Efforts to develop useful quantum computers have been blocked primarily by environmental noise. Quantum annealing is a scheme of quantum computation that is predicted to be more robust against noise, because despite the thermal environment mixing the system’s state in the energy basis, the system partially retains coherence in the computational basis, and hence is able to establish well-defined eigenstates. Here we examine the environment’s effect on quantum annealing using 16 qubits of a superconducting quantum processor. For a problem instance with an isolated small-gap anticrossing between the lowest two energy levels, we experimentally demonstrate that, even with annealing times eight orders of magnitude longer than the predicted single-qubit decoherence time, the probabilities of performing a successful computation are similar to those expected for a fully coherent system. Moreover, for the problem studied, we show that quantum annealing can take advantage of a thermal environment to achieve a speedup factor of up to 1,000 over a closed system.
Quantum computation1 is a computational paradigm that harnesses quantum physics to solve problems. Theory has suggested that quantum computation could provide a significant performance advantage over classical computation. However, efforts to develop useful quantum computers have been blocked primarily due to environmental noise. Adiabatic quantum computation (AQC)2,3 is a scheme of quantum computation that is theoretically predicted to be more robust against noise4,5,6,7,8,9,10,11,12.
In AQC, a physical quantum system is initially prepared in its known lowest-energy configuration, or ground state. The computation involves gradually deforming the system’s energy function, or Hamiltonian, in such a way that the system remains in the ground state throughout the evolution with high probability, and the ground state of the final Hamiltonian provides the solution to the problem to be solved. Quantum annealing (QA)13,14,15,16 is a very similar, but more practical, scheme of quantum computation that is different from AQC in two aspects: the evolution is not required to be adiabatic, that is, the system may leave the ground state due to thermal or non-adiabatic transitions; and the final Hamiltonian is restricted to be diagonal in the computation basis, with a ground state representing the solution to a hard optimization problem. Such optimization problems are integral to a wide range of applications from anthropology17 to zoology18.
QA has been experimentally demonstrated in non-programmable bulk solid state systems19,20 and also using 3-qubit nuclear magnetic resonance21. Recently, we have designed, fabricated and tested a programmable superconducting QA processor22,23. Here we report the first experimental exploration of the effect of thermal noise on QA. We explored this using a 16-qubit subset of the processor used in Johnson et al.23
Our processor implements the Hamiltonian
where s≡t/tf∈[0, 1], t is time, tf is the anneal time, are Pauli matrices for the ith qubit, and Δi(s) and Ε(s) are time-dependent energy scales. At t=0, Δi(0)>>Ε(0)≈0, and the ground state is a superposition of all computation basis states, that is, all eigenfunctions of operators. At t=tf, Ε(1)>>Δi(1)≈0, and H(s) is dominated by the Ising Hamiltonian , characterized by dimensionless local biases hi and pairwise couplings Jij. The ground state of H(1) thus represents the global minimum of . Finding this global minimum is known to be an NP-hard (nondeterministic polynomial time hard) optimization problem24.
An important feature of optimization problems is that, although it may be challenging to find a global minimum, it is easy to select the minimum from a set of candidate solutions, for example, provided by a probabilistic algorithm such as QA. If one is able to find a global minimum with probability PGM>0 in one trial taking time tf, the probability Ptotal of observing a global minimum at least once in k-independent trials, and the total time required ttotal, is given by
For a noise-free (closed) system, PGM would depend on the minimum energy gap, gmin, between the ground and first excited states, encountered at some point s=s* known as an anticrossing (illustrated in Fig. 1). Using a two-state approximation, one can write25,26,9:
where ν=|d(E1−E0)/ds|s* is the relative slope of the energy levels near s* and ta is the adiabatic timescale, which marks the boundary between adiabatic (tf>>ta) and non-adiabatic (tf<<ta) evolutions. Annealing such a closed system k times, with tf=ttotal/k, yields the same Ptotal as annealing once with tf=ttotal.
