A deceptive step towards quantum speedup detection

A deceptive step towards quantum speedup detection

Salvatore Mandrà salvatore.mandra@nasa.gov Quantum Artificial Intelligence Lab., NASA Ames Research Center, Moffett Field, CA 94035, USA Stinger Ghaffarian Technologies Inc., 7701 Greenbelt Rd., Suite 400, Greenbelt, MD 20770    Helmut G. Katzgraber hgk@tamu.edu Department of Physics and Astronomy, Texas A&M University, College Station, Texas 77843-4242, USA 1QB Information Technologies (1QBit), Vancouver, British Columbia, Canada V6B 4W4 Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, New Mexico 87501 USA
July 14, 2019

There have been multiple attempts to design synthetic benchmark problems with the goal of detecting quantum speedup in current quantum annealing machines. To date, classical heuristics have consistently outperformed quantum-annealing based approaches. Here we introduce a class of problems based on frustrated cluster loops — deceptive cluster loops — for which all currently known state-of-the-art classical heuristics are outperformed by the DW2000Qquantum annealing machine. While there is a sizable constant speedup over all known classical heuristics, a noticeable improvement in the scaling remains elusive. These results represent the first steps towards a detection of potential quantum speedup, albeit without a scaling improvement and for synthetic benchmark problems.

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I Introduction

Quantum annealing (QA) Kadowaki and Nishimori (1998); Farhi et al. (2001); Finnila et al. (1994); Martoňák et al. (2002); Santoro et al. (2002); Das and Chakrabarti (2008) has been proposed as a potentially efficient heuristic to optimize hard constraint satisfaction problems. In principle, the approach can overcome tall energy barriers commonly found in this class of optimization problems by exploiting quantum effects, thereby potentially outperforming commonly-used heuristics that use thermal kicks to overcome the barriers. However, despite a significant effort by the scientific community towards an optimization technique that, in principle, relies on quantum effects, it is still unclear whether quantum speedup is actually achievable using analog transverse-field quantum annealing approaches.

There have been multiple attempts to define quantum speedup Rønnow et al. (2014a); Mandrà et al. (2016), as well as quantify any “quantumness” and problem-solving efficacy of current commercially-available quantum annealers Johnson et al. (2011); Dickson et al. (2013); Boixo et al. (2014); Katzgraber et al. (2014); Rønnow et al. (2014a); Katzgraber et al. (2015); Heim et al. (2015); Hen et al. (2015); Albash et al. (2015); Martin-Mayor and Hen (2015); Marshall et al. (2016); Denchev et al. (2016); King et al. (2017); Albash and Lidar (2017). However, to date, any convincing detection of an improved scaling of quantum annealing with a transverse field over state-of-the-art classical optimization algorithms remains elusive. The increase in performance of quantum annealing machines in the last few years has resulted in an “arms race” with classical optimization algorithms implemented on CMOS hardware. The goal post to detect quantum speedup continuously keeps moving and has resulted in a renaissance in classical algorithm design to optimize hard constraint-satisfaction problems.

A key ingredient in the detection of quantum speedup is the selection of the optimization problems to be used as benchmark. Ideally, one would want a real-world industrial application where the time to solution of the quantum device scales better than any known algorithm with the size of the input. However, such application problems are not suitable for present-day quantum annealers, either because they require more variables than currently available or because precision requirements cannot be met by current technologies. Random spin-glass problems have been shown to be too easy to detect any scaling improvements Katzgraber et al. (2014, 2015). As such, efforts have shifted to carefully-designed synthetic problems. While some studies focus on post-selection techniques Katzgraber et al. (2015), others focus on the use of planted solutions Hen et al. (2015); King et al. (2017), or the use of gadgets Albash and Lidar (2017). Unfortunately, however, in all planted problems Hen et al. (2015); King et al. (2017) used to date, as well as problems that use gadgets Denchev et al. (2016), the underlying logical structure is easily decoded and the underlying problem trivially solved, sometimes even with exact polynomial methods Mandrà et al. (2016). Therefore, in the quest for quantum speedup, an important step is to design problems where no variable reduction or algorithmic trick can be exploited to reduce the complexity of the problem. Ideally, the benchmark problem should be hard for a small number of variables and “break” all known optimization heuristics.

