7,306 research outputs found
Hybrid Optimization Schemes for Quantum Control
Optimal control theory is a powerful tool for solving control problems in
quantum mechanics, ranging from the control of chemical reactions to the
implementation of gates in a quantum computer. Gradient-based optimization
methods are able to find high fidelity controls, but require considerable
numerical effort and often yield highly complex solutions. We propose here to
employ a two-stage optimization scheme to significantly speed up convergence
and achieve simpler controls. The control is initially parametrized using only
a few free parameters, such that optimization in this pruned search space can
be performed with a simplex method. The result, considered now simply as an
arbitrary function on a time grid, is the starting point for further
optimization with a gradient-based method that can quickly converge to high
fidelities. We illustrate the success of this hybrid technique by optimizing a
holonomic phasegate for two superconducting transmon qubits coupled with a
shared transmission line resonator, showing that a combination of Nelder-Mead
simplex and Krotov's method yields considerably better results than either one
of the two methods alone.Comment: 17 pages, 5 figures, 2 table
Universal Set of Gates for Microwave Dressed-State Quantum Computing
We propose a set of techniques that enable universal quantum computing to be
carried out using dressed states. This applies in particular to the effort of
realising quantum computation in trapped ions using long-wavelength radiation,
where coupling enhancement is achieved by means of static magnetic-field
gradient. We show how the presence of dressing fields enables the construction
of robust single and multi-qubit gates despite the unavoidable presence of
magnetic noise, an approach that can be generalised to provide shielding in any
analogous quantum system that relies on the coupling of electronic degrees of
freedom via bosonic modes
Reinforcement Learning in Different Phases of Quantum Control
The ability to prepare a physical system in a desired quantum state is
central to many areas of physics such as nuclear magnetic resonance, cold
atoms, and quantum computing. Yet, preparing states quickly and with high
fidelity remains a formidable challenge. In this work we implement cutting-edge
Reinforcement Learning (RL) techniques and show that their performance is
comparable to optimal control methods in the task of finding short,
high-fidelity driving protocol from an initial to a target state in
non-integrable many-body quantum systems of interacting qubits. RL methods
learn about the underlying physical system solely through a single scalar
reward (the fidelity of the resulting state) calculated from numerical
simulations of the physical system. We further show that quantum state
manipulation, viewed as an optimization problem, exhibits a spin-glass-like
phase transition in the space of protocols as a function of the protocol
duration. Our RL-aided approach helps identify variational protocols with
nearly optimal fidelity, even in the glassy phase, where optimal state
manipulation is exponentially hard. This study highlights the potential
usefulness of RL for applications in out-of-equilibrium quantum physics.Comment: A legend for the videos referred to in the paper is available on
https://mgbukov.github.io/RL_movies
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