Approaching the adiabatic timescale with machine-learning
arXiv:1809.03124 · doi:10.1073/pnas.1811501115
Abstract
The control and manipulation of quantum systems without excitation is challenging, due to the complexities in fully modeling such systems accurately and the difficulties in controlling these inherently fragile systems experimentally. For example, while protocols to decompress Bose-Einstein condensates (BEC) faster than the adiabatic timescale (without excitation or loss) have been well developed theoretically, experimental implementations of these protocols have yet to reach speeds faster than the adiabatic timescale. In this work, we experimentally demonstrate an alternative approach based on a machine learning algorithm which makes progress towards this goal. The algorithm is given control of the coupled decompression and transport of a metastable helium condensate, with its performance determined after each experimental iteration by measuring the excitations of the resultant BEC. After each iteration the algorithm adjusts its internal model of the system to create an improved control output for the next iteration. Given sufficient control over the decompression, the algorithm converges to a novel solution that sets the current speed record in relation to the adiabatic timescale, beating out other experimental realizations based on theoretical approaches. This method presents a feasible approach for implementing fast state preparations or transformations in other quantum systems, without requiring a solution to a theoretical model of the system. Implications for fundamental physics and cooling are discussed.
arXiv comment7 pages main text, 2 pages supporting information
References in corpus (43)
- Adiabatic Quantum Computing
- Fast optimal frictionless atom cooling in harmonic traps
- Shortcuts to adiabaticity
- Quantum control theory and applications: A survey
- Fermi-Hubbard physics with atoms in an optical lattice
- Optimal Control at the Quantum Speed Limit
- Measurement of the Temperature Dependence of the Casimir-Polder Force
- Optimal control technique for Many Body Quantum Systems dynamics
- Bounds for the adiabatic approximation with applications to quantum computation
- Molecular Quantum Computing by an Optimal Control Algorithm for Unitary Transformations
- Reinforcement Learning in Different Phases of Quantum Control
- Coherent Diabatic Ion Transport and Separation in a Multi-Zone Trap Array
- Optimal quantum control using randomized benchmarking
- Fast machine-learning online optimization of ultra-cold-atom experiments
- Shortcut to adiabaticity for an interacting Bose-Einstein condensate
- Ghost Imaging with Atoms
- Fast optimal transition between two equilibrium states
- Optimal trajectories for efficient atomic transport without final excitation
- Adaptive hybrid optimal quantum control for imprecisely characterized systems
- Optimal transport of ultracold atoms in the non-adiabatic regime
- Exploring the Quantum Speed Limit with Computer Games
- Shortcuts to adiabaticity for trapped ultracold gases
- Learning in Quantum Control: High-Dimensional Global Optimization for Noisy Quantum Dynamics
- Multiparameter optimisation of a magneto-optical trap using deep learning
- Prospective study on microscopic potential with Gogny interaction
- Prediction and real-time compensation of qubit decoherence via machine-learning
- Non-destructive Faraday imaging of dynamically controlled ultracold atoms
- Using Recurrent Neural Networks to Optimize Dynamical Decoupling for Quantum Memory
- Shortcuts to adiabaticity in the strongly-coupled regime: nonadiabatic control of a unitary Fermi gas
- Fast shuttling of a trapped ion in the presence of noise
- Solving the quantum many-body problem via correlations measured with a momentum microscope
- Controlling spontaneous-emission noise in measurement-based feedback cooling of a Bose-Einstein Condensate
- Paired atom laser beams created via four-wave mixing
- Feedback control of an interacting Bose-Einstein condensate using phase-contrast imaging
- Stochastic optimization of a cold atom experiment using a genetic algorithm
- Autonomous Calibration of Single Spin Qubit Operations
- Achieving the Quantum Ground State of a Mechanical Oscillator using a Bose-Einstein Condensate with Back-Action and Cold Damping feedback schemes
- Optimal diabatic dynamics of Majorana-based quantum gates
- Robustness of System-Filter Separation for the Feedback Control of a Quantum Harmonic Oscillator Undergoing Continuous Position Measurement
- Evolutionary optimization of an experimental apparatus
- Can math beat gamers in Quantum Moves?
- Bogoliubov-Cherenkov Radiation in an Atom Laser
- Fast and efficient transport of large ion clouds
Cited by in corpus (19)
- Intelligent metaphotonics empowered by machine learning
- Breaking Adiabatic Quantum Control with Deep Learning
- Bayesian optimal control of GHZ states in Rydberg lattices
- Optimal quantum control with poor statistics
- Phase diagram and optimal control for n-tupling discrete time crystal
- Single-site-resolved imaging of ultracold atoms in a triangular optical lattice
- Preparation of ordered states in ultra-cold gases using Bayesian optimization
- Machine-learning assisted quantum control in random environment
- Active Learning Algorithm for Computational Physics
- Connection between inverse engineering and optimal control in shortcuts to adiabaticity
- Shortcuts to adiabaticity for an interacting Bose-Einstein condensate via exact solutions of the generalized Ermakov equation
- Active Learning Approach to Optimization of Experimental Control
- Fast and robust particle shuttling for quantum science and technology
- Creation and manipulation of quantized vortices in Bose-Einstein condensates using reinforcement learning
- On the survival of the quantum depletion of a condensate after release from a magnetic trap
- Trap Frequency Measurement with a Pulsed Atom Laser
- Transitionless Quantum Driving of the Tomonaga-Luttinger Liquid
- Bayesian optimization of non-classical optomechanical correlations
- Adiabatic Dynamics of Entanglement