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arXiv:2609.04408

Thermal lifetime of the centralized repetition code: From quantum annealing to social dynamics

Ari Mizel, Van Molino

quant-phSubmitted Sep 3, 2026• Announced Sep 7, 2026• 0 citations
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Abstract

There is an extensive body of research probing the potential of adiabatic quantum computation and quantum annealing to solve hard computational problems. This research endeavor is complicated by noise afflicting hardware during the computational process. An error-correcting approach called quantum annealing correction (QAC) was suggested to mitigate this noise. The QAC approach employs a centralized version of the repetition code in which the qubits are configured in a star-graph pattern with one special qubit playing the role of the hub. In this paper, we explore the thermal physics of the centralized repetition code, using a Lindblad equation framework to analyze its lifetime when coupled to a thermal bath. We show that its centralized configuration leads to thermal stability, enabling the robust storage of a logical bit of information, despite the fact that there is only 1 ferromagnetic Ising interaction per qubit like a 1-dimensional Ising chain.

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There is an extensive body of research probing the potential of adiabatic quantum computation and quantum annealing to solve hard computational problems. This research endeavor is complicated by noise afflicting hardware during the computational process. An error-correcting approach called quantum annealing correction (QAC) was suggested to mitigate this noise. The QAC approach employs a centralized version of the repetition code in which the qubits are configured in a star-graph pattern with one special qubit playing the role of the hub. In this paper, we explore the thermal physics of the centralized repetition code, using a Lindblad equation framework to analyze its lifetime when coupled to a thermal bath. We show that its centralized configuration leads to thermal stability, enabling the robust storage of a logical bit of information, despite the fact that there is only 1 ferromagnetic Ising interaction per qubit like a 1-dimensional Ising chain.
The performance of open-system quantum annealing is adversely affected by thermal excitations out of the ground state. While the presence of energy gaps between the ground and excited states suppresses such excitations, error correction techniques are required to ensure full scalability of quantum annealing. Quantum annealing correction (QAC) is a method that aims to improve the performance of quantum annealers when control over only the problem (final) Hamiltonian is possible, along with decoding. Building on our earlier work [S. Matsuura et al., Phys. Rev. Lett. 116, 220501 (2016)], we study QAC using analytical tools of statistical physics by considering the effects of temperature and a transverse field on the penalty qubits in the ferromagnetic ppp-body infinite-range transverse-field Ising model. We analyze the effect of QAC on second (p=2p=2p=2) and first ($p\ensuremath{\ge}3$) order phase transitions, and construct the phase diagram as a function of temperature and penalty strength. Our analysis reveals that for sufficiently low temperatures and in the absence of a transverse field on the penalty qubit, QAC breaks up a single, large free-energy barrier into multiple smaller ones. We find theoretical evidence for an optimal penalty strength in the case of a transverse field on the penalty qubit, a feature observed in QAC experiments. Our results provide further compelling evidence that QAC provides an advantage over unencoded quantum annealing.
Quantum annealing correction (QAC) models provide a promising approach for mitigating errors in quantum annealers. Previous studies have established that QAC models are crucial for ensuring the robustness of the ground state of the Ising model on hardware. In this work, the effects of QAC models incorporating replicas with additional interactions, specifically, the and the , are investigated for problems characterized by a small energy gap between the ground and first excited states during quantum annealing, a well-known bottleneck to reaching the ground state. The results demonstrate that these QAC models can obtain the optimal solution within short annealing times by exploiting diabatic transitions, even for problems with a small energy gap. These findings highlight the potential of QAC models as practical near-term algorithms for hardware subject to runtime limitations and control noise.
Abstract We present a general error-correcting scheme for quantum annealing that allows for the encoding of a logical qubit into an arbitrarily large number of physical qubits. Given any Ising model optimization problem, the encoding replaces each logical qubit by a complete graph of degree C, representing the distance of the error-correcting code. A subsequent minor-embedding step then implements the encoding on the underlying hardware graph of the quantum annealer. We demonstrate experimentally that the performance of a D-Wave Two quantum annealing device improves as C grows. We show that the performance improvement can be interpreted as arising from an effective increase in the energy scale of the problem Hamiltonian or, equivalently, an effective reduction in the temperature at which the device operates. The number C thus allows us to control the amount of protection against thermal and control errors, and, in particular, to trade qubits for a lower effective temperature that scales as C−η, with η⩽2. This effective temperature reduction is an important step towards scalable quantum annealing.
Ising machines, quantum annealing (QA) machines, are promising for solving combinatorial optimization problems at high speed. Problems are formulated as Ising models where optimal solutions correspond to ground states, but stochastic noise often causes deviations, necessitating error reduction strategies. The quantum annealing correction (QAC) model introduces penalty spins to favor consistent solutions across replicas and has shown effectiveness in QA, but its behavior under simulated annealing (SA) remains insufficiently understood. This study systematically evaluates the stacked model, a recently proposed error reduction method, against QAC-based and simple parallelization approaches in SA. Our results demonstrate that, even for large-scale systems, the stacked model consistently outperforms alternatives as the number of replicas increases, maintaining relatively high success probability through spontaneous replica alignment via structured coupling. These findings highlight the stacked model as a scalable and error reduction framework for Ising-based optimization.
We demonstrate that the performance of a quantum annealer on hard random Ising optimization problems can be substantially improved using quantum annealing correction (QAC). Our error correction strategy is tailored to the D-Wave Two device. We find that QAC provides a statistically significant enhancement in the performance of the device over a classical repetition code, improving as a function of problem size as well as hardness. Moreover, QAC provides a mechanism for overcoming the precision limit of the device, in addition to correcting calibration errors. Performance is robust even to missing qubits. We present evidence for a constructive role played by quantum effects in our experiments by contrasting the experimental results with the predictions of a classical model of the device. Our work demonstrates the importance of error correction in appropriately determining the performance of quantum annealers.
Although Majorana platforms are promising avenues toward realizing topological quantum computing, they are still susceptible to errors from thermal noise and other sources. We show that the error rate of Majorana qubits can be drastically reduced using a one-dimensional repetition code. The success of the code is due the imbalance between the phase error rate and the flip error rate. We demonstrate how a repetition code can be naturally constructed from segments of Majorana nanowires. We find the optimal lifetime may be extended from a millisecond to over one second.