arXiv subjects

Juan José García-Ripoll

Publications and source records attributed to Juan José García-Ripoll.

At least 19 recordsLinked to original sources

arXiv2026

Photon-echo synchronization and quantum state transfer in short quantum links

The short quantum link regime, where the photon travel time $τ$ is comparable to the emitter lifetime $1/γ$, is experimentally relevant but theoretically underexplored: existing few-mode descriptions lose validity as retardation and multimode effects become significant. Using a Delay Differential Equation (DDE) framework that admits exact analytical solutions from the single-mode cavity limit to the multimode waveguide continuum, we show that emitters coupled to a short link spontaneously lock into self-synchronized Rabi oscillations driven by coherent photon echoes, breaking the link's discrete time-displacement symmetry. The resulting spectral structure -- persistent quasi-dark states and vacuum Rabi splitting, including in the superstrong coupling regime -- enables efficient quantum state transfer (QST): benchmarking three protocols across the full $γτ$ parameter space, we find that STIRAP exploits the quasi-dark-state structure to achieve a quadratic infidelity floor $\mathcal{O}((γτ)^2)$, outperforming both SWAP (linear error $\mathcal{O}(γτ)$) and wavepacket engineering for $γτ\lesssim 1.44$, even in regimes where retardation cannot be neglected. These results establish photon-echo synchronization as an engineering resource for quantum state transfer, with DDE modeling providing the exact analytical predictions needed to design and optimize short-link experiments on current circuit-QED hardware.

quant-ph↗

arXiv2026

Programmable Coherent Memory Kernels for Quantum Reservoir Computing in Waveguide QED

Reservoir computing requires not only long memory, but also memory distributed over the timescales relevant to a given task. In most quantum reservoir computing architectures, however, the memory profile emerges from intrinsic dynamics and cannot be directly engineered. Here we introduce a waveguide-QED quantum reservoir in which coherent memory return is controlled by the coupling geometry. The reservoir consists of a driven Kerr-nonlinear resonator coupled to a one-dimensional waveguide at spatially separated points. An unmeasured propagating field carries information about past inputs and coherently returns it to the resonator after a finite propagation time, forming a non-Markovian feedback channel without intermediate measurement or classical reinjection. Delay-resolved memory benchmarks reveal two complementary functions: waveguide propagation determines when past information returns, whereas Kerr dynamics convert the returned field into nonlinear temporal features accessible through a linear readout. NARMA-$n$ prediction shows that the optimal feedback delay shifts systematically with task order and approximately tracks the explicit input-product lag. These results establish coherent delayed feedback as a mechanism for engineering task-relevant memory in quantum reservoir computing.

quant-ph↗

arXiv2026

Approximation and composition of functions in quantized tensor trains via orthogonal polynomial expansions

This work explores the representation of univariate and multivariate functions as quantized tensor trains (QTT). It develops a constructive algorithm that employs expansions in orthogonal polynomial bases and Clenshaw evaluations to represent analytic and highly differentiable functions as QTT approximants. This approach provides a general framework for function composition in QTT form that generalizes efficiently to multidimensional settings. Without loss of generality, the presentation focuses on the Chebyshev basis, where the method demonstrates rapid convergence for highly differentiable functions, in agreement with classical theoretical predictions, and remains numerically stable at high polynomial orders. The performance of the algorithm is compared with that of tensor cross-interpolation (TCI) and multiscale interpolative constructions, demonstrating competitive performance and favorable scaling behavior in certain scenarios.

quant-ph↗

arXiv2026

SeeMPS: A Python-based Matrix Product State and Tensor Train Library

We introduce SeeMPS, a Python library dedicated to implementing tensor network algorithms based on the well-known Matrix Product States (MPS) and Quantized Tensor Train (QTT) formalisms. SeeMPS is implemented as a complete finite precision linear algebra package where exponentially large vector spaces are compressed using the MPS/TT formalism. It enables both low-level operations, such as vector addition, linear transformations, and Hadamard products, as well as high-level algorithms, including the approximation of linear equations, eigenvalue computations, and exponentially efficient Fourier transforms. This library can be used for traditional quantum many-body physics applications and also for quantum-inspired numerical analysis problems, such as solving PDEs, interpolating and integrating multidimensional functions, sampling multivariate probability distributions, etc.

quant-ph↗

arXiv2026

Emission and Absorption of Microwave Photons in Orthogonal Temporal Modes across a 30-Meter Two-Node Network

