Quantum neuronal sensing of quantum many-body states on a 61-qubit programmable superconducting processor - INSPIRE

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Quantum neuronal sensing of quantum many-body states on a 61-qubit programmable superconducting processor

Ming Gong
(
- Hefei, CUST and
- SRCQS, Shanghai
)
,
He-Liang Huang
(
- Hefei, CUST and
- SRCQS, Shanghai
)
,
Shiyu Wang
(
- Hefei, CUST and
- SRCQS, Shanghai
)
,
Chu Guo
(
- Hefei, CUST and
- SRCQS, Shanghai
)
,
Shaowei Li
(
- Hefei, CUST and
- SRCQS, Shanghai
)

Jan 15, 2022

20 pages

Published in:

Sci.Bull. 68 (2023) 906-912

Published: Apr 7, 2023

e-Print:

2201.05957 [quant-ph]

DOI:

10.1016/j.scib.2023.04.003 (publication)

View in:

ADS Abstract Service

reference search33 citations

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Abstract: (Elsevier B.V.)

Like classical neural networks used in image classification, quantum neural networks can also be trained to learn the input quantum states' features and output the probability distribution to classify the state into two different categories. The experiment is performed on a superconducting quantum processor \textit{Zuchongzhi 1.0}. It begins with the state preparation, generating either the localized or ergodic state. The state is the input state of the QNN, which in our case is a double-layered digital-analog variational quantum circuit. Here we use

N_q+1

trainable single-qubit rotations

R(\theta_i,\phi_i)

and a multi-qubit unitary operation

U

as the ansatz. A quantum-classical hybrid architecture implements the training of the quantum neural network. The classical computer is used to evaluate the gradient from the measurement results marked as ``P", which is the probability of measuring the readout qubit in its

|1\rangle

state. After the training, the QNN can classify the states in localization or ergodicity with the readout of only one qubit. Classifying many-body quantum states with distinct properties and phases of matter is one of the most fundamental tasks in quantum many-body physics. However, due to the exponential complexity that emerges from the enormous numbers of interacting particles, classifying large-scale quantum states has been extremely challenging for classical approaches. Here, we propose a new approach called quantum neuronal sensing. Utilizing a 61-qubit superconducting quantum processor, we show that our scheme can efficiently classify two different types of many-body phenomena: namely the ergodic and localized phases of matter. Our quantum neuronal sensing process allows us to extract the necessary information coming from the statistical characteristics of the eigenspectrum to distinguish these phases of matter by measuring only one qubit and offers better phase resolution than conventional methods, such as measuring the imbalance. Our work demonstrates the feasibility and scalability of quantum neuronal sensing for near-term quantum processors and opens new avenues for exploring quantum many-body phenomena in larger-scale systems.

Note:

20 pages, 3 figures in the main text, and 13 pages, 13 figures, and 1 table in supplementary materials

Quantum neural network
Quantum many-body state
Superconducting qubit
Variational quantum eigensolver
neural network: quantum
quantum circuit: variational
particle: interaction
many-body problem
qubit
quantum state

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