Cloud on-demand emulation of quantum dynamics with tensor networks

We introduce a tensor network based emulator, simulating a programmable analog quantum processing unit (QPU). The software package is fully integrated in a cloud platform providing a common interface for executing jobs on a HPC cluster as well as dispatching them to a QPU device. We also present typical emulation use cases in the context of Neutral Atom Quantum Processors, such as evaluating the quality of a state preparation pulse sequence, and solving Maximum Independent Set problems by applying a parallel sweep over a set of input pulse parameter values, for systems composed of a large number of qubits.

[1]  R. S. Said,et al.  QuOCS: The quantum optimal control suite , 2022, Comput. Phys. Commun..

[2]  D. Jaschke,et al.  Ab-initio two-dimensional digital twin for quantum computer benchmarking , 2022, 2210.03763.

[3]  E. Jennings,et al.  A Kubernetes 'Bridge' operator between cloud and external resources , 2022, ArXiv.

[4]  Trevor Vincent,et al.  Quantum computational advantage with a programmable photonic processor , 2022, Nature.

[5]  S. Carrasco,et al.  Quantum Optimal Control via Semi-Automatic Differentiation , 2022, Quantum.

[6]  Patrick J. Coles,et al.  Out-of-distribution generalization for learning quantum dynamics , 2022, ArXiv.

[7]  Patrick J. Coles,et al.  Dynamical simulation via quantum machine learning with provable generalization , 2022, ArXiv.

[8]  P. Zoller,et al.  Characterization and Verification of Trotterized Digital Quantum Simulation Via Hamiltonian and Liouvillian Learning , 2022, PRX Quantum.

[9]  H. Bluhm,et al.  qopt: An Experiment-Oriented Software Package for Qubit Simulation and Quantum Optimal Control , 2022, Physical Review Applied.

[10]  G. Carleo,et al.  NetKet 3: Machine Learning Toolbox for Many-Body Quantum Systems , 2021, SciPost Physics Codebases.

[11]  P. Zhang,et al.  Solving the Sampling Problem of the Sycamore Quantum Circuits. , 2021, Physical review letters.

[12]  F. Nori,et al.  Pulse-level noisy quantum circuits with QuTiP , 2021, Quantum.

[13]  Peter J. Karalekas,et al.  Pulser: An open-source package for the design of pulse sequences in programmable neutral-atom arrays , 2021, Quantum.

[14]  E. M. Stoudenmire,et al.  The ITensor Software Library for Tensor Network Calculations , 2020, SciPost Physics Codebases.

[15]  M. Lukin,et al.  Quantum optimization of maximum independent set using Rydberg atom arrays , 2018, Science.

[16]  Rupak Biswas,et al.  HybridQ: A Hybrid Simulator for Quantum Circuits , 2021, 2021 IEEE/ACM Second International Workshop on Quantum Computing Software (QCS).

[17]  Gokul Subramanian Ravi Quantum Computing in the Cloud: Analyzing job and machine characteristics , 2021, 2021 IEEE International Symposium on Workload Characterization (IISWC).

[18]  Travis S. Humble,et al.  Quantum Computers for High-Performance Computing , 2021, IEEE Micro.

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

[20]  Minh C. Tran,et al.  Theory of Trotter Error with Commutator Scaling , 2021 .

[21]  D. Barredo,et al.  Quantum simulation of 2D antiferromagnets with hundreds of Rydberg atoms , 2020, Nature.

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

[23]  Shannon Whitlock,et al.  Quantum simulation and computing with Rydberg-interacting qubits , 2020, AVS Quantum Science.

[24]  Jian-Wei Pan,et al.  Quantum computational advantage using photons , 2020, Science.

[25]  Yiannis Georgiou,et al.  Container Orchestration on HPC Systems , 2020, 2020 IEEE 13th International Conference on Cloud Computing (CLOUD).

[26]  Nathan Rini,et al.  REST API , 2020, Professional WordPress® Plugin Development.

[27]  S. White,et al.  Time-dependent variational principle with ancillary Krylov subspace , 2020, 2005.06104.

[28]  Yevgeny Bar Lev,et al.  Studying dynamics in two-dimensional quantum lattices using tree tensor network states , 2020, 2003.08944.

[29]  Daniel J. Egger,et al.  Qiskit pulse: programming quantum computers through the cloud with pulses , 2020, Quantum Science and Technology.

[30]  T. Lahaye,et al.  Many-body physics with individually controlled Rydberg atoms , 2020, 2002.07413.

[31]  Fabio Baruffa,et al.  Intel Quantum Simulator: a cloud-ready high-performance simulator of quantum circuits , 2020, Quantum Science and Technology.

[32]  M. Aichhorn,et al.  Time dependent variational principle for tree Tensor Networks , 2019, SciPost Physics.

