Symplectic Optimization on Gaussian States

Christopher Willby, Tomohiro Hashizume, Jason Crain, Dieter Jaksch Computing Gaussian ground states via variational optimization is challenging because the covariance matrices must satisfy the uncertainty principle, rendering constrained or Riemannian optimization costly, delicate, and thus difficult to scale, particularly in large and inhomogeneous systems. We introduce a symplectic optimization framework that addresses this challenge by parameterizing covariance matrices directly as…

Matrix Product State Simulation of Reacting Shear Flows

Robert Pinkston, Nikita Gourianov, Hirad Alipanah, Peyman Givi, Dieter Jaksch, Juan Jose Mendoza-Arenas Direct numerical simulation (DNS) of turbulent reactive flows has been the subject of significant research interest for several decades. Accurate prediction of the effects of turbulence on the rate of reactant conversion, and the subsequent influence of chemistry on hydrodynamics remain a challenge in combustion modeling. The…

An Information-Minimal Geometry for Qubit-Efficient Optimization

Gordon Ma, Dimitris G. Angelakis Qubit-efficient optimization seeks to represent an -variable combinatorial problem within a Hilbert space smaller than , using only as much quantum structure as the objective itself requires. Quadratic unconstrained binary optimization (QUBO) problems, for example, depend only on pairwise information -- expectations and correlations between binary variables -- yet standard quantum circuits explore exponentially large state spaces.…

Depth Optimization of Ansatz Circuits for Variational Quantum Algorithms

Spyros Tserkis, Muhammad Umer, Dimitris G. Angelakis The increasing depth of quantum circuits presents a major limitation for the execution of quantum algorithms, as the limited coherence time of physical qubits leads to noise that manifests as errors during computation. In this work, we focus on circuits relevant to variational quantum algorithms and demonstrate that their depth can be reduced…

Quantum time-marching algorithms for solving linear transport problems including boundary conditions

Sergio Bengoechea, Paul Over, Thomas Rung This article presents the first complete application of a quantum time-marching algorithm for simulating multidimensional linear transport phenomena with arbitrary boundaries, whereby the success probabilities are problem intrinsic. The method adapts the linear combination of unitaries algorithm to block encode the diffusive dynamics, while arbitrary boundary conditions are enforced by the method of images…

Quantum algorithms for cooling: A simple case study

Daniel Molpeceres, Sirui Lu, J. Ignacio Cirac, Barbara Kraus Preparation of low-energy quantum many-body states has a wide range of applications in quantum information processing and condensed-matter physics. Quantum cooling algorithms offer a promising alternative to other methods based, for instance, on variational and adiabatic principles, or on dissipative state preparation. In this work, we investigate a set of cooling…

Classical feature map surrogates and metrics for quantum control landscapes

Martino Calzavara, Tommaso Calarco, Felix Motzoi We derive and analyze three feature map representations of parametrized quantum dynamics, which generalize variational quantum circuits. These are (i) a Lie-Fourier partial sum, (ii) a Taylor expansion, and (iii) a finite-dimensional sinc kernel regression representation. The Lie-Fourier representation is shown to have a dense spectrum with discrete peaks, that reflects control Hamiltonian properties,…

Gradients, parallelism, and variance of quantum estimates

Francesco Preti, Michael Schilling, József Zsolt Bernád, Tommaso Calarco, Francisco Cárdenas-López, Felix Motzoi Computation of observables and their gradients on near-term quantum hardware is a central aspect of any quantum algorithm. In this work, we first review standard approaches to the estimation of observables with and without quantum amplitude estimation for both cost functions and gradients, discuss sampling problems, and…

Real-time adaptive quantum error correction by model-free multi-agent learning

Manuel Guatto, Francesco Preti, Michael Schilling, Tommaso Calarco, Francisco Andrés Cárdenas-López, Felix Motzoi Can we build efficient Quantum Error Correction (QEC) that adapts on the fly to time-varying noise? In this work we say yes, and show how. We present a two level framework based on Reinforcement Learning (RL) that learns to correct even non-stationary errors from scratch. At the…

Fast neutral-atom transport and transfer between optical tweezers

Cristina Cicali, Martino Calzavara, Eloisa Cuestas, Tommaso Calarco, Robert Zeier, Felix Motzoi We study the optimization of the transport and transfer of neutral atoms between optical tweezers, both critical steps in the implementation of quantum computers and simulators. We analyze four experimentally relevant pulse shapes (piecewise linear, piecewise quadratic, minimum jerk, and a combination of linear and minimum jerk), and…
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The QCFD (Quantum Computational Fluid Dynamics) project is funded under the European Union’s Horizon Programme (HORIZON-CL4-2021-DIGITAL-EMERGING-02-10), Grant Agreement 101080085 QCFD.