In reality, all implementations of quantum algorithms are open systems, and thus subject to relaxation (transitions between energy eigenstates) and dephasing (randomization of relative phases between the eigenstates). In the limit of weak coupling to the environment, dephasing in the energy basis is irrelevant for QA, because only the ground-state probability matters and the relative phases between the energy eigenstates do not carry any information during the computation. Thermal excitation and relaxation, on the other hand, can reduce the instantaneous probability of the ground state by populating the excited states. Nevertheless, in the limit of slow evolution, the ground state will always have the dominant probability, because the excited states are occupied approximately with equilibrium (Boltzmann) probabilities. Unless there are an exponential number of states within the energy kBT (kB is Boltzmann’s constant) from the ground state, the thermal reduction of the ground-state probability will not significantly affect the performance. This picture changes in the strong coupling limit wherein it may no longer be possible to identify well-defined eigenstates of the system Hamiltonian independent of the environment27. It should be noted that, even if the environment is weakly coupled to the system and the equilibrium probability of the ground state is not vanishingly small, the time to reach such a probability is a concern for practical computation. For example, it is conceivable that, due to thermal relaxation, one must wait orders of magnitude longer than a typical closed-system evolution time to reach an acceptable open-system probability. In that case, despite the above argument, the computation cannot be considered robust against the environment. We therefore define robustness against environmental noise as the ability of an open quantum annealing system to yield the correct solution with acceptable probability within a time comparable to the closed-system adiabatic timescale.
To experimentally investigate the effects of noise on performance, we design an instance of that has an anticrossing with a small gmin between an eigenstate , which is a superposition of 256 equal energy (degenerate) local minima of , and an eigenstate , which corresponds to the unique (nondegenerate) global minimum. By studying a small-gap problem, we are addressing what are expected to be the most difficult problems for closed-system AQC (although for an open system, problems with exponentially many low-energy excited states may represent the hardest problems). Moreover, we are interested in exploring evolution during which the minimum gap is passed non-adiabatically, and investigating dependence on annealing time and temperature. To experimentally violate adiabaticity, we require gmin/kB<<1 mK. For 16-qubit problems, such small gaps are quite uncommon, and it can be challenging to engineer an instance with sufficiently small gmin. Our designed instance is illustrated in Fig. 2a and further described in the Methods section and Supplementary Note 1. The same type of anticrossing has been argued28,29 to render QA ineffective because of the extremely small gmin, though methods have been proposed to eliminate such anticrossings30,31,32.
Of key importance are the energy scales Δi(s) and Ε(s) in equation (1), which can be calculated from independently calibrated device parameters (see, for example, Harris et al.33 and Johansson et al.34). Results for the 16 qubits used in this study are plotted in Fig. 2b. Using these quantities, we calculate the eigenspectrum of H(s). Features relevant to this work are found in a narrow region around s≈0.64, where the anticrossing is expected, as shown in Fig. 2c. The minimum gap, gmin/kB=0.011 mK, is more than three orders of magnitude smaller than T (≳20 mK). The global minimum (green) of is the dominant component of the ground state after the anticrossing, but dominates the first excited state before it. The opposite is true for (blue). Our first objective is to experimentally verify that there is an anticrossing at the predicted position.
In the limit of infinitely slow evolution, the instantaneous probability of occupying each eigenstate is approximately given by the Boltzmann distribution. Therefore, the probability PGM(s) of occupying should be small before the anticrossing but large after the anticrossing. The opposite should hold for the probability PΣ(s) of occupying . The two probabilities should coincide at the anticrossing, where PGM(s*)=PΣ(s*)≈0.5. Measuring the instantaneous probabilities PGM(s) and PΣ(s) would therefore provide information about the approximate position of the anticrossing.
We measure the instantaneous probabilities PGM(s) and PΣ(s) by annealing the system slowly, with tf=100 ms, but interrupting it at s=sd by rapidly moving to s=1 within 20 μs. This rapid evolution takes approximate snapshots of PGM(s=sd) and PΣ(s=sd). PΣ is determined by summing over the probabilities of observing all 256 local minima of at the end of the evolution. Such a measurement gives a representation of the equilibrium distribution up to some s, beyond which thermal relaxation timescales become exceedingly long (see Supplementary Fig. S5 and Supplementary Note 3 for more details).
