An efficient quantum algorithm for the time evolution of parameterized circuits

We introduce a novel hybrid algorithm to simulate the real-time evolution of quantum systems using parameterized quantum circuits. The method, named "projected – Variational Quantum Dynamics" (p-VQD) realizes an iterative, global projection of the exact time evolution onto the parameterized manifold. In the small time-step limit, this is equivalent to the McLachlan's variational principle. Our approach is efficient in the sense that it exhibits an optimal linear scaling with the total number of variational parameters. Furthermore, it is global in the sense that it uses the variational principle to optimize all parameters at once. The global nature of our approach then significantly extends the scope of existing efficient variational methods, that instead typically rely on the iterative optimization of a restricted subset of variational parameters. Through numerical experiments, we also show that our approach is particularly advantageous over existing global optimization algorithms based on the time-dependent variational principle that, due to a demanding quadratic scaling with parameter numbers, are unsuitable for large parameterized quantum circuits.

[1]  Tobias Haug,et al.  Optimal training of variational quantum algorithms without barren plateaus , 2021, ArXiv.

[2]  D. Coppersmith An approximate Fourier transform useful in quantum factoring , 2002, quant-ph/0201067.

[3]  Gavin E. Crooks,et al.  Gradients of parameterized quantum gates using the parameter-shift rule and gate decomposition , 2019, 1905.13311.

[4]  Patrick J. Coles,et al.  Variational quantum algorithm for estimating the quantum Fisher information , 2020, Physical Review Research.

[5]  M. Cerezo,et al.  Variational quantum algorithms , 2020, Nature Reviews Physics.

[6]  Patrick J. Coles,et al.  Variational Hamiltonian Diagonalization for Dynamical Quantum Simulation , 2020, 2009.02559.

[7]  E. Knill,et al.  Quantum algorithms for fermionic simulations , 2000, cond-mat/0012334.

[8]  B. Bauer,et al.  Quantum Algorithms for Quantum Chemistry and Quantum Materials Science. , 2020, Chemical reviews.

[9]  Elizabeth C. Behrman,et al.  Quantum circuit representation of Bayesian networks , 2021, Expert Syst. Appl..

[10]  E. Tosatti,et al.  Optimization using quantum mechanics: quantum annealing through adiabatic evolution , 2006 .

[11]  Maria Schuld,et al.  Quantum Machine Learning in Feature Hilbert Spaces. , 2018, Physical review letters.

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

[13]  M. Schuld,et al.  Circuit-centric quantum classifiers , 2018, Physical Review A.

[14]  F. Nori,et al.  Quantum Simulation , 2013, Quantum Atom Optics.

[15]  F. Verstraete,et al.  Time-dependent variational principle for quantum lattices. , 2011, Physical review letters.

[16]  Dries Sels,et al.  Geometry and non-adiabatic response in quantum and classical systems , 2016, 1602.01062.

[17]  I. Shparlinski,et al.  Pseudoprime reductions of elliptic curves , 2009, Mathematical Proceedings of the Cambridge Philosophical Society.

[18]  B. Clark,et al.  Unitary block optimization for variational quantum algorithms , 2021, 2102.08403.

[19]  Tsuyoshi Murata,et al.  {m , 1934, ACML.

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

[21]  H. Trotter On the product of semi-groups of operators , 1959 .

[22]  Marin Bukov,et al.  Geometric Speed Limit of Accessible Many-Body State Preparation , 2018, Physical Review X.

[23]  Ying Li,et al.  Theory of variational quantum simulation , 2018, Quantum.

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

[25]  Ying Li,et al.  Efficient Variational Quantum Simulator Incorporating Active Error Minimization , 2016, 1611.09301.

[26]  Garnet Kin-Lic Chan,et al.  Quantum Imaginary Time Evolution, Quantum Lanczos, and Quantum Thermal Averaging , 2019 .

[27]  Stephen K. Gray,et al.  Noise-Resilient Quantum Dynamics Using Symmetry-Preserving Ansatzes , 2019, 1910.06284.

[28]  P. Coveney,et al.  Scalable Quantum Simulation of Molecular Energies , 2015, 1512.06860.

[29]  G. Carleo,et al.  Light-cone effect and supersonic correlations in one- and two-dimensional bosonic superfluids , 2013, 1310.2246.

[30]  M. Suzuki,et al.  General theory of fractal path integrals with applications to many‐body theories and statistical physics , 1991 .

[31]  Patrick J. Coles,et al.  Variational fast forwarding for quantum simulation beyond the coherence time , 2019, npj Quantum Information.

[32]  Marcello Benedetti,et al.  Hardware-efficient variational quantum algorithms for time evolution , 2020, Physical Review Research.

