutions to a given instance of 3SAT, an NP-complete problem. The tensor network contraction that corresponds to the norm of the state counts the number of solutions to the instance. It follows that exact contractions of this tensor network are in the #P-complete computational complexity class, thus believed to be a hard task. Furthermore, we show that for a 3SAT instance with n bits, it is enough to perform a polynomial number of contractions of the tensor network structure associated to the computation of local observables to obtain one of the explicit solutions to the problem, if any. Physical realization of a state described by a generic tensor network is equivalent to finding the satisfying assignment of a 3SAT instance and, consequently, this experimental task is expected to be hard.
A. Garcia-Saez, A. Ferraro, A. Acin
We consider blocks of quantum spins in a chain at thermal equilibrium, focusing on their properties from a thermodynamical perspective. Whereas in classical systems the temperature behaves as an intensive magnitude, a deviation from this behavior is expected in quantum systems. In particular, we see that under some conditions the description of the blocks as thermal states with the same global temperature as the whole chain fails. We analyze this issue by employing the quantum fidelity as a figure of merit, singling out in detail the departure from the classical behavior. The influence in this sense of zero-temperature quantum phase transitions can be clearly observed within this approach. Then we show that the blocks can be considered indeed as thermal states with a high fidelity, provided an effective local temperature is properly identified. Such a result originates from typical properties of reduced sub-systems of energy-constrained Hilbert spaces. Finally, the relation between local and global temperature is analyzed as a function of the size of the blocks and the system parameters.
A. Garcia-Saez, J. I. Latorre
We present a new strategy for contracting tensor networks in arbitrary geometries. This method is designed to follow as strictly as possible the renormalization group philosophy, by first contracting tensors in an exact way and, then, performing a controlled truncation of the resulting tensor. We benchmark this approximation procedure in two dimensions against an exact contraction. We then apply the same idea to a three dimensional system. The underlying rational for emphasizing the exact coarse graining renormalization group step prior to truncation is related to monogamy of entanglement.