Catálogo de publicaciones - revistas

<jats:title>Abstract</jats:title> <jats:p>A state of the art method based on quantum variational algorithms can be a powerful approach for solving quantum many-body problems. However, the research scope in the field is mainly limited to organic molecules and simple lattice models. Here, we propose a workflow of a quantum variational algorithm for periodic systems on the basis of an effective model construction from first principles. The band structures of the Hubbard model of graphene with the mean-field approximation are calculated as a benchmark, and the calculated eigenvalues obtained by restricted Boltzmann machine-based variational quantum eigensolver (RBM-based VQE) show good agreement with the exact diagonalization results within a few meV. The results show that the present computational scheme has the potential to solve many-body problems quickly and correctly for periodic systems using a quantum computer.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>Noisy, intermediate-scale quantum (NISQ) computers are expected to execute quantum circuits of up to a few hundred qubits. The circuits have to conform to NISQ architectural constraints regarding qubit allocation and the execution of multi-qubit gates. Quantum circuit compilation (QCC) takes a nonconforming circuit and outputs a compatible circuit. Can classical optimisation methods be used for QCC? Compilation is a known combinatorial problem shown to be solvable by two types of operations: (1) qubit allocation, and (2) gate scheduling. We show informally that the two operations form a discrete ring. The search landscape of QCC is a two dimensional discrete torus where vertices represent configurations of how circuit qubits are allocated to NISQ registers. Torus edges are weighted by the cost of scheduling circuit gates. The novelty of our approach uses the fact that a circuit’s gate list is circular: compilation can start from any gate as long as all the gates will be processed, and the compiled circuit has the correct gate order. Our work bridges a theoretical and practical gap between classical circuit design automation and the emerging field of quantum circuit optimisation.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>Quantum key distribution (QKD) enables unconditionally secure communication guaranteed by the laws of physics. The last decades have seen tremendous efforts in making this technology feasible under real-life conditions, with implementations bridging ever longer distances and creating ever higher secure key rates. Readily deployed glass fiber connections are a natural choice for distributing the single photons necessary for QKD both in intra- and intercity links. Any fiber-based implementation however experiences chromatic dispersion which deteriorates temporal detection precision. This ultimately limits maximum distance and achievable key rate of such QKD systems. In this work, we address this limitation to both maximum distance and key rate and present an effective and easy-to-implement method to overcome chromatic dispersion effects. By exploiting entangled photons’ frequency correlations, we make use of nonlocal dispersion compensation to improve the photons’ temporal correlations. Our experiment is the first implementation utilizing the inherently quantum-mechanical effect of nonlocal dispersion compensation for QKD in this way. We experimentally show an increase in key rate from 6.1 to 228.3 bits/s over 6.46 km of telecom fiber. Our approach is extendable to arbitrary fiber lengths and dispersion values, resulting in substantially increased key rates and even enabling QKD in the first place where strong dispersion would otherwise frustrate key extraction at all.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>Bayesian analysis is a framework for parameter estimation that applies even in uncertainty regimes where the commonly used local (frequentist) analysis based on the Cramér–Rao bound (CRB) is not well defined. In particular, it applies when no initial information about the parameter value is available, e.g., when few measurements are performed. Here, we consider three paradigmatic estimation schemes in continuous-variable (CV) quantum metrology (estimation of displacements, phases, and squeezing strengths) and analyse them from the Bayesian perspective. For each of these scenarios, we investigate the precision achievable with single-mode Gaussian states under homodyne and heterodyne detection. This allows us to identify Bayesian estimation strategies that combine good performance with the potential for straightforward experimental realization in terms of Gaussian states and measurements. Our results provide practical solutions for reaching uncertainties where local estimation techniques apply, thus bridging the gap to regimes where asymptotically optimal strategies can be employed.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>Variational hybrid quantum–classical algorithms are powerful tools to maximize the use of noisy intermediate-scale quantum devices. While past studies have developed powerful and expressive ansatze, their near-term applications have been limited by the difficulty of optimizing in the vast parameter space. In this work, we propose a heuristic optimization strategy for such ansatze used in variational quantum algorithms, which we call ‘parameter-efficient circuit training (PECT)’. Instead of optimizing all of the ansatz parameters at once, PECT launches a sequence of variational algorithms, in which each iteration of the algorithm activates and optimizes a subset of the total parameter set. To update the parameter subset between iterations, we adapt the <jats:italic>Dynamic Sparse Reparameterization</jats:italic> scheme which was originally proposed for training deep convolutional neural networks. We demonstrate PECT for the Variational Quantum Eigensolver, in which we benchmark unitary coupled-cluster ansatze including UCCSD and <jats:italic>k</jats:italic>-UpCCGSD, as well as the Low-Depth Circuit Ansatz (LDCA), to estimate ground state energies of molecular systems. We additionally use a layerwise variant of PECT to optimize a hardware-efficient circuit for the Sycamore processor to estimate the ground state energy densities of the one-dimensional Fermi-Hubbard model. From our numerical data, we find that PECT can enable optimizations of certain ansatze that were previously difficult to converge and more generally can improve the performance of variational algorithms by reducing the optimization runtime and/or the depth of circuits that encode the solution candidate(s).</jats:p>

<jats:title>Abstract</jats:title> <jats:p>We improve the quality of quantum circuits on superconducting quantum computing systems, as measured by the quantum volume (QV), with a combination of dynamical decoupling, compiler optimizations, shorter two-qubit gates, and excited state promoted readout. This result shows that the path to larger QV systems requires the simultaneous increase of coherence, control gate fidelities, measurement fidelities, and smarter software which takes into account hardware details, thereby demonstrating the need to continue to co-design the software and hardware stack for the foreseeable future.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>Improving axial resolution is paramount importance for three-dimensional optical imaging systems. Here, we investigate the ultimate precision in axial localization using vortex beams. For Laguerre–Gauss (LG) beams, this limit can be achieved with just an intensity scan. The same is not true for superpositions of LG beams, in particular for those with intensity profiles that rotate on defocusing. Microscopy methods based on rotating vortex beams may thus benefit from replacing traditional intensity sensors with advanced mode-sorting techniques.</jats:p>