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

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. 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.

[1]  Haibin Zhang,et al.  Quantum computational advantage via 60-qubit 24-cycle random circuit sampling. , 2021, Science bulletin.

[2]  Haibin Zhang,et al.  Strong Quantum Computational Advantage Using a Superconducting Quantum Processor. , 2021, Physical review letters.

[3]  Patrick J. Coles,et al.  Cost function dependent barren plateaus in shallow parametrized quantum circuits , 2021, Nature Communications.

[4]  F. Vicentini.  Machine learning toolbox for quantum many body physics , 2021, Nature Reviews Physics.

[5]  Jian-Wei Pan,et al.  Observation of Strong and Weak Thermalization in a Superconducting Quantum Processor. , 2021, Physical review letters.

[6]  Jian-Wei Pan,et al.  Quantum walks on a programmable two-dimensional 62-qubit superconducting processor , 2021, Science.

[7]  M. Lukin,et al.  Quantum phases of matter on a 256-atom programmable quantum simulator , 2020, Nature.

[8]  Jian-Wei Pan,et al.  Experimental characterization of the quantum many-body localization transition , 2020, Physical Review Research.

[9]  S. Montangero,et al.  Efficient Tensor Network Ansatz for High-Dimensional Quantum Many-Body Problems. , 2020, Physical review letters.

[10]  Kwan Hui Lim,et al.  Hybrid quantum-classical convolutional neural networks , 2019, Science China Physics, Mechanics & Astronomy.

[11]  Jian-Wei Pan,et al.  Optimal readout of superconducting qubits exploiting high-level states , 2021 .

[12]  Usha Ruby Dr.A,et al.  Binary cross entropy with deep learning technique for Image classification , 2020 .

[13]  W. Schirmacher,et al.  Level statistics and Anderson delocalization in two-dimensional granular materials , 2020, 2008.07155.

[14]  F. Mintert,et al.  Quantum Many-Body Scars in Optical Lattices. , 2020, Physical review letters.

[15]  Yaoshiang Ho,et al.  The Real-World-Weight Cross-Entropy Loss Function: Modeling the Costs of Mislabeling , 2020, IEEE Access.

[16]  H. Fan,et al.  Observation of energy-resolved many-body localization , 2019, 1912.02818.

[17]  Dylan J. Brown,et al.  Observation of two-dimensional Anderson localisation of ultracold atoms , 2019, Nature Communications.

[18]  P. Kunkel,et al.  Experimental extraction of the quantum effective action for a non-equilibrium many-body system , 2019, Nature Physics.

[19]  J. Zakrzewski,et al.  Model of level statistics for disordered interacting quantum many-body systems , 2019, Physical Review B.

[20]  L. Lamata,et al.  Digital-analog quantum computation , 2018, Physical Review A.

[21]  John C. Platt,et al.  Quantum supremacy using a programmable superconducting processor , 2019, Nature.

[22]  J. Cirac,et al.  Restricted Boltzmann machines in quantum physics , 2019, Nature Physics.

[23]  Xiaobo Zhu,et al.  Propagation and Localization of Collective Excitations on a 24-Qubit Superconducting Processor. , 2019, Physical review letters.

[24]  A. Michailidis,et al.  Probing the many-body localization phase transition with superconducting circuits , 2019, Physical Review B.

[25]  Marcello Benedetti,et al.  Parameterized quantum circuits as machine learning models , 2019, Quantum Science and Technology.

[26]  Franco Nori,et al.  Strongly correlated quantum walks with a 12-qubit superconducting processor , 2019, Science.

[27]  C. Gogolin,et al.  Evaluating analytic gradients on quantum hardware , 2018, Physical Review A.

[28]  Soonwon Choi,et al.  Quantum convolutional neural networks , 2018, Nature Physics.

[29]  Jakub Zakrzewski,et al.  Level statistics across the many-body localization transition , 2018, Physical Review B.

[30]  Robert Koenig,et al.  Approximation algorithms for quantum many-body problems , 2018, Journal of Mathematical Physics.

[31]  Immanuel Bloch,et al.  Colloquium : Many-body localization, thermalization, and entanglement , 2018, Reviews of Modern Physics.

[32]  J. Escalante,et al.  Level spacing statistics for light in two-dimensional disordered photonic crystals , 2018, Scientific Reports.

[33]  Keisuke Fujii,et al.  Quantum circuit learning , 2018, Physical Review A.

[34]  Matthias Troyer,et al.  Neural-network quantum state tomography , 2018 .

[35]  Yusuke Nomura,et al.  Constructing exact representations of quantum many-body systems with deep neural networks , 2018, Nature Communications.

[36]  O. Thomas,et al.  Experimental realization of a Rydberg optical Feshbach resonance in a quantum many-body system , 2017, Nature Communications.

[37]  Jinguo Liu,et al.  Approximating quantum many-body wave functions using artificial neural networks , 2017, 1704.05148.

[38]  Tatsuhiko N. Ikeda,et al.  Thermalization and prethermalization in isolated quantum systems: a theoretical overview , 2017, 1712.08790.

[39]  C. Monroe,et al.  Observation of a many-body dynamical phase transition with a 53-qubit quantum simulator , 2017, Nature.

[40]  M. Lukin,et al.  Probing many-body dynamics on a 51-atom quantum simulator , 2017, Nature.

[41]  J. Schmiedmayer,et al.  Experimental characterization of a quantum many-body system via higher-order correlations , 2015, Nature.

[42]  Lu-Ming Duan,et al.  Efficient representation of quantum many-body states with deep neural networks , 2017, Nature Communications.

[43]  Scott Aaronson,et al.  Complexity-Theoretic Foundations of Quantum Supremacy Experiments , 2016, CCC.

[44]  Matthias Troyer,et al.  Solving the quantum many-body problem with artificial neural networks , 2016, Science.

[45]  L. Balents,et al.  Quantum spin liquids: a review , 2016, Reports on progress in physics. Physical Society.

[46]  C. F. Roos,et al.  Efficient tomography of a quantum many-body system , 2016, Nature Physics.

[47]  Roger G. Melko,et al.  Machine learning phases of matter , 2016, Nature Physics.

[48]  C. Hainaut,et al.  Experimental Observation of Two-Dimensional Anderson Localization with the Atomic Kicked Rotor. , 2015, Physical review letters.

[49]  F. Alet,et al.  Many-body localization edge in the random-field Heisenberg chain , 2014, 1411.0660.

[50]  J. Eisert,et al.  Quantum many-body systems out of equilibrium , 2014, Nature Physics.

[51]  E. Bogomolny,et al.  Distribution of the ratio of consecutive level spacings in random matrix ensembles. , 2012, Physical review letters.

[52]  D. Gross,et al.  Efficient quantum state tomography. , 2010, Nature communications.

[53]  C. Miniatura,et al.  Coherent matter wave transport in speckle potentials , 2007, cond-mat/0702183.

[54]  D. Huse,et al.  Localization of interacting fermions at high temperature , 2006, cond-mat/0610854.

[55]  Shie Mannor,et al.  The cross entropy method for classification , 2005, ICML.

[56]  G. Vidal.  Efficient simulation of one-dimensional quantum many-body systems. , 2003, Physical review letters.

[57]  X. Wen.  THEORY OF THE EDGE STATES IN FRACTIONAL QUANTUM HALL EFFECTS , 1992 .