Figure 2d shows measurements of PGM(s) and PΣ(s). As expected, PGM≈1−PΣ≈0 before the anticrossing. The total probability of states other than the global minimum and the 256 local minima is <0.1%. The two data sets cross near the theoretically predicted s*, where PGM=PΣ≈0.5. For s>s*, PGM≈1−PΣ becomes large. As the tunnelling amplitudes Δi(s) are reduced towards the end of annealing, the relaxation between the energy levels becomes slower and slower, and finally the probabilities freeze due to extremely slow relaxation23. The ground-state probability at the freeze-out point, therefore, determines the final success probability. As a result, the probabilities saturate for s≳0.66 because of the diminishing relaxation rates between and . (The saturation point and value depend on tf; see Supplementary Fig. S5 and Supplementary Note 3.)
To examine the impact of noise on QA, we consider the effects of varying T and tf. In the case where tf<<ta, passing through the small-gap anticrossing quickly will approximately swap the probabilities of the two crossing states, as depicted in Fig. 1. For T=0, this would give vanishingly small PGM. Because dominates the excited state just before the anticrossing, increasing T is expected to increase PGM. As T approaches δE/kB, the energy separation between and , and higher eigenstates, those latter states will be populated, thus reducing PGM. Therefore, a peak in PGM as a function of T is expected at Tpeak≲δE/kB.
Figure 3 shows experimental measurements of the final success probability PGM as a function of T for different tf. All curves, except for those with tf ≥200 ms, show an initial increase with T up to a maximum. For T≳40 mK, the benefits of thermal noise diminish as the system becomes thermally excited to the eigenstates that are at energy δE/kB∼50 mK above the lowest two states, depicted in Fig. 2c.
It should be emphasized that the enhancement with temperature observed in this experiment is a result of having a small-gap anticrossing separated from all other states by a large energy gap. In more general cases, when several excited states have comparable energy gaps with respect to the ground state with no small-gap anticrossings, increasing T would decrease the probability of success, as observed in Fig. 3 for T>40 mK.
The degradation of PGM for high temperatures is instructive, but there is something important to be learned from the low-temperature limit as well. An open system with T=0 has been theoretically predicted to behave similarly to a closed system for such an anticrossing10,35,36,34. In this experiment, it was infeasible to reduce T below ∼20 mK. Instead, to obtain a crude estimate of PGM at T=0, we extrapolate the curves in Fig. 3 (blue squares). In Fig. 4a, the data shown in Fig. 3 are plotted as a function of tf for different T. We fit (blue dashed line) the formula for closed-system probability, equation (4), to the extrapolated points (blue squares) using ta as the fitting parameter, giving ta=57.2 ms. The fact that the extrapolated points (T=0) could be fit with equation (4) supports the prediction above. All T>0 curves fit very poorly to this equation.
Despite the influence of thermal noise, it can be seen in Fig. 4a that 0.45≲PGM≲0.8 at tf=ta for all T>0 studied. These probabilities are comparable to PGM=0.63 expected for the closed system. This is because the timescale to reach equal thermal occupation of two anticrossing states is determined by a relaxation time proportional to gmin−2 (refs 9, 10), similar to ta in equation (4). It is important to note that for a single, unbiased qubit near s*, we estimate a decoherence time that is millions of times shorter than ta (see Supplementary Note 2). The fact that PGM similar to that of a closed system can be reached in time ta, despite the significantly shorter decoherence time, supports theoretical predictions that QA can be performed in the presence of small environmental noise4,5,6,7,8,9,10,11,12.
We can also extract the value of gmin for this instance based on the value of ta found from the fitting above. In equation (4), ν is only weakly dependent on Hamiltonian parameters, whereas gmin is exponentially sensitive37. We therefore use the value of ν/kB≈5.3 K, obtained from the computed spectrum in Fig. 2c, to calculate gmin based on the above ta. The result, gmin/kB=0.021 mK, is about twice as large as that predicted in Fig. 2c. This is within the expected uncertainty, considering the exponential sensitivity of gmin (see Supplementary Note 1).
