Exponential Enhancement of the Efficiency of Quantum Annealing by Non-Stoquastic Hamiltonians

Non-stoquastic Hamiltonians have both positive and negative signs in off-diagonal elements in their matrix representation in the standard computational basis and thus cannot be simulated efficiently by the standard quantum Monte Carlo method due to the sign problem. We describe our analytical studies of this type of Hamiltonians with infinite-range non-random as well as random interactions from the perspective of possible enhancement of the efficiency of quantum annealing or adiabatic quantum computing. It is shown that multi-body transverse interactions like $XX$ and $XXXXX$ with positive coefficients appended to a stoquastic transverse-field Ising model render the Hamiltonian non-stoquastic and reduce a first-order quantum phase transition in the simple transverse-field case to a second-order transition. This implies that the efficiency of quantum annealing is exponentially enhanced, because a first-order transition has an exponentially small energy gap (and therefore exponentially long computation time) whereas a second-order transition has a polynomially decaying gap (polynomial computation time). The examples presented here represent rare instances where strong quantum effects, in the sense that they cannot be efficiently simulated in the standard quantum Monte Carlo, have analytically been shown to exponentially enhance the efficiency of quantum annealing for combinatorial optimization problems.

[1]  H. Nishimori,et al.  Quantum annealing in the transverse Ising model , 1998, cond-mat/9804280.

[2]  Cedric Yen-Yu Lin,et al.  Different Strategies for Optimization Using the Quantum Adiabatic Algorithm , 2014, 1401.7320.

[3]  S. Jordan,et al.  Adiabatic optimization versus diffusion Monte Carlo methods , 2016, 1607.03389.

[4]  Huaiyu Mi,et al.  Ontologies and Standards in Bioscience Research: For Machine or for Human , 2010, Front. Physio..

[5]  西森 秀稔,et al.  Elements of Phase Transitions and Critical Phenomena , 2011 .

[6]  西森 秀稔 Statistical physics of spin glasses and information processing : an introduction , 2001 .

[7]  H. Katzgraber,et al.  Exponentially Biased Ground-State Sampling of Quantum Annealing Machines with Transverse-Field Driving Hamiltonians. , 2016, Physical review letters.

[8]  Itay Hen,et al.  Practical engineering of hard spin-glass instances , 2016, 1605.03607.

[9]  M. Sipser,et al.  Quantum Computation by Adiabatic Evolution , 2000, quant-ph/0001106.

[10]  M. Paranjape,et al.  Macroscopic quantum tunneling and phase transition of the escape rate in spin systems , 2014 .

[11]  R. Somma,et al.  Quantum approach to classical statistical mechanics. , 2006, Physical review letters.

[12]  Damian S. Steiger,et al.  Heavy Tails in the Distribution of Time to Solution for Classical and Quantum Annealing. , 2015, Physical review letters.

[13]  E. Farhi,et al.  A Quantum Adiabatic Evolution Algorithm Applied to Random Instances of an NP-Complete Problem , 2001, Science.

[14]  Rosenbaum,et al.  Quantum annealing of a disordered magnet , 1999, Science.

[15]  Hidetoshi Nishimori,et al.  Quantum Effects in Neural Networks , 1996 .

[16]  J. Biamonte,et al.  Realizable Hamiltonians for Universal Adiabatic Quantum Computers , 2007, 0704.1287.

[17]  Ryan Babbush,et al.  What is the Computational Value of Finite Range Tunneling , 2015, 1512.02206.

[18]  E. Farhi,et al.  Quantum Adiabatic Evolution Algorithms with Different Paths , 2002, quant-ph/0208135.

[19]  George A. Hagedorn,et al.  A note on the switching adiabatic theorem , 2012, 1204.2318.

[20]  M. Ruskai,et al.  Bounds for the adiabatic approximation with applications to quantum computation , 2006, quant-ph/0603175.

[21]  Firas Hamze,et al.  Glassy Chimeras could be blind to quantum speedup: Designing better benchmarks for quantum annealing machines , 2014, 1401.1546.

[22]  Matthew B. Hastings,et al.  Obstructions to classically simulating the quantum adiabatic algorithm , 2013, Quantum Inf. Comput..

[23]  S. Knysh,et al.  Zero-temperature quantum annealing bottlenecks in the spin-glass phase , 2016, Nature Communications.

[24]  S. Knysh,et al.  Quantum Optimization of Fully-Connected Spin Glasses , 2014, 1406.7553.

[25]  R. Car,et al.  Theory of Quantum Annealing of an Ising Spin Glass , 2002, Science.

[26]  H. Nishimori Statistical Physics of Spin Glasses and Information Processing , 2001 .

[27]  Firas Hamze,et al.  Seeking Quantum Speedup Through Spin Glasses: The Good, the Bad, and the Ugly , 2015, 1505.01545.

[28]  H. Nishimori,et al.  Quantum annealing with antiferromagnetic fluctuations. , 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

[29]  A. Karimi,et al.  Master‟s thesis , 2011 .

[30]  H. Neven,et al.  Understanding Quantum Tunneling through Quantum Monte Carlo Simulations. , 2015, Physical review letters.

[31]  J. Smolin,et al.  Classical signature of quantum annealing , 2013, Front. Phys..