In this work we introduce a class of benchmark problems designed for DW2000Qquantum annealers whose logical structure is not directly recognizable and whose typical computational complexity can be tuned via a control parameter that tunes the relative strength of inter- vs intracell couplers in the DW2000QChimera Bunyk et al. (2014) topology. Note that this approach can be easily generalized to other topologies. We demonstrate that for a particular setting of the control parameter where the ground state of the virtual problem cannot be decoded, the D-Wave Systems Inc. DW2000Qquantum annealer outperforms all known classical optimization algorithms by approximately two to three orders of magnitude. More precisely, we compare against the two best heuristics to solve Ising-like problems on the DW2000QChimera topology, the Hamze-de Freitas-Selby (HFS) Hamze and de Freitas (2004); Selby (2014) and parallel tempering Monte Carlo with isoenergetic cluster moves (PT+ICM) Zhu et al. (2015) heuristics. Although we were not able to identify the optimal annealing time given the hard limit of as minimum annealing time in the DW2000Qdevice, the scaling is comparable and the speedup persists for increasing system sizes. Therefore, we present the first steps towards the detection of potential quantum speedup, however, for now, without a noticeable scaling improvement. Problems with tunable complexity as the ones shown here, combined with a careful statistical analysis, bulletproof definitions of quantum speedup, the inclusion of power consumption in the analysis, as well as the use of the currently best-available heuristics are key in the assessment of the performance of quantum-enhanced optimization techniques.

The paper is structured as follows. In Sec. II we present some technical details of this study. In particular, we introduce the benchmark problems used in Sec. III. Results are outlined in Sec. IV, followed by a discussion and concluding remarks.

Ii Technical Details

The DW2000Qquantum annealer is designed to optimize classical problem Hamiltonians of the quadratic form


where is known as Chimera graph Bunyk et al. (2014) constructed of a two-dimensional lattice of fully-connected cells. The couplers and biases are programmable parameters that define the optimization problem to be studied. Although the DW2000QChimera architecture graph has been kept fixed since the first commercial generation of the machine, the number of qubits doubled almost every two years. At the moment, the latest DW2000Qchip counts working flux-qubits and working couplers. To minimize the cost function , the DW2000Qquantum chip anneals quantum fluctuations driven by a transverse field of the form


More precisely, the annealing protocol starts with the system initialized to a quantum paramagnetic state. Then, the amplitude of is slowly reduced while the amplitude of the problem Hamiltonian is gradually increased. If the annealing is slow enough, the adiabatic theorem Morita and Nishimori (2008) ensures that the quantum system remains in its instantaneous lowest energy state for the entire annealing protocol. Therefore, (close-to) optimal configurations for can be retrieved by measuring the state of the qubits along the -basis at the end of the anneal.

Given its intrinsic analog nature, combined with the heuristic properties of quantum annealing, the DW2000Qdevice is only able to find the optimum of a cost function up to a probability . Indeed, fast annealing in proximity of level crossings Kadowaki and Nishimori (1998); Farhi et al. (2001); Santoro and Tosatti (2006), as well as quantum dephasing effects Amin et al. (2009); Dickson et al. (2013); Albash and Lidar (2015), thermal excitations Wang et al. (2016); Nishimura et al. (2016); Marshall et al. (2017) and programming errors Mandrà et al. (2015); Katzgraber et al. (2015), can lead to higher energy states of at the end of the anneal. A commonly-accepted metric is the time-to-solution (TTS). The TTS is defined as the time needed for a heuristic, either classical or quantum, to find the lowest energy state with success probability, that is:


where is either the running time (for a classical heuristic) or the annealing time (for the DW2000Qquantum chip) and is the number of repetitions needed to reach the desired success probability Rønnow et al. (2014b). In this work we analyze the TTS as a function of the number of input variables in the problem.

