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Anti-crossings and spectral gap during quantum adiabatic evolution

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Abstract

We aim to give more insights into adiabatic evolution concerning the occurrence of anti-crossings and their link to the spectral minimum gap \(\Delta _{\min }\). We study in detail adiabatic quantum computation applied to a specific combinatorial problem called weighted max k-clique. A clear intuition of the parametrization introduced by V. Choi is given which explains why the characterization is not general enough. We show that the instantaneous vectors involved in the anti-crossing vary brutally through it making the instantaneous ground-state hard to follow during the evolution. This result leads to a relaxation of the parametrization to be more general.

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References

  1. Albash, T., Lidar, D.A.: Adiabatic quantum computation. Rev. Mod. Phys. 90, 015002 (2018). https://doi.org/10.1103/RevModPhys.90.015002

    Article  ADS  MathSciNet  Google Scholar 

  2. Altshuler, B., Krovi, H., Roland, J.: Adiabatic quantum optimization fails for random instances of np-complete problems (2009)

  3. Childs, A.M., Farhi, E., Goldstone, J., Gutmann, S.: Finding cliques by quantum adiabatic evolution. Quantum Inf. Comput. 2(3), 181–191 (2002)

    MathSciNet  MATH  Google Scholar 

  4. Choi, V.: The effects of the problem Hamiltonian parameters on the minimum spectral gap in adiabatic quantum optimization. Quantum Inf. Process. 19(3), 90 (2020). https://doi.org/10.1007/s11128-020-2582-1

    Article  ADS  MathSciNet  Google Scholar 

  5. Crosson, E., Farhi, E., Lin, C.Y.Y., Lin, H.H., Shor, P.: Different strategies for optimization using the quantum adiabatic algorithm (2014)

  6. Farhi, E., Goldstone, J., Gutmann, S., Sipser, M.: Quantum computation by adiabatic evolution (2000)

  7. Laumann, C.R., Moessner, R., Scardicchio, A., Sondhi, S.L.: Quantum annealing: the fastest route to quantum computation? Eur. Phys. J. Spec. Top. 224(1), 75–88 (2015). https://doi.org/10.1140/epjst/e2015-02344-2

    Article  Google Scholar 

  8. Lucas, A.: Ising formulations of many NP problems. Front. Phys. 2, 5 (2014). https://doi.org/10.3389/fphy.2014.00005

    Article  Google Scholar 

  9. Sun, J., Lu, S.: On the quantum adiabatic evolution with the most general system Hamiltonian. Quantum Inf. Process. 18(7), 1–11 (2019). https://doi.org/10.1007/s11128-019-2313-7

    Article  ADS  MathSciNet  Google Scholar 

  10. Wilkinson, M.: Statistics of multiple avoided crossings. J. Phys. A Math. Gen. 22(14), 2795–2805 (1989). https://doi.org/10.1088/0305-4470/22/14/026

    Article  ADS  Google Scholar 

Download references

Acknowledgements

This work was supported in part by the French National Research Agency (ANR) under the research project SoftQPRO ANR-17-CE25-0009-02 and by the DGE of the French Ministry of Industry under the research project PIAGDN/QuantEx P163746-484124. This work has also received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 817482 (PASQuanS).

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A Appendices

A Appendices

To illustrate the intuition on the \(a_k\)’s, this graph (Fig. 9) is the same structure of the graph (Fig. 2) where nodes 1 and 3 are swapped as well as nodes 5 and 6. We keep the same weights vector \(w=[1,1,1,1.5,1.5,1.5]\).

Fig. 9
figure 9

Toy example 2

For \(\alpha =0.2\), this produces the following final states according to their energy (Fig. 10a) and the eigenvalues evolution (Fig. 10b):

Fig. 10
figure 10

States a with their energy and \(E_i(s)\) b during evolution for toy example 9 with \(\alpha =0.2\)

With this instance, we observe that the final slope of \(E_0(s)\) comes from the slope of \(E_2(s)\); therefore, we expect that \(g_2(s)\) becomes dominant before transmitting to \(g_1(s)\) after the first anti-crossing between \(E_2\) and \(E_1\). Eventually, the anti-crossing of \(E_1\) and \(E_0\) will produce a crossing of \(g_1\) and \(g_0\). The latter will become dominant till being equal to 1 at the end. Now, focusing on the slope of \(E_0(s)\) before the anti-crossing, following the different successive anti-crossings, the jumps end up in the 4th energy level. In terms of \(a_k\)’s and \(b_k\)’s, this means that \(a_3(s)\) becomes important just before the anti-crossing and crosses \(a_0(s)\) at the anti-crossing. The same goes for \(b_3(s)\) and \(b_0(s)\). The plots below support these previous analyses.

Fig. 11
figure 11

\(a_k\) (a), \(b_k\) (b) and \(g_k\) (c) during evolution for toy Example 9 with \(\alpha =0.2\)

Remarks:

  1. 1.

    In Fig. 11a, we only see one specific situation, namely the crossing of \(a_3\) with \(a_0\) at the anti-crossing point \(s^*\) between \(E_0\) and \(E_1\). This means that the curve \(E_0(s)\) was going toward the 4th energy level in terms of the slope before \(s^*\) and immediately change its slope toward its final direction of the 1st energy level. Hypothetically, we could observe \(a_3\) crossing \(a_1\), which will indicate that \(E_0(s)\) change its direction toward the 2nd energy level, then \(a_1\) crossing \(a_0\). In this hypothetical case, \(E_0(s)\) undergoes two anti-crossings.

  2. 2.

    In Fig. 11b, we focus on the behavior of \(|E_1(s)\rangle \). Here, we understand that \(E_1(s)\) first went toward the 3rd energy level before changing direction toward the lowest energy level. This is \(b_2\) crossing \(b_0\) when the first anti-crossing between \(E_1\) and \(E_2\) occurs. Then, it takes the direction of the 4th energy level when \(b_0\) crosses \(b_3\). Indeed, it fetches the direction of \(E_0\) before anti-crossing (remember it was \(a_3\) which was dominant at this point). Then, again it takes back its initial direction with \(b_2\) becoming dominant at the second anti-crossing between \(E_1\) and \(E_2\). Eventually, it smoothly changes its direction toward its final goal.

  3. 3.

    In Fig. 11c, the point of view is quite different as we look from the final lowest energy position \(E_0(1)\) and see from where it comes. We see in Fig. 10b that the final blue slope undergoes two anti-crossing before becoming blue. Indeed, it starts green and then jumps to red and finally blue. These successive anti-crossings appear on the plot of \(g_k\); first, \(g_2\) is dominant, and then at the first anti-crossing between \(E_2\) and \(E_1\), \(g_2\) crosses with \(g_1\). Now, the final slope of \(E_0\) is transported by \(E_1\). Eventually, \(E_1\) anti-crosses \(E_0\) so \(g_1\) crosses \(g_0\) and the evolution (at least for the ground-state) can finish peacefully.

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Braida, A., Martiel, S. Anti-crossings and spectral gap during quantum adiabatic evolution. Quantum Inf Process 20, 260 (2021). https://doi.org/10.1007/s11128-021-03198-7

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