[33]  J. Spall Implementation of the simultaneous perturbation algorithm for stochastic optimization , 1998 .

[34]  J. Stokes,et al.  Quantum Natural Gradient , 2019, Quantum.

[35]  Quantum Digital Signatures , 2001, quant-ph/0105032.

[36]  Michael I. Jordan,et al.  Advances in Neural Information Processing Systems 30 , 1995 .

[37]  J. Gambetta,et al.  Quantum equation of motion for computing molecular excitation energies on a noisy quantum processor , 2019, 1910.12890.

[38]  S. White,et al.  Real-time evolution using the density matrix renormalization group. , 2004, Physical review letters.

[39]  R. Cleve,et al.  Quantum fingerprinting. , 2001, Physical review letters.

[40]  A. Green,et al.  Real- and Imaginary-Time Evolution with Compressed Quantum Circuits , 2020, PRX Quantum.

[41]  J. Herskowitz,et al.  Proceedings of the National Academy of Sciences, USA , 1996, Current Biology.

[42]  D. Abrams,et al.  Simulation of Many-Body Fermi Systems on a Universal Quantum Computer , 1997, quant-ph/9703054.

[43]  Roger Melko,et al.  Quantum Boltzmann Machine , 2016, 1601.02036.

[44]  Guifré Vidal Efficient simulation of one-dimensional quantum many-body systems. , 2004, Physical review letters.

[45]  W. Marsden I and J , 2012 .

[46]  Markus Heyl,et al.  Quantum Many-Body Dynamics in Two Dimensions with Artificial Neural Networks. , 2020, Physical review letters.

[47]  Michele Fabrizio,et al.  Localization and Glassy Dynamics Of Many-Body Quantum Systems , 2011, Scientific Reports.

[48]  A. D. McLachlan,et al.  A variational solution of the time-dependent Schrodinger equation , 1964 .

[49]  Jacob biamonte,et al.  Quantum machine learning , 2016, Nature.

[50]  Edward Grant,et al.  An initialization strategy for addressing barren plateaus in parametrized quantum circuits , 2019, Quantum.

[51]  Gavin E. Crooks,et al.  Measuring Analytic Gradients of General Quantum Evolution with the Stochastic Parameter Shift Rule , 2020, Quantum.

[52]  G. Vidal,et al.  Time-dependent density-matrix renormalization-group using adaptive effective Hilbert spaces , 2004 .

[53]  J. Gambetta,et al.  Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets , 2017, Nature.

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

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

[56]  Travis S. Humble,et al.  Quantum supremacy using a programmable superconducting processor , 2019, Nature.

[57]  Patrick J. Coles,et al.  Variational Quantum Linear Solver. , 2020 .

[58]  Alán Aspuru-Guzik,et al.  A variational eigenvalue solver on a photonic quantum processor , 2013, Nature Communications.

[59]  Kevin Barraclough,et al.  I and i , 2001, BMJ : British Medical Journal.

[60]  A. Green,et al.  Parallel quantum simulation of large systems on small NISQ computers , 2020, npj Quantum Information.

[61]  Alán Aspuru-Guzik,et al.  Quantum autoencoders for efficient compression of quantum data , 2016, 1612.02806.

[62]  T. Martínez,et al.  Hybrid Quantum/Classical Derivative Theory: Analytical Gradients and Excited-State Dynamics for the Multistate Contracted Variational Quantum Eigensolver , 2019, 1906.08728.

[63]  J. Demmel On condition numbers and the distance to the nearest ill-posed problem , 2015 .

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

[65]  Kishor Bharti,et al.  Quantum Assisted Simulator , 2020 .

[66]  I. Kassal,et al.  Polynomial-time quantum algorithm for the simulation of chemical dynamics , 2008, Proceedings of the National Academy of Sciences.

[67]  Kristan Temme,et al.  Supervised learning with quantum-enhanced feature spaces , 2018, Nature.

[68]  Iordanis Kerenidis,et al.  q-means: A quantum algorithm for unsupervised machine learning , 2018, NeurIPS.

[69]  P. Dirac Note on Exchange Phenomena in the Thomas Atom , 1930, Mathematical Proceedings of the Cambridge Philosophical Society.

[70]  M. Mézard,et al.  Journal of Statistical Mechanics: Theory and Experiment , 2011 .

[71]  Ryan Babbush,et al.  Barren plateaus in quantum neural network training landscapes , 2018, Nature Communications.

[72]  Peter W. Shor,et al.  Algorithms for quantum computation: discrete logarithms and factoring , 1994, Proceedings 35th Annual Symposium on Foundations of Computer Science.