QuCumber: wavefunction reconstruction with neural networks

Abstract

As we enter a new era of quantum technology, it is increasingly important to develop methods to aid in the accurate preparation of quantum states for a variety of materials, matter, and devices. Computational techniques can be used to reconstruct a state from data, however the growing number of qubits demands ongoing algorithmic advances in order to keep pace with experiments. In this paper, we present an open-source software package called QuCumber that uses machine learning to reconstruct a quantum state consistent with a set of projective measurements. QuCumber uses a restricted Boltzmann machine to efficiently represent the quantum wavefunction for a large number of qubits. New measurements can be generated from the machine to obtain physical observables not easily accessible from the original data.

See https://github.com/PIQuIL/QuCumber

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Cited by in corpus (15)

• Discovering physical concepts with neural networks
• Integrating Neural Networks with a Quantum Simulator for State Reconstruction
• Machine learning quantum states in the NISQ era
• Neural network quantum state tomography in a two-qubit experiment
• Characterizing the loss landscape of variational quantum circuits
• The learnability scaling of quantum states: restricted Boltzmann machines
• Eigenstate extraction with neural-network tomography
• U(1) symmetric recurrent neural networks for quantum state reconstruction
• Reconstructing quantum molecular rotor ground states
• Adaptive Quantum State Tomography with Active Learning
• Pruning a restricted Boltzmann machine for quantum state reconstruction
• Machine Learning for Estimation and Control of Quantum Systems
• Probing Criticality in Quantum Spin Chains with Neural Networks
• Learning Algebraic Models of Quantum Entanglement
• Neural quantum states for entanglement depth certification from randomized Pauli measurements