Iii Synthetic Benchmark Problems

In this Section we outline and discuss a new synthetic benchmark we call “deceptive cluster loop” (DCL) problems based on traditional frustrated cluster loop problems. However, DCL problems have a tunable parameter that for particular values hides the underlying logical structure of the planted problem, thus “deceptive.”

iii.1 Traditional frustrated cluster loop problems

Based on the fact that it is typically hard for agnostic optimization algorithms to find the lowest energy state of very long frustrated chains, the frustrated cluster loop à la Hen (H-FCL) is a random model that has been proven to be hard for many classical heuristics Hen et al. (2015). The idea is simple: Given , an arbitrary connectivity graph for the problem Hamiltonian , and two parameters and , H-FCL instances are constructed as follows:

  1. Generate loops on the graph, where is the number of nodes in . Loops are constructed by placing random walkers on random nodes (tails are eliminated once random walkers cross their own path).

  2. For each loop , assign to all the corresponding couplings but one randomly chosen one, for which the value is assigned instead.

  3. The final Hamiltonian is then constructed by adding up all the loop couplings, i.e.,


    The instance is discarded if there is a coupling such that


The parameters and correspond to the density of “constraints” and to the “ruggedness” of the H-FCL problem, respectively.

Although the H-FCL problems can be, in principle, directly generated for the Chimera graph Hen et al. (2015), in a recent paper King et al. (2017), King et al. have chosen a different approach (called here K-FCL) that can be divided into two steps:

  1. All couplings inside a unit cell of the Chimera structure are set to be ferromagnetic, i.e., , . Because the unit cells are fully-connected, all the physical qubits within a single cell are forced to behave as a single virtual qubit. This process generates a two-dimensional lattice with open boundary conditions of these virtual variables. Here, is the number of cells on the Chimera graph with physical variables (qubits) and virtual variables.

  2. The embedded instances are then generated on the virtual lattice with a given and .

These K-FCL problems chi () have considerably fewer (virtual) variables than other benchmarks, but have proven to be computationally difficult for many heuristics, in particular the HFSand PT+ICMsolvers King et al. (2017). We emphasize, however, that the virtual problem is planar and can therefore be solved in polynomial time using minimum-weight-perfect-matching techniques Kolmogorov (2009); Mandrà and Katzgraber (2017). As such, any speedup claims based on these problems have to be taken with a grain of salt.

iii.2 Deceptive cluster loop benchmark problems

Inspired by the K-FCL benchmark problems, we have developed a new class of problems we call deceptive cluster loops. Although the ground state of the problem cannot be planted and therefore has to be computed with other efficient heuristics, we show that while the DW2000Qdevice maintains its performance for this class of problems, all other known heuristics struggle with solving these instances. In addition, the virtual problem cannot be easily decoded, i.e., the problems cannot be solved in polynomial time or with other clever approximations that exploit the logical structure Mandrà et al. (2016).

The structure of the DCL problems can be summarized as follows: Starting from an embedded K-FCL instance, all the inter-cell couplers in a cell are multiplied by a factor , whereas all intra-cell couplers have magnitude .

One of the main feature of the proposed DCL problems that distinguishes them from other FCL-like models King et al. (2017); Albash and Lidar (2017) is the presence of two distinct limits for small and large . For small , i.e., in the limit of weak inter-cell couplings, each unit cell results to be strongly connected and therefore, behaves like a single virtual variable. In particular, when , the DCL problems are equivalent to K-FCL problems. The corresponding Ising model has a two-dimensional planar square lattice as the underlying graph and therefore can be solved in polynomial time Mandrà and Katzgraber (2017). On the other hand, in the limit of large , i.e., in the limit of strong inter-cell couplings, either horizontal or vertical chains that go across different unit cells become strongly coupled. By observing that there always exists a gauge transformation for Chimera graphs such that all the inter-cell couplings can be fixed to be ferromagnetic, it is straightforward to see that the corresponding virtual model for is the virtual fully-connected bipartite model Venturelli et al. (2015). For intermediate values of , the DCL problems become a nontrivial combination of the two limits and therefore, optimal states cannot be mapped onto either virtual models. The effect becomes most pronounced when for the inter-cell couplers is comparable to the connectivity of the intra-cell variables, i.e., for the current D-Wave Chimera architecture, where the local intra-cell environment felt by a variable in the cell competes with the strength of the inter-cell couplers.

