Enhanced quantum state preparation via stochastic prediction of neural network
arXiv:2307.14715 · doi:10.1103/PhysRevA.108.052418
Abstract
In pursuit of enhancing the predication capabilities of the neural network, it has been a longstanding objective to create dataset encompassing a diverse array of samples. The purpose is to broaden the horizons of neural network and continually strive for improved prediction accuracy during training process, which serves as the ultimate evaluation metric. In this paper, we explore an intriguing avenue for enhancing algorithm effectiveness through exploiting the knowledge blindness of neural network. Our approach centers around a machine learning algorithm utilized for preparing arbitrary quantum states in a semiconductor double quantum dot system, a system characterized by highly constrained control degrees of freedom. By leveraging stochastic prediction generated by the neural network, we are able to guide the optimization process to escape local optima. Notably, unlike previous methodologies that employ reinforcement learning to identify pulse patterns, we adopt a training approach akin to supervised learning, ultimately using it to dynamically design the pulse sequence. This approach not only streamlines the learning process but also constrains the size of neural network, thereby improving the efficiency of algorithm.
arXiv comment8pages, 8figures
References in corpus (48)
- Spins in few-electron quantum dots
- Machine learning and the physical sciences
- Solving the Quantum Many-Body Problem with Artificial Neural Networks
- NMR Techniques for Quantum Control and Computation
- Silicon Quantum Electronics
- Quantum information processing with superconducting circuits: a review
- Non-local propagation of correlations in long-range interacting quantum systems
- Training Schrödinger's cat: quantum optimal control
- A programmable two-qubit quantum processor in silicon
- Storing quantum information for 30 seconds in a nanoelectronic device
- Quantum CNOT Gate for Spins in Silicon
- Demonstration of Entanglement of Electrostatically Coupled Singlet-Triplet Qubits
- High-fidelity readout and control of a nuclear spin qubit in silicon
- Optimal control technique for Many Body Quantum Systems dynamics
- Universal quantum control of two-electron spin quantum bits using dynamic nuclear polarization
- Universal quantum computation with spin-1/2 pairs and Heisenberg exchange
- Chopped random-basis quantum optimization
- Fidelity benchmarks for two-qubit gates in silicon
- Quantum control and process tomography of a semiconductor quantum dot hybrid qubit
- From transistor to trapped-ion computers for quantum chemistry
- Composite pulses for robust universal control of singlet-triplet qubits
- Two-axis control of a singlet-triplet qubit with an integrated micromagnet
- Experimental neural network enhanced quantum tomography
- Semiconductor Quantum Computation
- Gate fidelity and coherence of an electron spin in a Si/SiGe quantum dot with micromagnet
- Interlaced Dynamical Decoupling and Coherent Operation of a Singlet-Triplet Qubit
- When does reinforcement learning stand out in quantum control? A comparative study on state preparation
- Deep Reinforcement Learning for Quantum Gate Control
- Notch filtering the nuclear environment of a spin qubit
- Robust quantum gates for singlet-triplet spin qubits using composite pulses
- Conditional operation of a spin qubit
- Automatic spin-chain learning to explore the quantum speed limit
- Reinforcement learning for autonomous preparation of Floquet-engineered states: Inverting the quantum Kapitza oscillator
- Deep Reinforcement Learning Control of Quantum Cartpoles
- A Tutorial on Optimal Control and Reinforcement Learning methods for Quantum Technologies
- Quantum optimal control of multi-level dissipative quantum systems with Reinforcement Learning
- Implementing quantum logic gates with GRAPE: principles and practicalities
- Neural-network-designed pulse sequences for robust control of singlet-triplet qubits
- Quantum Adiabatic Algorithm Design using Reinforcement Learning
- Classifying global state preparation via deep reinforcement learning
- Deep reinforcement learning for universal quantum state preparation via dynamic pulse control
- Universal and optimal coin sequences for high entanglement generation in 1D discrete time quantum walks
- Improving the gate fidelity of capacitively coupled spin qubits
- Fast pulse sequences for dynamically corrected gates in single-triplet qubits
- Optimizing adiabatic quantum pathways via a learning algorithm
- Stochastic learning control of adiabatic speedup in a non-Markovian open qutrit system
- Simulating noisy quantum channels via quantum state preparation algorithms
- Simulation of positive operator-valued measures and quantum instruments via quantum state preparation algorithms