[33]  M. Rams,et al.  Breaking the Entanglement Barrier: Tensor Network Simulation of Quantum Transport. , 2019, Physical review letters.

[34]  F. Verstraete,et al.  Symmetric cluster expansions with tensor networks , 2019, 1912.10512.

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

[36]  Patrick J. Coles,et al.  Variational fast forwarding for quantum simulation beyond the coherence time , 2019, 1910.04292.

[37]  Thomas Kohler,et al.  Time-evolution methods for matrix-product states , 2019, Annals of Physics.

[38]  T. Barthel,et al.  Optimized Lie–Trotter–Suzuki decompositions for two and three non-commuting terms , 2019, Annals of Physics.

[39]  Mark Saffman,et al.  Quantum computing with neutral atoms , 2017, National science review.

[40]  D. Barredo,et al.  Analysis of imperfections in the coherent optical excitation of single atoms to Rydberg states , 2018, 1802.10424.

[41]  Travis S. Humble,et al.  A language and hardware independent approach to quantum-classical computing , 2017, SoftwareX.

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

[43]  Masashi Sugiyama,et al.  Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 2 Applications and Future Perspectives , 2017, Found. Trends Mach. Learn..

[44]  D. Barredo,et al.  Optical Control of the Resonant Dipole-Dipole Interaction between Rydberg Atoms. , 2017, Physical review letters.

[45]  Ying Ran,et al.  Anyon condensation and a generic tensor-network construction for symmetry-protected topological phases , 2016, 1611.07652.

[46]  Alan Edelman,et al.  Julia: A Fresh Approach to Numerical Computing , 2014, SIAM Rev..

[47]  Andrzej Cichocki,et al.  Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 1 Low-Rank Tensor Decompositions , 2016, Found. Trends Mach. Learn..

[48]  Christopher T. Chubb,et al.  Hand-waving and interpretive dance: an introductory course on tensor networks , 2016, 1603.03039.

[49]  Ivan Oseledets,et al.  Unifying time evolution and optimization with matrix product states , 2014, 1408.5056.

[50]  David J. Schwab,et al.  Supervised Learning with Tensor Networks , 2016, NIPS.

[51]  Frank Pollmann,et al.  Time-evolving a matrix product state with long-ranged interactions , 2014, 1407.1832.

[52]  M. Troyer,et al.  Probing the stability of the spin liquid phases in the Kitaev-Heisenberg model using tensor network algorithms , 2014, 1408.4020.

[53]  B. Lanyon,et al.  Quasiparticle engineering and entanglement propagation in a quantum many-body system , 2014, Nature.

[54]  Hideo Aoki,et al.  Nonequilibrium dynamical mean-field theory and its applications , 2013, 1310.5329.

[55]  Roman Orus,et al.  A Practical Introduction to Tensor Networks: Matrix Product States and Projected Entangled Pair States , 2013, 1306.2164.

[56]  Franco Nori,et al.  QuTiP: An open-source Python framework for the dynamics of open quantum systems , 2011, Comput. Phys. Commun..

[57]  Ivan Oseledets,et al.  Tensor-Train Decomposition , 2011, SIAM J. Sci. Comput..

[58]  U. Schollwoeck The density-matrix renormalization group in the age of matrix product states , 2010, 1008.3477.

[59]  Thomas G. Walker,et al.  Quantum information with Rydberg atoms , 2009, 0909.4777.

[60]  G. Evenbly,et al.  Simulation of two-dimensional quantum systems using a tree tensor network that exploits the entropic area law , 2009, 0903.5017.

[61]  Nicholas J. Higham,et al.  Functions of matrices - theory and computation , 2008 .

[62]  J. Latorre Entanglement entropy and the simulation of quantum mechanics , 2006, quant-ph/0611271.

[63]  G. Vidal,et al.  Classical simulation of quantum many-body systems with a tree tensor network , 2005, quant-ph/0511070.

[64]  F. Verstraete,et al.  Renormalization algorithms for Quantum-Many Body Systems in two and higher dimensions , 2004, cond-mat/0407066.

[65]  Andy B. Yoo,et al.  Approved for Public Release; Further Dissemination Unlimited X-ray Pulse Compression Using Strained Crystals X-ray Pulse Compression Using Strained Crystals , 2002 .

[66]  Cleve B. Moler,et al.  Nineteen Dubious Ways to Compute the Exponential of a Matrix, Twenty-Five Years Later , 1978, SIAM Rev..

[67]  Lukin,et al.  Fast quantum gates for neutral atoms , 2000, Physical review letters.

[68]  P. Knight,et al.  The Quantum jump approach to dissipative dynamics in quantum optics , 1997, quant-ph/9702007.

[69]  Ernst Hairer,et al.  Solving Ordinary Differential Equations I: Nonstiff Problems , 2009 .