The tunable interaction between stationary quantum bits and propagating modes of light allows for the encoding of quantum information in the state of itinerant photons. This ability fulfills a central requirement for quantum networking, enabling quantum state transfer between distant quantum devices. Conventionally, a symmetric envelope of the photon wavepacket is used for such purposes. Yet, the use of alternative \textit{temporal modes} enables multiple applications in waveguide quantum electrodynamics that remain unexplored experimentally. Here, we use superconducting quantum circuits to generate individual itinerant microwave photons shaped in three mutually orthogonal temporal modes. We transfer the created photons across a 30-m cryogenic link, showing that the orthogonality allows us to decide at the receiver which mode to absorb, reflecting the other two with a selectivity ratio of 40. This experimental capability extends the microwave-frequency quantum communication toolbox, enabling a new photonic degree of freedom.

quant-ph↗

arXiv2026

Decoherence-Free Qubit and Chiral Emission from a Giant Molecule in Waveguide QED

Combining decoherence protection with directional photon emission in a single waveguide quantum electrodynamics (QED) device remains an open challenge. Here we show that an artificial giant molecule -- strongly interacting artificial atoms coupled to a photonic waveguide at multiple spatially separated points -- achieves both: a fully operational decoherence-free (DF) qubit and state-dependent chiral single-photon emission, arising from the same photon-interference mechanism. Initialization reduces to a local excitation of a single atom, universal single-qubit gates are implemented by modulating a single atomic frequency, and readout exploits state-dependent chiral emission with directionality reaching 100% and low measurement error of 1.2%. The coexistence of decoherence protection and directional emission in a single device positions giant molecules as protected chiral nodes for modular quantum networks in waveguide QED.

quant-ph↗

arXiv2026

Universal Resources for QAOA and Quantum Annealing

The Quantum Approximate Optimization Algorithm (QAOA) is a variational ansatz that resembles the Trotterized dynamics of a Quantum Annealing (QA) protocol. This work formalizes this connection formally and empirically, showing the angles of a multilayer QAOA circuit converge to universal QA trajectories. Furthermore, the errors in both QAOA circuits and QA paths act as thermal excitations in pseudo-Boltzmann probability distributions whose temperature decreases with the invested resource -- i.e. integrated angles or total time -- and which in QAOA also contain a higher temperature arising from the Trotterization. This also means QAOA and QA are cooling protocols and simulators of partition functions whose target temperature can be tuned by rescaling the universal trajectory. The average cooling power of both methods exhibits favorable algebraic scalings with respect to the target temperature and problem size, whereby in QAOA the coldest temperature is inversely proportional to the number of layers, $T\sim 1/p$, and to the integrated angles -- or integrated interactions in QA.

quant-ph↗

arXiv2026

High-Resolution Tensor-Network Fourier Methods for Exponentially Compressed Non-Gaussian Aggregate Distributions

Characteristic functions of weighted sums of independent random variables exhibit low-rank structure in the quantized tensor train (QTT) representation, also known as matrix product states (MPS), enabling up to exponential compression of their fully non-Gaussian probability distributions. Under variable independence, the global characteristic function factorizes into local terms. Its low-rank QTT structure arises from intrinsic spectral smoothness in continuous models, or from spectral energy concentration as the number of components $D$ grows in discrete models. We demonstrate this on weighted sums of Bernoulli and lognormal random variables. In the former, despite an adversarial, incompressible small-$D$ regime, the characteristic function undergoes a sharp bond-dimension collapse for $D \gtrsim 300$ components, enabling polylogarithmic time and memory scaling. In the latter, the approach reaches high-resolution discretizations of $N = 2^{30}$ frequency modes on standard hardware, far beyond the $N = 2^{24}$ ceiling of dense implementations. These compressed representations enable efficient computation of Value at Risk (VaR) and Expected Shortfall (ES), supporting applications in quantitative finance and beyond.

stat.ML↗

arXiv2026

Comparative study of matrix product state/quantized tensor-train algorithms for solving time-independent partial differential equations

This work presents a comparative study of new and existing optimization and diagonalization methods for solving time-independent partial differential equations (PDEs) using matrix product states (MPS) in the quantized tensor-train formalism (QTT). This study focuses on Hamiltonian equations, for which five algorithms are introduced: explicit imaginary-time evolution methods, steepest gradient descent in conventional and optimized forms, a power method, and an explicitly restarted Arnoldi method. The first five methods are engineered using a framework of limited-precision linear algebra, in which operators -- i.e., the equation itself -- and vectors are represented using matrix product operator (MPO) and matrix product state (MPS) formalisms, and where operator-vector multiplication and vector addition are approximated with limited resources. All methods are benchmarked using an exactly solvable PDE for a quantum harmonic oscillator in one and two dimensions over a regular grid with up to $2^{30}$ points and compared with the density matrix renormalization group (DMRG) method. Our study reveals that all MPS-based techniques exponentially outperform exact diagonalization techniques based on vectors regarding memory usage. Imaginary-time algorithms are shown to underperform any gradient descent in terms of calibration needs and costs. Finally, MPS DMRG and interpolated Arnoldi-like asymptotically outperform all other methods, including state-of-the-art vector-based exact diagonalization, with significant advantages in time and memory use.