From a physical point of view, the DCL problems have another important property which makes them interesting in their own right: By continuously changing the scaling parameter , it is possible to modify the critical spin-glass temperature from () Katzgraber et al. (2014), to () Venturelli et al. (2015). Therefore, it would be interesting to understand the nature of the spin-glass phase for intermediate where the system is neither planar or fully-connected Mandrà and Katzgraber (2018).

Figure 1: Time-to-solution (TTS) for the parallel tempering isoenergetic cluster method (PT+ICM), the Hamze-de Freitas-Selby (HFS) heuristic, as well for the D-Wave 2000Q (DW2000Q) quantum chip. All data points are for fixed scaling while changing the linear problem size . For this analysis, we also used a modified PT+ICMalgorithm (PT+ICM+L) to take advantage of the knowledge of the virtual ground state for both small () and large () scaling limits (see main text for more details). For DW2000Q, PT+ICM and HFS, fits are obtained by considering the last points only (fit parameters are reported in the Appendix). For PT+ICM+L, points are fit with a Bezier curve to guide the eye.
Figure 2: Exponential fit parameters for the time-to-solution (TTS) of the form , for DW2000Q, PT+ICMand HFS. While the computational scaling parameters (top) are not significantly different, DW2000Qis over times faster than PT+ICMand HFS when analyzing the prefactor (bottom) . The linear regression is computed by considering only the last five linear sizes [see Fig. (7)].

Iv Results

In this Section, we compare the DW2000Qquantum chip against two of the fastest classical heuristics for Chimera Hamiltonians, namely the Hamze-de Freitas-Selby (HFS) heuristic and the parallel tempering isoenergetic cluster method (PT+ICM). Both HFSand PT+ICMhave been modified to correctly compute TTS as described in Eq. (3). Moreover, PT+ICMhas been further optimized to exploit the knowledge of the virtual ground states in both limits of small () and large () scaling (referred to as PT+ICM+L). In particular, is computed by running PT+ICMfrom either an initial random state or from one of the two virtual ground states and then taking the minimum value. For each linear size , we generated DCL instances with parameters and (instances at different have been obtained by properly rescaling the inter-cell couplings). In all plots, points represent the median of the distribution while the error bar correspond to the percentiles. If not otherwise indicated, DW2000Qannealing time has been fixed to the minimum allowed, namely . Simulation parameters for the classical heuristics are listed in the Appendix alb ().

Figure (1) summarizes our results where DW2000Qis compared to both HFSand PT+ICM. Interestingly, excluding the region of small where PT+ICM+Lis designed to be the fastest, DW2000Qalways performs better than the two classical heuristics for the considered values of , being approximately times faster for . To better appreciate the different computational scalings among the classical and quantum heuristics we analyzed, Fig. (2), top panels, reports the scaling exponent of an exponential fit of the form:


In the plots, boxes represent the confidence interval for computed using only the percentile of TTS while whisker bars represent the confidence interval for computed using the percentile of the TTS. As one can see, while HFSis statistically indistinguishable from the DW2000Q data, PT+ICMperforms slightly better for large . However, the better performance for PT+ICMfor large can be explained by noticing that PT+ICMhas been optimized for each while both DW2000Qand HFSuse the same setup regardless of the value of . Figure (2, bottom panels, shows that DW2000Qis consistently faster than both HFSand PT+ICMby, on average, a factor of . Unfortunately, we cannot “certify” the DW2000Qcomputational scaling because we are not able to find the optimal annealing time for the allowed minimum annealing time in the device. Still, as shown in Fig. (3), we have strong indication that the computational scaling we have found is reliable because of its stability for a large variation of annealing times one ().

Figure 3: Comparison of the time-to-solution (TTS) for DW2000Qby varying annealing time. Despite the fact that we are not able to identify an optimal annealing time, the last five linear sizes are consistent for a wide range of annealing times.

