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arXiv:1806.08517 · Journal of the Physical Society of Japan

Pulsed Quantum Annealing

Vasilios D. Karanikolas, Shiro Kawabata

Published Aug 17, 2020• 7 citations
arXivHTMLPDF

Abstract

We propose a modified quantum annealing protocol, i. e., pulsed quantum annealing} (PQA), in order to increase the success probability by a pulse application during the quantum annealing process. It is well known that the success probability of the conventional quantum annealing is reduced due to the Landau-Zener transitions. By applying a pulse to the system, we modulate the success probability and increase it, compared to the conventional quantum annealing, by optimizing the pulse parameters. We demonstrate our findings for a single qubit both numerically and analytically. The analytical model is based on the tranfer matrix approach and it is in good agreement with the full numerical results. We also investigate the PQA protocol for multi-qubit cases $i. e.,$ random spin-glass instances, and we present an overall increase of the success probability over the conventional quantum annealing, by optimizing the pulse parameters. Our results indicate that PQA can be used to design future high-performance quantum annealing machines, especially for hard instances that the conventional QA protocol behaves poorly.

DOI
10.7566/jpsj.89.094003

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We propose a modified quantum annealing protocol, i. e., pulsed quantum annealing} (PQA), in order to increase the success probability by a pulse application during the quantum annealing process. It is well known that the success probability of the conventional quantum annealing is reduced due to the Landau-Zener transitions. By applying a pulse to the system, we modulate the success probability and increase it, compared to the conventional quantum annealing, by optimizing the pulse parameters. We demonstrate our findings for a single qubit both numerically and analytically. The analytical model is based on the tranfer matrix approach and it is in good agreement with the full numerical results. We also investigate the PQA protocol for multi-qubit cases i.e.,i. e.,i.e., random spin-glass instances, and we present an overall increase of the success probability over the conventional quantum annealing, by optimizing the pulse parameters. Our results indicate that PQA can be used to design future high-performance quantum annealing machines, especially for hard instances that the conventional QA protocol behaves poorly.
Quantum annealing is an innovative idea and method for avoiding the increase of the calculation cost of the combinatorial optimization problem. Since the combinatorial optimization problems are ubiquitous, quantum annealing machine with high efficiency and scalability will give an immeasurable impact on many fields. However, the conventional quantum annealing machine may not have a high success probability for finding the solution because the energy gap closes exponentially as a function of the system size. To propose an idea for finding high success probability is one of the most important issues. Here we show that a degenerate two-level system provides the higher success probability than the conventional spin-1/2 model in a weak longitudinal magnetic field region. The physics behind this is that the quantum annealing in this model can be reduced into that in the spin-1/2 model, where the effective longitudinal magnetic field may open the energy gap, which suppresses the Landau-Zener tunneling providing leakage of the ground state. We also present the success probability of the Λ-type system, which may show the higher success probability than the conventional spin-1/2 model.
Quantum annealing (QA) is a method for solving combinatorial optimization problems. We can estimate the computational time for QA using what is referred to as the adiabatic condition derived from the adiabatic theorem. The adiabatic condition consists of two parts: an energy gap and a transition matrix. Most past studies have focused on the relationship between the energy gap and computational time. The success probability of QA is considered to decrease exponentially owing to the exponentially decreasing energy gap at the first-order phase-transition point. In this study, through a detailed analysis of the relationship between the energy gap, transition matrix, and computational cost during QA, we propose a general method for constructing counterintuitive models wherein QA with a constant annealing time fails despite a constant energy gap, based on polynomial energy. We assume that the energy of the total Hamiltonian is at most $\mathrm{\ensuremath{\Theta}}(L)$, where LLL is the number of qubits. In our formalism, we choose a known model that exhibits an exponentially small energy gap during QA, and modify the model by adding a specific penalty term to the Hamiltonian. In the modified model, the transition matrix in the adiabatic condition becomes exponentially large as the number of qubits increases, while the energy gap remains constant. Moreover, we achieve a quadratic speedup, for which the upper bound for improvement in the adiabatic condition is determined by the polynomial energy. For concrete examples, we consider the adiabatic Grover search and the ferromagnetic ppp-spin model. In these cases, with the addition of the penalty term, although the success probability of QA on the modified models becomes exponentially small despite a constant energy gap, we are able to achieve a success probability considerably higher than that of conventional QA. Moreover, in concrete examples, we numerically show the scaling of the computational cost is quadratically improved compared to the conventional QA. Our findings pave the way for a better understanding of QA performance.