quant-ph↗

arXiv2026

Resource-Efficient Digitized Adiabatic Quantum Factorization

Digitized adiabatic quantum factorization is a hybrid algorithm that exploits the advantage of digitized quantum computers to implement efficient adiabatic algorithms for factorization through gate decompositions of analog evolutions. In this paper, we harness the flexibility of digitized computers to derive a digitized adiabatic algorithm able to reduce the gate-demanding costs of implementing factorization. To this end, we propose a new approach for adiabatic factorization by encoding the solution of the problem in the kernel subspace of the problem Hamiltonian, instead of using ground-state encoding considered in the standard adiabatic factorization proposed by Peng $et$ $al$. [Phys. Rev. Lett. 101, 220405 (2008)]. Our encoding enables the design of adiabatic factorization algorithms belonging to the class of Quadratic Unconstrained Binary Optimization (QUBO) methods, instead the Polinomial Unconstrained Binary Optimization (PUBO) used by standard adiabatic factorization. We illustrate the performance of our QUBO algorithm by implementing the factorization of integers $N$ up to 8 bits. The results demonstrate a substantial improvement over the PUBO formulation, both in terms of reduced circuit complexity and increased fidelity in identifying the correct solution.

quant-ph↗

arXiv2026

Time-delayed collective dynamics in waveguide QED and bosonic quantum networks

This work introduces a theoretical framework to model the collective dynamics of quantum emitters in highly non-Markovian environments, interacting through the exchange of photons with significant retardations. The formalism consists on a set of coupled delay differential equations for the emitter's polarizations $σ^\pm_i$, supplemented by input-output relations that describe the field mediating the interactions. These equations capture the dynamics of both linear (bosonic) and nonlinear (two-level) emitter arrays. It is exact in some limits$-$e.g., bosonic emitters or generic systems with up to one collective excitation$-$and can be integrated to provide accurate results for larger numbers of photons. These equations support a study of collective spontaneous emission of emitter arrays in open waveguide-QED environments. This study uncovers an effect we term cascaded super- and sub-radiance, characterized by light-cone-limited propagation and increasingly correlated photon emission across distant emitters. The collective nature of this dynamics for two-level systems is evident both in the enhancement of collective emission rates, as well as in a superradiant burst with a faster than linear growth. While these effects should be observable in existing circuit QED devices or slight generalizations thereof, the formalism put forward in this work can be extended to model other systems such as network of quantum emitters or the generation of correlated photon states.

quant-ph↗

arXiv2025

Efficient quantum state preparation of multivariate functions using tensor networks

For the preparation of high-dimensional functions on quantum computers, we introduce tensor network algorithms that are efficient with regard to dimensionality, optimize circuits composed of hardware-native gates and take gate errors into account during the optimization. To avoid the notorious barren plateau problem of vanishing gradients in the circuit optimization, we smoothly transform the circuit from an easy-to-prepare initial function into the desired target function. We show that paradigmatic multivariate functions can be accurately prepared such as, by numerical simulations, a 17-dimensional Gaussian encoded in the state of 102 qubits and, through experiments, a 9-dimensional Gaussian realized using 54 qubits on Quantinuum's H2 quantum processor.

quant-ph↗

arXiv2025

Optimised Feature Subset Selection via Simulated Annealing

We introduce SA-FDR, a novel algorithm for $\ell_0$-norm feature selection that considers this task as a combinatorial optimisation problem and solves it by using simulated annealing to perform a global search over the space of feature subsets. The optimisation is guided by the Fisher discriminant ratio, which we use as a computationally efficient proxy for model quality in classification tasks. Our experiments, conducted on datasets with up to hundreds of thousands of samples and hundreds of features, demonstrate that SA-FDR consistently selects more compact feature subsets while achieving a high predictive accuracy. This ability to recover informative yet minimal sets of features stems from its capacity to capture inter-feature dependencies often missed by greedy optimisation approaches. As a result, SA-FDR provides a flexible and effective solution for designing interpretable models in high-dimensional settings, particularly when model sparsity, interpretability, and performance are crucial.

cs.LG↗

arXiv2025

Quantics Tensor Train for solving Gross-Pitaevskii equation

We present a quantum-inspired solver for the one-dimensional Gross-Pitaevskii equation in the Quantics Tensor-Train (QTT) representation. By evolving the system entirely within a low-rank tensor manifold, the method sidesteps the memory and runtime barriers that limit conventional finite-difference and spectral schemes. Two complementary algorithms are developed: an imaginary-time projector that drives the condensate toward its variational ground state and a rank-adapted fourth-order Runge-Kutta integrator for real-time dynamics. The framework captures a broad range of physical scenarios - including barrier-confined condensates, quasi-random potentials, long-range dipolar interactions, and multicomponent spinor dynamics - without leaving the compressed representation. Relative to standard discretizations, the QTT approach achieves an exponential reduction in computational resources while retaining quantitative accuracy, thereby extending the practicable regime of Gross-Pitaevskii simulations on classical hardware. These results position tensor networks as a practical bridge between high-performance classical computing and prospective quantum hardware for the numerical treatment of nonlinear Schrodinger-type partial differential equations.

