Supervised learning of random quantum circuits via scalable neural networks

  title={Supervised learning of random quantum circuits via scalable neural networks},
  author={S. Cantori and Diego Vitali and S. Pilati},
Predicting the output of quantum circuits is a hard computational task that plays a pivotal role in the development of universal quantum computers. Here we investigate the supervised learning of output expectation values of random quantum circuits. Deep convolutional neural networks (CNNs) are trained to predict single-qubit and two-qubit expectation values using databases of classically simulated circuits. These circuits are represented via an appropriately designed one-hot encoding of the… 



Simulation of Quantum Circuits Using the Big-Batch Tensor Network Method.

The proposed big-batch method is extended to a full-amplitude simulation approach that is more efficient than the existing Schrödinger method on shallow circuits and the Schr Ödinger-Feynman method in general, enabling the state vector of Google's simplifiable circuit with n=43 qubits and m=14 cycles to be obtained using only one GPU.

A deep learning model for noise prediction on near-term quantum devices

An approach for a deep-learning compiler of quantum circuits, designed to reduce the output noise of circuits run on a specific device, and suggests that device-specific compilers using machine learning may yield higher fidelity operations and provide insights for the design of noise models.

Scalable neural networks for the efficient learning of disordered quantum systems.

This article implements a scalable convolutional network that can address arbitrary system sizes and demonstrates that the network scalability enables a transfer-learning protocol, whereby a pretraining performed on small systems drastically accelerates the learning of large-system properties, allowing reaching high accuracy with small training sets.

Power of data in quantum machine learning

This work shows that some problems that are classically hard to compute can be easily predicted by classical machines learning from data and proposes a projected quantum model that provides a simple and rigorous quantum speed-up for a learning problem in the fault-tolerant regime.

Neural predictor based quantum architecture search

It is demonstrated a neural predictor guided QAS can discover powerful quantum circuit ansatz, yielding state-of-the-art results for various examples from quantum simulation and quantum machine learning.

Machine learning assisted readout of trapped-ion qubits

A simple yet versatile neural network is exploited to classify multi-qubit quantum states, which is trained using experimental data and efficiently treat qubit readout crosstalk, resulting in a 30% improvement in detection error over the conventional threshold method.

Precise measurement of quantum observables with neural-network estimators

It is shown that unsupervised learning of single-qubit data allows the trained networks to accommodate measurements of complex observables, otherwise costly using traditional post-processing techniques, without requiring additional quantum resources.

Defining and detecting quantum speedup

Here, it is shown how to define and measure quantum speedup and how to avoid pitfalls that might mask or fake such a speedup, and the subtle nature of the quantum speed up question is illustrated.

An introduction to quantum machine learning

This contribution gives a systematic overview of the emerging field of quantum machine learning and presents the approaches as well as technical details in an accessible way, and discusses the potential of a future theory of quantum learning.

Strong Quantum Computational Advantage Using a Superconducting Quantum Processor.

This work develops a two-dimensional programmable superconducting quantum processor, Zuchongzhi, which is composed of 66 functional qubits in a tunable coupling architecture and establishes an unambiguous quantum computational advantage that is infeasible for classical computation in a reasonable amount of time.