To further analyze the effects of the rescaling factor in Fig. (4) we show the performance of DW2000Qcompared to PT+ICM, PT+ICM+L, and HFSat fixed linear size of the system . As expected, PT+ICM+Lperforms the best for both small and large , while its performance quickly degenerates for , i.e., in the region where the true ground state is a nontrivial overlap of the virtual ground states at either small or large [see Fig. (6) for the number of “broken” virtual variables for either the virtual planar model or the virtual fully-connected bipartite model]. In contrast, the DW2000Qperformance gradually decreases by increasing the scaling factor (precision issues Venturelli et al. (2015); Katzgraber et al. (2015) may be one of the dominant factor of the loss of performance).

As final remark, it is important to stress that the advantage is not just on the typical instance. Indeed, as it is shown in Fig. (5), DW2000Qperforms best even in an instance by instance comparison, when contrasted to HFSand PT+ICM.

Figure 4: Time-to-solution (TTS) for the parallel tempering isoenergetic cluster method (PT+ICM), the Hamze-de Freitas-Selby (HFS) heuristic, as well for D-Wave 2000Q (DW2000Q) quantum chip. All data points are for fixed linear lattice size . For this analysis, we also used a modified PT+ICMalgorithm (PT+ICM+L) to take advantage of the knowledge of the virtual grounds state for both small () and large () scaling limits (see main text for more details). Data show that HFSis barely affected by the scaling , while the DW2000Qperformance slowly degrades by increasing (most likely due to precision issues Venturelli et al. (2015); Katzgraber et al. (2015)). As expected, PT+ICM+Lperforms the best for both small and large , while its performance quickly degenerates for , i.e., in the region where the true ground state is different from the virtual ground state. Interestingly, for , DW2000Qhas the better performance resulting and is at least times faster than HFSand PT+ICM.
Figure 5: Instance by instance comparison of the Time-to-solution (TTS) between the different heuristics used, the parallel tempering isoenergetic cluster method (PT+ICM) and the Hamze-de Freitas-Selby (HFS) algorithms, and the D-Wave 2000Q (DW2000Q) quantum chip. As one can see, DW2000Qis consistently faster than HFSand PT+ICM.

V Inclusion of Power Consumption

While most of the benchmark studies have largely focused on pure computational speed, the inclusion of power consumption has been largely neglected in the literature pow (a). With ever-growing data centers, power consumption has become an important issue and “greener” computational solutions are highly sought after.

A large-sized data center like the one hosted at NASA Ames top (2016); nas () has a typical energy consumption of approximately MW, with a ratio between power usage and cooling. With more than cores for the current NASA high-performance computing cluster, the typical energy consumption is approximately W/core. In contrast, the energy consumption of the DW2000Qquantum processing unit is approximately pW. Keeping the quantum processing unit cooled to mK requires approximately kW. In our analysis, we found that the DW2000Qdevice was approximately times faster than the used PT+ICMand HFSheuristics. Therefore, to compete against DW2000Q, compute cores are needed running in parallel with a total energy consumption between and kW. Therefore, power consumption is, overall, comparable. However, there is a remarkable difference: The data center uses of the total consumed energy to run the computers, while the DW2000Qdevice requires only of the power to run the quantum processing unit. Therefore, while an improvement of the power usage effectiveness (PUE) Brady et al. (2013); gre () for the classical data center would eventually reduce the total cooling power of , far more efficient cooling alternatives are needed to reduce the quantum PUE (qPUE). It is unclear how the qPUE can be reduced due to the cryogenic requirements for quantum processing units. However, dry dilution refrigerators with more efficient pumping systems might improve this metric pow (b).

Vi Conclusions

In conclusion, we present the first class of tunable benchmark problems – Deceptive Cluster Loops (DCL) – for which the D-Wave quantum chip (DW2000Q) shows an advantage over the currently best classical heuristics, namely the parallel tempering isoenergetic cluster method (PT+ICM) and the Hamze-de Freitas-Selby (HFS) algorithm. The benchmarks are characterized by a control parameter , the scaling factor of the inter-cell couplings, that allows to continuously transform the model from a virtual planar model () to a virtual fully-connected bipartite problem (). While classical heuristics are faster in the small- and large- limit where the logical structure can be exploited, DW2000Qis the fastest in the crossover region , where the DCL problems are neither virtual planar nor virtual fully-connected bipartite. Indeed, while the computational scaling is comparable among classical and quantum heuristics, the DW2000Q device is approximately two orders of magnitude faster than the currently best known heuristics (PT+ICMand HFS) with a comparable scaling. This result represents the first of its kind since the inception of the D-Wave quantum chip.