Given the values of PGM in Fig. 4a, one can also compare the relative performance of repeated annealing of the QA processor to that of an ideal closed system. Figure 4b shows the total time ttotal required to achieve Ptotal=0.99 by repeated annealings, as a function of tf, calculated using equation (3). This calculation ignores the overhead for processor preparation and readout, which do not reflect underlying physics. As noted earlier, for the closed system (blue squares and dashed line), ttotal is independent of tf. For T>0, ttotal decreases with decreasing tf, and can be almost three orders of magnitude smaller than that expected for the closed system with tf=0.01 ms. Clearly, in the case of a small-gap anticrossing, such as the one studied here, annealing an open system fast multiple times can have a significant performance advantage over annealing slowly once or annealing a closed system, as predicted11. Such an efficiency enhancement due to coupling to the environment has been predicted to have an important role in nature, for example, in photosynthetic quantum energy transfer38.
In summary, we have found experimental evidence that for a 16-qubit instance with an avoided crossing having an extremely small gap in energy, QA can be robust against thermal noise, well beyond the decoherence time, in line with theoretical predictions. Using a QA processor comprising superconducting flux qubits, we show that the presence of a small amount of thermal noise does not hinder QA in the studied example, and can, in fact, significantly enhance its performance. The thermal enhancement of the performance is restricted to instances with a small-gap anticrossing well separated from other excited states. For general problems with many energy levels thermally occupied during the evolution, increasing T is expected to decrease the final success probability because energy levels within kBT of the ground state may become thermally occupied during the annealing. However, unless the number of these nearby levels increases exponentially with the number of problem variables, the corresponding reduction in ground-state probability can be compensated by repeating the annealing a number of times. These results suggest that QA offers a practical and promising way to perform quantum optimization.
The devices studied in this experiment are from a QA processor made of 128 superconducting flux qubits, all controlled by on-chip Single Flux Quantum superconducting digital circuitry as well as filtered analog signal input lines. The qubits are briefly discussed in this section and in detail in Harris et al.33 The on-chip control circuitry is described in detail in Johnson et al.39 The qubits are numbered 0–127 starting from the top-left corner of the chip, and the 16-qubit subset used in this experiment is depicted in Supplementary Fig. S2. Supplementary Fig. S2b represents the qubits as line segments, intersecting at couplers, reflecting the qubit’s physical layout as long, thin radio frequency Superconducting Quantum Interference Device (rf-SQUID) loops. Some of these qubits have been used in previously published experiments. For example, qubit 48 was used in a single-qubit experiment40, qubits 48–55 were used in two-qubit experiments41, as well as the 8-qubit experiments discussed in Harris et al.22 and Johnson et al.23 The annealing procedure we use here is the same as that described in Harris et al.22
The rf-SQUID flux qubit
The qubits used in this experiment are superconducting compound Josephson junction rf-SQUID flux qubits described in detail in Harris et al.33 A simplified version of the qubit is illustrated in Supplementary Fig. S3. It has two superconducting loops and therefore two flux degrees of freedom Φ1 and Φ2, subject to external flux biases Φ1x and Φ2x, respectively. At Φ1x≈Φ0/2, the rf-SQUID has two bistable states with persistent current flowing clockwise or counterclockwise through the large loop. These two states form the qubit’s logical ‘0’ and ‘1’ states. The value of the persistent current (Ip) and the tunnelling amplitude (Δi) between the two bistable states are controlled by Φ2x. The energy bias between the two states is controlled by Φ1x. Supplementary Fig. S4 plots the measured values of Ip and Δi as a function of s for all 16 qubits. Measurement details are provided in Harris et al.33 The qubits are calibrated to have approximately the same persistent currents, but Δi is not uniform among the qubits, as is clear from the figure. The overall energy scale E(s) in equation (1) is related to the persistent current through , where M0 is a characteristic mutual inductance determined by the qubit–qubit couplers. Annealing is accomplished by linearly changing Φ2x from −0.59Φ0 at t=0 to −0.65Φ0 at t=tf. To have a uniform Hamiltonian throughout the evolution, other time-dependent biases are also applied as detailed in Harris et al.22
The problem instance
The instance, shown in Fig. 2a, is designed to have a small energy gap anticrossing, using a phenomenon similar to that presented in Dickson32. The eight central qubits in the figure, if unbiased, would have as ground states the all-down and all-up ferromagnetically ordered states. Additional qubits are attached in such a way as to cause 255 more states, close to the all-up state and far from the all-down state, to become degenerate with these states. Then, biases on the central qubits are adjusted to raise the energy of the 256 nearby states, relative to the all-down state, . Repulsion of the 256 degenerate states due to tunnelling between them, away from the end of the evolution, causes the uniform superposition of them, , to be lower in energy than for large enough Δi(s). Because there is a small amount of tunnelling between and , they anticross with a small gap, while being separated from higher energy states by about Δi(s*), as shown in Fig. 2c. Additional details can be found in Supplementary Fig. S1 and Supplementary Note 1.