cond-mat.quant-gas↗

arXiv2025

Pseudospectral method for solving PDEs using Matrix Product States

This research focuses on solving time-dependent partial differential equations (PDEs), in particular the time-dependent Schrödinger equation, using matrix product states (MPS). We propose an extension of Hermite Distributed Approximating Functionals (HDAF) to MPS, a highly accurate pseudospectral method for approximating functions of derivatives. Integrating HDAF into an MPS finite precision algebra, we test four types of quantum-inspired algorithms for time evolution: explicit Runge-Kutta methods, Crank-Nicolson method, explicitly restarted Arnoli iteration and split-step. The benchmark problem is the expansion of a particle in a quantum quench, characterized by a rapid increase in space requirements, where HDAF surpasses traditional finite difference methods in accuracy with a comparable cost. Moreover, the efficient HDAF approximation to the free propagator avoids the need for Fourier transforms in split-step methods, significantly enhancing their performance with an improved balance in cost and accuracy. Both approaches exhibit similar error scaling and run times compared to FFT vector methods; however, MPS offer an exponential advantage in memory, overcoming vector limitations to enable larger discretizations and expansions. Finally, the MPS HDAF split-step method successfully reproduces the physical behavior of a particle expansion in a double-well potential, demonstrating viability for actual research scenarios.

quant-ph↗

arXiv2024

Topological Josephson parametric amplifier array: A proposal for directional, broadband, and low-noise amplification

Low-noise microwave amplifiers are crucial for detecting weak signals in fields such as quantum technology and radio astronomy. However, designing an ideal amplifier is challenging, as it must cover a wide frequency range, add minimal noise, and operate directionally - amplifying signals only in the observer's direction while protecting the source from environmental interference. In this work, we demonstrate that an array of non-linearly coupled Josephson parametric amplifiers (JPAs) can collectively function as a directional, broadband quantum amplifier by harnessing topological effects. By applying a collective four-wave-mixing pump with inhomogeneous amplitudes and linearly increasing phase, we break time-reversal symmetry in the JPA array and stabilize a topological amplification regime where signals are exponentially amplified in one direction and exponentially suppressed in the opposite. We show that compact devices with few sites $N\sim 11-17$ can achieve exceptional performance, with gains exceeding 20 dB over a bandwidth ranging from hundreds of MHz to GHz, and reverse isolation suppressing backward noise by more than 30 dB across all frequencies. The device also operates near the quantum noise limit and provides topological protection against up to 15% fabrication disorder, effectively suppressing gain ripples. The amplifier's intrinsic directionality eliminates the need for external isolators, paving the way for fully on-chip, near-ideal superconducting pre-amplifiers.

quant-ph↗

arXiv2024

Learning Generalized Statistical Mechanics with Matrix Product States

We introduce a variational algorithm based on Matrix Product States that is trained by minimizing a generalized free energy defined using Tsallis entropy instead of the standard Gibbs entropy. As a result, our model can generate the probability distributions associated with generalized statistical mechanics. The resulting model can be efficiently trained, since the resulting free energy and its gradient can be calculated exactly through tensor network contractions, as opposed to standard methods which require estimating the Gibbs entropy by sampling. We devise a variational annealing scheme by ramping up the inverse temperature, which allows us to train the model while avoiding getting trapped in local minima. We show the validity of our approach in Ising spin-glass problems by comparing it to exact numerical results and quasi-exact analytical approximations. Our work opens up new possibilities for studying generalized statistical physics and solving combinatorial optimization problems with tensor networks.

cond-mat.stat-mech↗

arXiv2024

Dynamical photon-photon interaction mediated by a quantum emitter

Single photons constitute a main platform in quantum science and technology: they carry quantum information over extended distances in the future quantum internet and can be manipulated in advanced photonic circuits enabling scalable photonic quantum computing. The main challenge in quantum photonics is how to generate advanced entangled resource states and efficient light-matter interfaces. Here we utilize the efficient and coherent coupling of a single quantum emitter to a nanophotonic waveguide for realizing quantum nonlinear interaction between single-photon wavepackets. This inherently multimode quantum system constitutes a new research frontier in quantum optics. We demonstrate control of a photon with another photon and experimentally unravel the dynamical response of two-photon interactions mediated by a quantum emitter, and show that the induced quantum correlations are controlled by the pulse duration. The work will open new avenues for tailoring complex photonic quantum resource states.

quant-ph↗