Vii Acknowledgments

H. G. K. acknowledges support from the National Science Foundation (Grant No. DMR-1151387) and thanks M. Thom for multiple discussions on power consumption of the DW2000Qdevices. He also thanks N. Artner for support. S. M. acknowledges E. G. Rieffel for the careful reading of the manuscript and useful discussion, and the NASA Ames Research Center for support and computational resources. This research is based upon work supported by the Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA), via Interagency Umbrella Agreement IA1-1198. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the ODNI, IARPA, or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon.

Appendix A Number of Broken Virtual Variables in the DCL Model

Our numerical simulations for suggest that the DCL model reduces to the virtual planar model for , while it is a virtual fully-connected bipartite for [see Fig. (4)]. Figure (6) shows the number of “broken” virtual variables for either the virtual planar model or the virtual fully-connected bipartite model. The interesting regime is obtained when , i.e., when the DCL model is neither a virtual planar model nor a virtual fully-connected bipartite model. In this regime, the ground state of the DCL model cannot be found by solving a corresponding virtual problems and therefore, logical structures cannot be exploited as in Refs. King et al. (2017); Mandrà and Katzgraber (2017).

Figure 6: (Left panel) Number of broken virtual variables with respect to the virtual planar model (limit for ). (Right panel) Number of broken virtual variables with respect to the virtual fully-connected bipartite model (limit for ). As one can see, the number of broken variables goes to zero when the scaling approaches the limit of the corresponding virtual model.

Appendix B Simulation parameters

In this Section, we briefly report the main parameters we used for our experiments and numerical simulations.

DCL Random Instances — We randomly generate instances for each system size (). The instances are generated by following the prescription in Ref. King et al. (2017) (with , ) and then properly rescaling the inter-cell couplings by a factor . For consistency, the same instances have been used for all values of . Unlike in Ref. King et al. (2017), we used all available qubits, i.e., some of the unit cells are not complete.

DW2000QParameters — For all experiments, we use the minimum allowed annealing time of and gauges runs, i.e., total readouts. The initialization time and the readout time have not been included in the calculation of the TTS.

PT+ICMParameters — The lowest and highest temperature for parallel tempering have been chosen to be and , respectively, to maximize the performance of PT+ICM. Optimal sweeps for each instance and have been determined by computing the cumulative distribution of the probability to find the ground state ( runs for each instance and ). The overall optimal number of sweeps is then obtained by bootstrapping the optimal number of sweeps for each instance. For all simulations, the number of sweeps has been optimized to minimize the TTS of the percentile. The initialization time and the readout time have been not included in the calculation of the TTS.

PT+ICM+LParameters — The parameters used are the same as for PT+ICM. The computational time to find the ground state of either the virtual planar model of the virtual fully-connected bipartite model has been set to zero (in reality, the computational time to find the ground state of the fully-connected bipartite model is nonnegligible).

HFSParameters — The option -S13, namely “Exhaust maximal tree width 1 subgraphs” with partial random state initialization, has been used. Optimal sweeps for each instance and have been determined by computing the cumulative distribution of the probability to find the ground state ( runs for each instance and ). The overall optimal number of sweeps is then obtained by bootstrapping the optimal number of sweeps for each instance. For all the simulations, the number of sweeps has been optimized to minimize the TTS of the percentile. The initialization time and the readout time have been not included in the calculation of the TTS.

Exponential Fits — For all the linear regressions, only the last system sizes () have been used. fits for the percentile have been obtained using a linear least squares model. fits for the confidence have been computed by randomly extract values in the confidence interval, one for each size, and then bootstrapping the linear regression data. Figure (7) shows the fits for different values of .

Figure 7: Linear regressions by varying the linear system size , at different inter-cell couplings scaling .


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