Predicting quantum dynamical cost landscapes with deep learning

Abstract

State-of-the-art quantum algorithms routinely tune dynamically parametrized cost functionals for combinatorics, machine learning, equation-solving, or energy minimization. However, large search complexity often demands many (noisy) quantum measurements, leading to the increasing use of classical probability models to estimate which areas in the cost functional landscape are of highest interest. Introducing deep learning based modelling of the landscape, we demonstrate an order of magnitude increases in accuracy and speed over state-of-the-art Bayesian methods. Moreover, once trained the deep neural network enables the extraction of information at a much faster rate than conventional numerical simulation. This allows for on-the-fly experimental optimizations and detailed classification of complexity and navigability throughout the phase diagram of the landscape.

arXiv comment12 pages, 8 figures

References in corpus (47)

• SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python
• TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
• Quantum Computing in the NISQ era and beyond
• Supplementary information for "Quantum supremacy using a programmable superconducting processor"
• A variational eigenvalue solver on a quantum processor
• Hardware-efficient Variational Quantum Eigensolver for Small Molecules and Quantum Magnets
• Quantum computational advantage using photons
• Barren plateaus in quantum neural network training landscapes
• Solving the Quantum Many-Body Problem with Artificial Neural Networks
• The Magnus expansion and some of its applications
• Control of quantum phenomena: Past, present, and future
• Training Schrödinger's cat: quantum optimal control
• Generation and manipulation of Schrödinger cat states in Rydberg atom arrays
• Self-Verifying Variational Quantum Simulation of the Lattice Schwinger Model
• Tensor networks for complex quantum systems
• Optimal Control at the Quantum Speed Limit
• Quantum Approximate Optimization of Non-Planar Graph Problems on a Planar Superconducting Processor
• Chopped random-basis quantum optimization
• Realizing Rapid, High-Fidelity, Single-Shot Dispersive Readout of Superconducting Qubits
• Reinforcement Learning in Different Phases of Quantum Control
• Second order gradient ascent pulse engineering
• Quantum Circuit Simplification and Level Compaction
• Quantum many-body dynamics in two dimensions with artificial neural networks
• Fast machine-learning online optimization of ultra-cold-atom experiments
• Towards simulating 2D effects in lattice gauge theories on a quantum computer
• Dressing the chopped-random-basis optimization: a bandwidth-limited access to the trap-free landscape
• Optimal control methods for fast time-varying Hamiltonians
• Are there traps in quantum control landscapes?
• Neural Belief-Propagation Decoders for Quantum Error-Correcting Codes
• Global optimization of quantum dynamics with AlphaZero deep exploration
• Remote optimization of an ultra-cold atoms experiment by experts and citizen scientists
• Charting the circuit QED design landscape using optimal control theory
• Quantum Control Landscapes: A Closer Look
• Exploring the trade-off between fidelity- and time-optimal control of quantum unitary transformations
• Glassy Phase of Optimal Quantum Control
• Counteracting systems of diabaticities using DRAG controls: The status after 10 years
• Quantum Optimal Control: Landscape Structure and Topology
• Optimal quantum control with poor statistics
• Hessian-based optimization of constrained quantum control
• Policy Gradient based Quantum Approximate Optimization Algorithm
• An adaptive quantum approximate optimization algorithm for solving combinatorial problems on a quantum computer
• Exploiting Landscape Geometry to Enhance Quantum Optimal Control
• Searching for quantum optimal controls under severe constraints
• Linear and logarithmic time compositions of quantum many-body operators
• Crowdsourcing human common sense for quantum control
• Exploring Quantum Control Landscape Structure
• Gaussian processes for choosing laser parameters for driven, dissipative Rydberg aggregates

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• Non-Adiabatic Quantum Optimization for Crossing Quantum Phase Transitions
• Toward a Theory of Phase Transitions in Quantum Control Landscapes
• Phenomenon of a stronger trapping behaviour in $\Lambda$-type quantum systems with symmetry
• Quantum optimal control in quantum technologies. Strategic report on current status, visions and goals for research in Europe
• Control landscapes for high-fidelity generation of C-NOT and C-PHASE gates with coherent and environmental driving
• On optimization of coherent and incoherent controls for two-level quantum systems
• On the detailed structure of quantum control landscape for fast single qubit phase-shift gate generation
• Machine learning for predicting control landscape maps of quantum molecular dynamics: Laser-induced three-dimensional alignment of asymmetric top molecules
• Diverse efficiency of observable optimization for four-level quantum systems with higher-order traps