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arXiv:2610.06268

Adiabatic tracking and optimal schedules for classical and quantum Gibbs-state preparation

Pedro C. S. Costa, Mauro E. S. Morales, Dong An, Kaur Kristjuhan

quant-phmath-phSubmitted Oct 5, 2026• Announced Oct 6, 2026• 0 citations
v1 · Oct 5, 2026v2 · Oct 27, 2026
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Abstract

We develop a discrete adiabatic framework for Gibbs-state preparation in classical and quantum optimisation algorithms. For finite time-inhomogeneous reversible Markov chains, we derive a tracking theorem that bounds the distance from the evolving distribution to the instantaneous stationary state by combining two effects: contraction of previously accumulated error by the Markov dynamics and the new error generated as the stationary distribution changes from one step to the next. For Gibbs paths, this leads to explicit step-complexity bounds and to gap- and fluctuation-adapted schedules controlled by the Markov-chain spectral gap and the energy-fluctuation scale $V(β)=\sqrt{\operatorname{Var}_{π_β}(E)}$. In the weak-damping regime, the resulting schedule takes the form $\dotβ\proptoδ(β)/V(β)$. We then apply the discrete quantum adiabatic theorem to temperature-dependent Szegedy quantum walks. The quantum-walk eigenphase gap satisfies $Δ_w=Θ(\sqrtδ)$, where $δ$ is the spectral gap of the underlying Markov chain. By analysing the finite-difference terms entering the theorem, we show that this square-root spectral amplification translates into a quadratic improvement in the gap dependence only for suitably gap-adapted schedules. Under the corresponding regularity conditions, the quantum adiabatic cost scales as $O(δ_{\min}^{-1/2})$, compared with the classical $O(δ_{\min}^{-1})$ relaxation scale.

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50 pages, 12 figures

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The efficiency of adiabatic quantum evolution is governed by the evolution time T , which typically scales as O ( Δ − 2 ) with the minimum energy gap Δ . However, the rigorous lower bound is O ( L Δ − 1 ) , where L is the adiabatic path length. Although L is formally upper bounded by O ( Δ − 1 ) , such a bound is often too loose in practice, and L can be bounded independently of Δ . This indicates the potential for a quadratic speedup through adiabatic schedule construction. Here, we introduce the constant geometric speed (CGS) schedule, which traverses the adiabatic path at a uniform rate. We show that this approach reduces the scaling of the evolution time by a factor of Δ − 1 , provided L remains bounded independently of Δ . We propose a segmented CGS protocol where path segment lengths are computed from eigenstate overlaps on the fly, reducing the prior spectral-knowledge requirement from the full gap function Δ ( s ) to just a global lower bound on the energy gap. Numerical tests on adiabatic unstructured search, N 2 , and a [2Fe-2S] cluster demonstrate the optimal Δ − 1 scaling, confirming a quadratic speedup over the standard linear schedule.
Adiabatic quantum computing is a powerful framework for state preparation, while its evolution time often scales quadratically in the inverse Hamiltonian spectral gap, leading to sub-optimal computational complexity. In this work, we introduce a nonlinear adaptive strategy for finding the time scheduling function, and show that the gap dependence can be quadratically improved to be inverse linear for a wide range of systems under a mild gap measure condition. Through variational analysis, we further demonstrate the optimality of our schedule for systems with linear gap and the partial optimality for general systems, while we also rigorously show that the commonly used linear schedule is never optimal.
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In this work we consider the problem of preparation of the stationary distribution of irreducible, time-reversible Markov chains, which is a fundamental task in algorithmic Markov chain theory. For the classical setting, this task has a complexity lower bound of Ω(δ−1)\Omega(\delta^{-1})Ω(δ−1), where δ\deltaδ is the spectral gap of the Markov chain, and other dependencies contribute only logarithmically. In the quantum case, the conjectured complexity is O(δ−1)O(\sqrt{\delta^{-1}})O(δ−1​), with other dependencies contributing only logarithmically. However, this bound has only been achieved for a few special classes of Markov chains. In this work, we provide a method for the sequential preparation of stationary distributions for sequences of time-reversible NNN-state Markov chains, akin to the setting of simulated annealing methods. The complexity of preparation we achieve is O(δ−1N4)O(\sqrt{\delta^{-1}} \sqrt[4]{N})O(δ−1​4N​), neglecting logarithmic factors. While this result falls short of the conjectured optimal time, it provides a quadratic improvement over na\{i}ve approaches. Moreover, for the case when the output distributions are required to be encoded in pure quantum states we identify the settings where our algorithm is strictly optimal. The settings of slowly evolving sequences of Markov chains naturally appear in reinforcement learning, and consequently our results can be readily applied in quantum machine learning as well.
Recently, several approaches to solving linear systems on a quantum computer have been formulated in terms of the quantum adiabatic theorem for a continuously varying Hamiltonian.Such approaches have enabled near-linear scaling in the condition number κ of the linear system, without requiring a complicated variable-time amplitude amplification procedure.However, the most efficient of those procedures is still asymptotically suboptimal by a factor of log(κ).Here, we prove a rigorous form of the adiabatic theorem that bounds the error in terms of the spectral gap for intrinsically discrete-time evolutions.In combination with the qubitized quantum walk, our discrete adiabatic theorem gives a speed-up for all adiabatic algorithms.Here, we use this combination to develop a quantum algorithm for solving linear systems that is asymptotically optimal, in the sense that the complexity is strictly linear in κ, matching a known lower bound on the complexity.Our O[κ log(1/ )] complexity is also optimal in terms of the combined scaling in κ and the precision .Compared to existing suboptimal methods, our algorithm is simpler and easier to implement.Moreover, we determine the constant factors in the algorithm, which would be suitable for determining the complexity in terms of gate counts for specific applications.
Szegedy's quantum walk quadratically accelerates reversible classical Markov chains, but extending this mechanism to efficiently implementable quantum Gibbs samplers has remained challenging beyond special classes. We present a walk-free quantum algorithm for preparing purified Gibbs states with a quadratic improvement in spectral-gap dependence for a broad class of quantum Gibbs samplers that satisfy exact Kubo-Martin-Schwinger detailed balance. Our main structural result is an explicit factorization of the corresponding parent Hamiltonian into noncommutative first-order operators. This turns purified Gibbs-state preparation into a singular-value filtering problem and enables a quantum singular value transformation algorithm with quadratically improved gap dependence under standard coherent-access and warm-start assumptions. The framework applies to several efficiently implementable Gibbs samplers beyond the Davies setting. Moreover, we construct noncommuting Hamiltonians with efficiently preparable warm starts whose refinement under quantum Gibbs samplers requires exponential time. For these models, our algorithm achieves an end-to-end quantum speedup over the worst-case mixing scale.