Combining the synergistic control capabilities of modelling and experiments: illustration of finding a minimum time quantum objective

Abstract

A common way to manipulate a quantum system, for example spins or artificial atoms, is to use properly tailored control pulses. In order to accomplish quantum information tasks before coherence is lost, it is crucial to implement the control in the shortest possible time. Here we report the near time-optimal preparation of a Bell state with fidelity higher than $99\%$ in an NMR experiment, which is feasible by combining the synergistic capabilities of modelling and experiments operating in tandem. The pulses preparing the Bell state are found by experiments that are recursively assisted with a gradient-based optimization algorithm working with a model. Thus, we explore the interplay between model-based numerical optimal design and experimental-based learning control. Utilizing the balanced synergism between the dual approaches should have broad applications for accelerating the search for optimal quantum controls.

References in corpus (16)

• NMR Techniques for Quantum Control and Computation
• Training Schrödinger's cat: quantum optimal control
• Time Optimal Control in Spin Systems
• High fidelity spin entanglement using optimal control
• Quantum Brachistochrone
• Hybrid Quantum-Classical Approach to Quantum Optimal Control
• Sub-Riemannian Geometry and Time Optimal Control of Three Spin Systems: Quantum Gates and Coherence Transfer
• Driving at the quantum speed limit: Optimal control of a two-level system
• Adaptive hybrid optimal quantum control for imprecisely characterized systems
• Quantum brachistochrone curves as geodesics: obtaining accurate control protocols for time-optimal quantum gates
• Experimental time-optimal universal control of spin qubits in solids
• Preparation of pseudo-pure states by line-selective pulses in Nuclear Magnetic Resonance
• A Data-Driven Gradient Algorithm for High-Precision Quantum Control
• Experimental exploration over a quantum control landscape through nuclear magnetic resonance
• Controlling qubit networks in polynomial time
• In situ upgrade of quantum simulators to universal computers

Cited by in corpus (11)

• From pulses to circuits and back again: A quantum optimal control perspective on variational quantum algorithms
• Progress toward favorable landscapes in quantum combinatorial optimization
• Assessing three closed-loop learning algorithms by searching for high-quality quantum control pulses
• Complete Quantum-State Tomography with a Local Random Field
• Learning How to Dynamically Decouple
• Controlling NMR spin systems for quantum computation
• Drawing together control landscape and tomography principles
• Experimental graybox quantum system identification and control
• Fast generation of entanglement between coupled spins using optimization and deep learning methods
• Quantum optimal control in quantum technologies. Strategic report on current status, visions and goals for research in Europe
• Algorithmic Primitives for Quantum-Assisted Quantum Control