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arXiv:2609.08973

Restart and first detection in a lackadaisical quantum walk with flat-band localization

Debraj Das

quant-phcond-mat.stat-mechSubmitted Sep 8, 2026• Announced Sep 9, 2026• 0 citations
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Abstract

We study stochastic and sharp restart in a one-dimensional lackadaisical discrete-time quantum walk with self-loop weight $\ell$. In the absence of restart, the dynamics has a flat band responsible for intrinsic localization and two dispersive bands supporting ballistic propagation. We compare two initially localized benchmark states: a flat-band-active state with finite flat-band overlap and a flat-band-dark state with zero flat-band overlap. For geometric stochastic restart with per-step restart probability $q$, the stationary mean-squared displacement scales as $q^{-2}$ as $q\to0$. In the same limit, the restart-site occupation probability approaches the restart-free intrinsic localized value for the flat-band-active state, whereas for the flat-band-dark state it vanishes as $q\ln(1/q)$. For power-law restart, where $p_m\propto m^{-s}$ is the probability that the waiting time to the next restart is $m$ steps, a normalized stationary site-occupation distribution exists only for $s>2$, while the stationary absolute spatial moment of order $p$ is finite only for $s>p+2$. In the regime $1<s\leq2$, at every fixed lattice site, the flat-band-active occupation converges to the intrinsic flat-band profile, while the flat-band-dark occupation tends to zero. We also consider monitored first detection with sharp restart, in which the walk is reinitialized after a fixed number $r$ of consecutive unsuccessful measurements. For fixed $r$, the mean first-detected-passage time of the flat-band-active state exhibits a minimum at an intermediate self-loop weight, whereas the flat-band-dark state approaches a ballistic detection limit as $\ell\to\infty$.

Comments
21 pages, 9 figures

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We study stochastic and sharp restart in a one-dimensional lackadaisical discrete-time quantum walk with self-loop weight ℓ\ellℓ. In the absence of restart, the dynamics has a flat band responsible for intrinsic localization and two dispersive bands supporting ballistic propagation. We compare two initially localized benchmark states: a flat-band-active state with finite flat-band overlap and a flat-band-dark state with zero flat-band overlap. For geometric stochastic restart with per-step restart probability qqq, the stationary mean-squared displacement scales as q−2q^{-2}q−2 as q→0q\to0q→0. In the same limit, the restart-site occupation probability approaches the restart-free intrinsic localized value for the flat-band-active state, whereas for the flat-band-dark state it vanishes as qln⁡(1/q)q\ln(1/q)qln(1/q). For power-law restart, where pm∝m−sp_m\propto m^{-s}pm​∝m−s is the probability that the waiting time to the next restart is mmm steps, a normalized stationary site-occupation distribution exists only for s>2s>2s>2, while the stationary absolute spatial moment of order ppp is finite only for s>p+2s>p+2s>p+2. In the regime 1<s≤21<s\leq21<s≤2, at every fixed lattice site, the flat-band-active occupation converges to the intrinsic flat-band profile, while the flat-band-dark occupation tends to zero. We also consider monitored first detection with sharp restart, in which the walk is reinitialized after a fixed number rrr of consecutive unsuccessful measurements. For fixed rrr, the mean first-detected-passage time of the flat-band-active state exhibits a minimum at an intermediate self-loop weight, whereas the flat-band-dark state approaches a ballistic detection limit as ℓ→∞\ell\to\inftyℓ→∞.
Classical first-passage times under restart are used in a wide variety of models, yet the quantum version of the problem still misses key concepts. We study the quantum hitting time with restart using a monitored quantum walk. The restart strategy eliminates the problem of dark states, i.e., cases where the particle evades detection, while maintaining the ballistic propagation which is important for a fast search. We find profound effects of quantum oscillations on the restart problem, namely, a type of instability of the mean detection time, and optimal restart times that form staircases, with sudden drops as the rate of sampling is modified. In the absence of restart and in the Zeno limit, the detection of the walker is not possible, and we examine how restart overcomes this well-known problem, showing that the optimal restart time becomes insensitive to the sampling period.
Restart is a common strategy observed in nature that accelerates first-passage processes, and has been extensively studied using classical random walks. In the quantum regime, restart in continuous-time quantum walks (CTQWs) has been shown to expedite the quantum hitting times []. Here, we study how restarting monitored discrete-time quantum walks (DTQWs) affects the quantum hitting times. We show that the restarted DTQWs outperform classical random walks in target searches, benefiting from quantum ballistic propagation, a feature shared with their continuous-time counterparts. Moreover, the explicit coin degree of freedom in DTQWs allows them to surpass even CTQWs in target detection without sacrificing any quantum advantage. Additionally, knowledge of the target's parity or position relative to the origin can be leveraged to tailor DTQWs for even faster searches. Our study paves the way for more efficient use of DTQWs in quantum-walk-based search algorithms, simulations, and modeling of quantum transport towards targeted sites in complex quantum networks.
We investigate the effect of stochastic restart/resetting on discrete-time quantum walks concerning its impact on the meantime to absorption in the walk on the one-dimensional lattice terminated by a pair of totally absorbing sites. The two distinguished sites also play the role of an apparatus that detects the moment of time when a quantum walk reaches these boundaries. Our results reveal a notable difference between the quantum walk and its classical counterpart. Specifically, we focus on instances of classical random walks in which stochastic resetting fails to reduce the meantime to absorption. Conversely, we show that stochastic resetting can be utilized to effectively diminish the meantime to absorption in the quantum version of this walk. We relate the observed difference between quantum and classical cases to the fact that restarting the quantum state not only changes position distribution but inevitably changes complementary quasimomentum distribution.
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A powerful strategy to accelerate quantum-walk-based search algorithms leverages on resetting protocols, where a detector monitors a target site and the evolution of the walker is restarted if no detection occurs within a fixed time interval. The optimal resetting rate can be extracted from the time evolution of the probability S(t) that the detector has not clicked up to time t. We analyze S(t) for a quantum walk on a one-dimensional lattice when the coupling between sites decays algebraically as d^{-α} with the distance d, for α∈(0,∞). At long times, S(t) decays with a universal power-law exponent that is independent of α. At short times, S(t) exhibits a plethora of phase transitions as a function of α. From this, we provide a strategy to determine the optimal resetting rate. We identify two regimes: for α>1, the resetting rate r is bounded from below by the velocity with which information propagates causally across the lattice; for α<1, instead, the long-range hopping tends to localize the walker: The optimal resetting rate depends on the size of the lattice and diverges as α→0. Our strategy directly connects local measurement outcomes with the global dynamics encoded in S(t). We derive simple models explaining our numerical results, shedding light on the interplay of long-range coherent dynamics, symmetries, and local quantum measurement processes in determining equilibrium. Our findings offer experimentally testable predictions and provide new physical insights on optimizing quantum search through resetting.
Abstract In this paper, we study the properties of lackadaisical quantum walks (LQWs) on a line. This model was first proposed in Wong (2015 J. Phys. A: Math. Theor . 48 435304) as a quantum analogue of lazy random walks where each vertex is attached to τ self-loops. We derive an analytic expression for the localization probability of the walker at the origin after infinite steps, and obtain the peak velocities of the walker. We also calculate the wave function of the walker starting from the origin and obtain a long-time approximation for the entire probability density function. As an application of the density function, we prove that lackadaisical quantum walks spread ballistically for arbitrary τ , and give an analytic solution for the variance of the walker’s probability distribution.