[32]  Daniel A. Lidar,et al.  Adiabatic approximation with exponential accuracy for many-body systems and quantum computation , 2008, 0808.2697.

[33]  H. Neven,et al.  Scaling analysis and instantons for thermally assisted tunneling and quantum Monte Carlo simulations , 2016, 1603.01293.

[34]  B. Chakrabarti,et al.  Colloquium : Quantum annealing and analog quantum computation , 2008, 0801.2193.

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

[36]  Itay Hen,et al.  Exponential Complexity of the Quantum Adiabatic Algorithm for certain Satisfiability Problems , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[37]  M. Troyer,et al.  Quantum versus classical annealing of Ising spin glasses , 2014, Science.

[38]  Kostyantyn Kechedzhi,et al.  Open system quantum annealing in mean field models with exponential degeneracy , 2015, 1505.05878.

[39]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[40]  Aram Wettroth Harrow,et al.  Simulated Quantum Annealing Can Be Exponentially Faster Than Classical Simulated Annealing , 2016, 2016 IEEE 57th Annual Symposium on Foundations of Computer Science (FOCS).

[41]  A. Young,et al.  First-order phase transition in the quantum adiabatic algorithm. , 2009, Physical review letters.

[42]  Daniel A. Lidar,et al.  Defining and detecting quantum speedup , 2014, Science.

[43]  P. Shor,et al.  Performance of the quantum adiabatic algorithm on random instances of two optimization problems on regular hypergraphs , 2012, 1208.3757.

[44]  T. Jorg,et al.  Energy gaps in quantum first-order mean-field–like transitions: The problems that quantum annealing cannot solve , 2009, 0912.4865.

[45]  Sompolinsky,et al.  Spin-glass models of neural networks. , 1985, Physical review. A, General physics.

[46]  A. Leggett,et al.  Dynamics of the dissipative two-state system , 1987 .

[47]  M. Paranjape,et al.  Macroscopic quantum tunneling and quantum-classical phase transitions of the escape rate in large spin systems , 2014, 1403.4208.

[48]  Itay Hen,et al.  Unraveling Quantum Annealers using Classical Hardness , 2015, Scientific reports.

[49]  Dla Polski,et al.  EURO , 2004 .

[50]  R. Xu,et al.  Theory of open quantum systems , 2002 .

[51]  Alejandro Perdomo-Ortiz,et al.  Strengths and weaknesses of weak-strong cluster problems: A detailed overview of state-of-the-art classical heuristics versus quantum approaches , 2016, 1604.01746.

[52]  Aaas News,et al.  Book Reviews , 1893, Buffalo Medical and Surgical Journal.

[53]  T. Kadowaki Study of Optimization Problems by Quantum Annealing , 2002, quant-ph/0205020.

[54]  Daniel A. Lidar,et al.  Reexamining classical and quantum models for the D-Wave One processor , 2014, 1409.3827.

[55]  D. Amit,et al.  Statistical mechanics of neural networks near saturation , 1987 .

[56]  Daniel A. Lidar,et al.  Probing for quantum speedup in spin-glass problems with planted solutions , 2015, 1502.01663.

[57]  M. Suzuki,et al.  Relationship between d-Dimensional Quantal Spin Systems and (d+1)-Dimensional Ising Systems: Equivalence, Critical Exponents and Systematic Approximants of the Partition Function and Spin Correlations , 1976 .

[58]  Donald Geman,et al.  Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[59]  Andrew Lucas,et al.  Ising formulations of many NP problems , 2013, Front. Physics.

[60]  S. Sinha,et al.  Model study of dissipation in quantum phase transitions , 2011, 1104.5306.

[61]  H. Nishimori,et al.  Convergence of Quantum Annealing with Real-Time Schrodinger Dynamics(General) , 2007, quant-ph/0702252.

[62]  David P. DiVincenzo,et al.  The complexity of stoquastic local Hamiltonian problems , 2006, Quantum Inf. Comput..

[63]  Sandjai Bhulai,et al.  Modelling of Trends in Twitter Using Retweet Graph Dynamics , 2014, WAW.

[64]  H. Nishimori,et al.  Mathematical foundation of quantum annealing , 2008, 0806.1859.

[65]  Sompolinsky,et al.  Storing infinite numbers of patterns in a spin-glass model of neural networks. , 1985, Physical review letters.

[66]  Daniel A. Lidar,et al.  Evidence for quantum annealing with more than one hundred qubits , 2013, Nature Physics.

[67]  H. Nishimori,et al.  Many-body transverse interactions in the quantum annealing of the p-spin ferromagnet , 2012, 1207.2909.

[68]  Hidetoshi Nishimori,et al.  Convergence theorems for quantum annealing , 2006, quant-ph/0608154.

[69]  H. Nishimori,et al.  Quantum annealing with antiferromagnetic transverse interactions for the Hopfield model , 2014, 1410.0450.

[70]  H. Katzgraber,et al.  Ground-state statistics from annealing algorithms: quantum versus classical approaches , 2009 .

[71]  Daniel A. Lidar,et al.  Tunneling and speedup in quantum optimization for permutation-symmetric problems , 2015, 1511.03910.

[72]  Ericka Stricklin-Parker,et al.  Ann , 2005 .