Quantum annealing (QA) is a promising quantum computing paradigm for solving optimization problems. However, the performance of QA is often limited by issues such as slow convergence rates and sensitivity to parameter settings. This paper presents a novel optimization method designed to address these limitations. The approach introduces refined parameters and an adaptive strategy to enhance the algorithm's convergence speed and solution accuracy. We explore the application of this optimized QA method to complex problems, demonstrating its potential for improved performance. The core of the optimization lies in modifying the annealing schedule and incorporating a dynamic adjustment of the magnetic field strength, denoted as H(t)H(t)H(t), where ttt represents time. The objective function to be minimized is typically represented as E[s]E[s]E[s], where sss is the state vector of the quantum annealer. We introduce a variance reduction technique, denoted as σ(t)\sigma(t)σ(t), which is dynamically adjusted based on the error estimates of the current state. The overall optimization process can be summarized as: min⁡sE[s]subject todSdt=−β(t)E[s]−γ(t)H(t)s\min_{s} E[s] \quad \text{subject to} \quad \frac{dS}{dt} = - \beta(t) E[s] - \gamma(t) H(t)smins​E[s]subject todtdS​=−β(t)E[s]−γ(t)H(t)s where β(t)\beta(t)β(t) is the annealing rate and γ(t)\gamma(t)γ(t) is the magnetic field strength. The adaptive strategy involves updating β(t)\beta(t)β(t) and γ(t)\gamma(t)γ(t) based on the error in the current solution, aiming to reduce the variance σ(t)\sigma(t)σ(t) and improve the convergence. The theoretical analysis suggests that this approach can significantly reduce the computational cost and improve the solution quality compared to traditional QA methods. Furthermore, we investigate the impact of different parameter settings on the algorithm's performance, highlighting the importance of careful tuning for optimal results. The results demonstrate a notable improvement in solution quality and convergence speed across various benchmark problems.
Probing the lowest energy configuration of a complex system by quantum annealing was recently found to be more effective than its classical, thermal counterpart. By comparing classical and quantum Monte Carlo annealing protocols on the two-dimensional random Ising model (a prototype spin glass), we confirm the superiority of quantum annealing relative to classical annealing. We also propose a theory of quantum annealing based on a cascade of Landau-Zener tunneling events. For both classical and quantum annealing, the residual energy after annealing is inversely proportional to a power of the logarithm of the annealing time, but the quantum case has a larger power that makes it faster.
In reverse quantum annealing, the initial state is an eigenstate of the final problem Hamiltonian and the transverse field is cycled rather than strictly decreased as in standard (forward) quantum annealing. We present a numerical study of the reverse quantum annealing protocol applied to the ppp-spin model (p=3p=3p=3), including pausing, in an open-system setting accounting for dephasing in the energy eigenbasis, which results in thermal relaxation. We consider both independent and collective dephasing and demonstrate that in both cases the open-system dynamics substantially enhances the performance of reverse annealing. Namely, including dephasing overcomes the failure of purely closed-system reverse annealing to converge to the ground state of the ppp-spin model. We demonstrate that pausing further improves the success probability. The collective dephasing model leads to somewhat better performance than independent dephasing. The protocol we consider corresponds closely to the one implemented in the current generation of commercial quantum annealers, and our results help to explain why recent experiments demonstrated enhanced success probabilities under reverse annealing and pausing.
We study the effect of the anneal path control per qubit, a new user control feature offered on the D-Wave 2000Q quantum annealer, on the performance of quantum annealing for solving optimization problems by numerically solving the time-dependent Schrödinger equation for the time-dependent Hamiltonian modeling the annealing problems. The anneal path control is thereby modeled as a modified linear annealing scheme, resulting in an advanced and retarded scheme. The considered optimization problems are 2-SAT problems with 12 Boolean variables, a known unique ground state and a highly degenerate first excited state. We show that adjustment of the anneal path control can result in a widening of the minimal spectral gap by one or two orders of magnitude and an enhancement of the success probability of finding the solution of the optimization problem. We scrutinize various iterative methods based on the spin floppiness, the average spin value, and on the average energy and describe their performance in boosting the quantum annealing process.