How to cite this article: Dickson, N. G. et al. Thermally assisted quantum annealing of a 16-qubit problem. Nat. Commun. 4:1903 doi: 10.1038/ncomms2920 (2013).
Feynman, R. P. . Simulating physics with computers. Int. J. Theo. Phys. 21, 467–488 (1982) .
Farhi, E. et al. A quantum adiabatic evolution algorithm applied to random instances of an NP-complete problem. Science 292, 472–475 (2001) .
Aharonov, D. et al. Adiabatic quantum computation is equivalent to standard quantum computation. SIAM Rev. 50, 755–787 (2008) .
Childs, A. M., Farhi, E. & Preskill, J. . Robustness of adiabatic quantum computation. Phys. Rev. A 65, 012322 (2001) .
Roland, J. & Cerf, N. J. . Noise resistance of adiabatic quantum computation using random matrix theory. Phys. Rev. A 71, 032330 (2005) .
Ashhab, S., Johansson, J. R. & Nori, F. . Decoherence in a scalable adiabatic quantum computer. Phys. Rev. A 74, 052330 (2006) .
Sarandy, M. S. & Lidar, D. A. . Adiabatic quantum computation in open systems. Phys. Rev. Lett. 95, 250503 (2005) .
Tiersch, M. & Schützhold, R. . Non-Markovian decoherence in the adiabatic quantum search algorithm. Phys. Rev. A 75, 062313 (2007) .
Amin, M. H. S., Love, P. J. & Truncik, C. J. S. . Themally assisted adiabatic quantum computation. Phys. Rev. Lett. 100, 060503 (2008) .
Amin, M. H. S., Averin, D. V. & Nesteroff, J. A. . Decoherence in adiabatic quantum computation. Phys. Rev. A 79, 022107 (2009) .
Amin, M. H. S., Truncik, C. J. S. & Averin, D. V. . Role of single qubit decoherence time in adiabatic quantum computation. Phys. Rev. A 80, 022303 (2009) .
Lloyd, S. . Robustness of Adiabatic Quantum Computing. Preprint at http://arXiv.org/abs/0805.2757 (2008) .
Finnila, A. B., Gomez, M. A., Sebenik, C., Stenson, C. & Doll, J. D. . Quantum annealing: a new method for minimizing multidimensional functions. Chem. Phys. Lett. 219, 343–348 (1994) .
Kadowaki, T. & Nishimori, H. . Quantum annealing in the transverse Ising model. Phys. Rev. E 58, 5355–5363 (1998) .
Hogg, T. . Quantum search heuristics. Phys. Rev. A 61, 052311 (2000) .
Santoro, G. E., Martonak, R., Tosatti, E. & Car, R. . Theory of quantum annealing of an Ising spin glass. Science 295, 2427–2430 (2002) .
Boorman, S. A. A. . Combinatiorial optimization model for transmission of job information through contact networks. Bell J. Econ. 6, 216–249 (1975) .
Chen, X. & Tompa, M. . Comparative assessment of methods for aligning multiple genome sequences. Nat. Biotechnol. 28, 567–572 (2010) .
Brooke, J., Bitko, D., Rosenbaum, T. F. & Aeppli, G. . Quantum annealing of a disordered magnet. Science 284, 779–781 (1999) .
