AI-enhanced tuning of quantum dot Hamiltonians toward Majorana modes

Abstract

We propose a neural network-based model capable of learning the broad landscape of working regimes in quantum dot simulators, and using this knowledge to autotune these devices - based on transport measurements - toward obtaining Majorana modes in the structure. The model is trained in an unsupervised manner on synthetic data in the form of conductance maps, using a physics-informed loss that incorporates key properties of Majorana zero modes. We show that, with appropriate training, a deep vision-transformer network can efficiently memorize relation between Hamiltonian parameters and structures on conductance maps and use it to propose parameters update for a quantum dot chain that drive the system toward topological phase. Starting from a broad range of initial detunings in parameter space, a single update step is sufficient to generate nontrivial zero modes. Moreover, by enabling an iterative tuning procedure - where the system acquires updated conductance maps at each step - we demonstrate that the method can address a much larger region of the parameter space.

arXiv comment12 pages, 8 figures, 2 tables

References in corpus (36)

• Unpaired Majorana fermions in quantum wires
• Helical liquids and Majorana bound states in quantum wires
• New directions in the pursuit of Majorana fermions in solid state systems
• A generic new platform for topological quantum computation using semiconductor heterostructures
• Search for Majorana fermions in superconductors
• Realizing Majorana zero modes in superconductor-semiconductor heterostructures
• Introduction to topological superconductivity and Majorana fermions
• Kondo effect in an integer-spin quantum dot
• From Andreev to Majorana bound states in hybrid superconductor-semiconductor nanowires
• Andreev bound states versus Majorana bound states in quantum dot-nanowire-superconductor hybrid structures: Trivial versus topological zero-bias conductance peaks
• Zero-voltage conductance peak from weak antilocalization in a Majorana nanowire
• Realization of a minimal Kitaev chain in coupled quantum dots
• Shuttling a single charge across a one-dimensional array of silicon quantum dots
• How to realize a robust practical Majorana chain in a quantum dot-superconductor linear array
• Experimental Quantum Hamiltonian Learning
• Spin and Majorana polarization in topological superconducting wires
• Parity qubits and poor man's Majorana bound states in double quantum dots
• Learning Quantum Systems
• Nontopological zero-bias peaks in full-shell nanowires induced by flux tunable Andreev states
• Adaptive tuning of Majorana fermions in a quantum dot chain
• Physics-informed neural networks for quantum control
• Colloquium: Advances in automation of quantum dot devices control
• Spin and Charge Signatures of Topological Superconductivity in Rashba Nanowires
• Low-energy sub-gap states in multi-channel Majorana wires
• Autonomous tuning and charge state detection of gate defined quantum dots
• Signatures of Majorana protection in a three-site Kitaev chain
• Linear and non-linear transport across a finite Kitaev chain: an exact analytical study
• Adversarial Hamiltonian learning of quantum dots in a minimal Kitaev chain
• Spin-polarized Andreev tunneling through the Rashba chain
• Robustness of Majorana edge states of short-length Kitaev chains coupled with environment
• Neural network based deep learning analysis of semiconductor quantum dot qubits for automated control
• Machine Learning the Disorder Landscape of Majorana Nanowires
• Majorana excitons in a Kitaev chain of semiconductor quantum dots in a nanowire
• Topological gap protocol based machine learning optimization of Majorana hybrid wires
• Mitigating disorder and optimizing topological indicators with vision-transformer-based neural networks in Majorana nanowires
• Rapid Autotuning of a SiGe Quantum Dot into the Single-Electron Regime with Machine Learning and RF-Reflectometry FPGA-Based Measurements