Brooke, J., Rosenbaum, T. F. & Aeppli, G. . Tunable quantum tunnelling of magnetic domain walls. Nature 413, 610–613 (2001) .
Steffen, M., van Dam, W., Hogg, T., Breyta, G. & Chuang, I. . Experimental implementation of an adiabatic quantum optimization algorithm. Phys. Rev. Lett. 90, 067903 (2003) .
Harris, R. et al. Experimental investigation of an eight qubit unit cell in a superconducting optimization processor. Phys. Rev. B 82, 024511 (2010) .
Johnson, M. W. et al. Quantum annealing with manufactured spins. Nature 473, 194–198 (2011) .
Barahona, F. . On the computational complexity of Ising spin glass models. J. Phys. A: Math. Gen. 15, 3241–3253 (1982) .
Landau, L. D. . Zur theorie der energieubertragung. Phys. Z. Sowjetunion 2, 46–51 (1932) .
Zener, C. . Non-adiabatic crossing of energy levels. Proc. R. Soc. Lond. Ser. A 137, 696–702 (1932) .
Deng, Q., Averin, D. V., Amin, M. H. & Smith, P. . Decoherence induced deformation of the ground state in adiabatic quantum computation. Sci. Rep. 3, 1479 (2013) .
Altshuler, B., Krovi, H. & Roland, J. . Anderson localization casts clouds over adiabatic quantum optimization. Proc. Natl Acad. Sci. USA 107, 12446–12450 (2010) .
Young, A. P., Knysh, S. & Smelyanskiy, V. N. . First-order phase transition in the quantum adiabatic algorithm. Phys. Rev. Lett. 104, 020502 (2010) .
Farhi, E. et al. Quantum adiabatic algorithms, small gaps, and different paths. Quant. Info. Comp. 11, 181–214 (2011) .
Dickson, N. G. & Amin, M. H. S. . Does adiabatic quantum optimization fail for NP-complete problems? Phys. Rev. Lett. 106, 050502 (2011) .
Dickson, N. . Elimination of perturbative crossings in adiabatic quantum optimization. New J. Phys. 13, 073011 (2011) .
Harris, R. et al. Experimental demonstration of a robust and scalable flux qubit. Phys. Rev. B 81, 134510 (2010) .
Johansson, J. et al. Landau-Zener transitions in a superconducting flux qubit. Phys. Rev. B 80, 012507 (2009) .
Ao, P. & Rammer, J. . Quantum dynamics of a two-state system in a dissipative environment. Phys. Rev. B 43, 5397–5418 (1991) .
Wubs, M., Saito, K., Kohler, S., Hänggi, P. & Kayanuma, Y. . Gauging a quantum heat bath with dissipative landau-zener transitions. Phys. Rev. Lett. 97, 200404 (2006) .
Amin, M. H. S. & Choi, V. . First-order quantum phase transition in adiabatic quantum computation. Phys. Rev. A 80, 062326 (2009) .
Mohseni, M., Rebentrost, P., Lloyd, S. & Aspuru-Guzik, A. . Environment-assisted quantum walks in photosynthetic energy transfer. J. Chem. Phys. 129, 174106 (2008) .
Johnson, M. W. et al. A scalable control system for a superconducting adiabatic quantum optimization processor. Supercond. Sci. Technol. 23, 065004 (2010) .
Lanting, T. et al. Probing high frequency noise with macroscopic resonant tunnelling. Phys. Rev. B 83, 180502(R) (2011) .
Lanting, T. et al. Cotunneling in pairs of coupled flux qubits. Phys. Rev. B 82, 060512(R) (2010) .
We thank I. Affleck, D. V. Averin, H. G. Katzgraber, D. A. Lidar, R. Raussendorf and A. Yu. Smirnov for useful discussions.
The authors declare no competing financial interests.
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Dickson, N., Johnson, M., Amin, M. et al. Thermally assisted quantum annealing of a 16-qubit problem. Nat Commun 4, 1903 (2013). https://doi.org/10.1038/ncomms2920
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