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arXiv:2604.04137 · Physical Review A

Noise tolerance via reinforcement in the quantum search problem

Marjan Homayouni-Sangari, Abolfazl Ramezanpour

quant-phcond-mat.dis-nncs.DSSubmitted Apr 5, 2026• Announced Apr 7, 2026• 0 citations
v1 · Apr 5, 2026v2 · Aug 16, 2026
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Abstract

We find that reinforcement exponentially reduces computation time of the quantum search problem from $\sqrt{D}$ to $\ln D$ in a $D$-dimensional system. Therefor, a reinforced quantum search is expected to exhibit an exponentially larger noise threshold compared to a standard search algorithm in a noisy environment. We use numerical simulations to characterize the level of noise tolerance via reinforcement in the presence of both coherent and incoherent noise, considering a system of $N$ qubits and a single $D$-level (qudit) system. Our results show that reinforcement significantly enhances the algorithm's success probability and improves the scaling of its computation time with system size. These findings indicate that reinforcement offers a promising strategy for error mitigation, especially when a precise noise model is unavailable.

Journal reference
Phys. Rev. A 114, 022423 (2026)
DOI
10.1103/qt8h-6dyg
Comments
16 pages, 5 figures

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We find that reinforcement exponentially reduces computation time of the quantum search problem from D\sqrt{D}D​ to ln⁡D\ln DlnD in a DDD-dimensional system. Therefor, a reinforced quantum search is expected to exhibit an exponentially larger noise threshold compared to a standard search algorithm in a noisy environment. We use numerical simulations to characterize the level of noise tolerance via reinforcement in the presence of both coherent and incoherent noise, considering a system of NNN qubits and a single DDD-level (qudit) system. Our results show that reinforcement significantly enhances the algorithm's success probability and improves the scaling of its computation time with system size. These findings indicate that reinforcement offers a promising strategy for error mitigation, especially when a precise noise model is unavailable.
Abstract The performance of quantum simulations heavily depends on the efficiency of noise mitigation techniques and error correction algorithms. Reinforcement has emerged as a powerful strategy to enhance the efficiency of learning and optimization algorithms. In this study, we demonstrate that a reinforced quantum dynamics can exhibit significant robustness against interactions with a noisy environment. We study a quantum annealing process in which reinforcement encourages the system to maintain its current state or follow a noise-free evolution. A learning algorithm is used to derive a concise approximation of these reinforced dynamics, thereby reducing the total evolution time and, consequently, the system’s exposure to noisy interactions. This also avoids the complexities associated with implementing quantum feedback in such reinforcement algorithms. The efficacy of our method is demonstrated through numerical simulations of reinforced quantum annealing with one-qubit and two-qubit systems under Pauli noise.
We present theoretical and experimental studies on efficient quantum search with noise. We propose a noise-tolerant method that significantly reduces the running time and exponentially improves the error threshold with number of qubits for Grover's search. Experiments are implemented on different quantum computing setups, with all results clearly confirming the advantage of our noise-tolerant method to the original Grover's search. In one setup, our method produces quantum advantages while the original Grover's search does not. In another setup with smaller noise, our method produces a larger quantum advantage than the original Grover's search does.
In order to understand the bounds of utilization of Grover's search algorithm for large unstructured data in the presence of quantum computer noise, we undertake a series of simulations by inflicting various types of noise, modeled by the IBM qiskit. We apply three forms of Grover's algorithm: (i) the standard one, with 4--10 qubits; (ii) a recently published modified Grover's algorithm, set to reduce the circuit depth; and (iii) the algorithms in (i) and (ii) with multicontrol Toffoli's gates modified by the addition of an ancilla qubit. Based on these simulations, we find the upper bound of noise for these cases, establish its dependence on the quantum depth of the circuit, and provide comparison among them. By extrapolation of the fitted thresholds, we predict what would be the typical gate error bounds when applying Grover's algorithms for the search of data in a data set as large as 32 000.
Abstract Grover’s quantum search algorithm promises a quadratic speedup for unstructured search over its classical counterpart. But this advantage is affected by noise acting on the search space. Here, we show that a quantum switch can act as a resource to mitigate the effects of noise. In this scenario, the noise is modeled by a depolarizing channel, which coherently acts on the entire quantum register. We show that a quantum switch can significantly reduce the error in Grover’s search algorithm. We consider the success probability of finding the marked item as the sole quantifier of diminishing the effect of noise in the search space in the presence of quantum switch. We propose two frameworks for the application of quantum switches. In the first framework, we apply the superposition of channel’s orders in the form of a switch and do a post-selection at every iteration of the applications of the Grover operator. In the second framework, we delay this measurement and post-selection until the very end. The number of post selections is minimal in the second scenario, and hence the noise reduction can be attributed more to the presence of quantum switch. We illustrate with an example of significant advantage in the success probability of Grover’s algorithm using quantum switch.
The effect of unitary noise on the performance of Grover's quantum search algorithm is studied. This type of noise may result from tiny fluctuations and drift in the parameters of the (quantum) components performing the computation. The resulting operations are still unitary, but not precisely those assumed in the design of the algorithm. Here we focus on the effect of such noise in the Hadamard gate W, which is an essential component in each iteration of the quantum search process. To this end W is replaced by a noisy Hadamard gate U. The parameters of U at each iteration are taken from an arbitrary probability distribution (e.g., a Gaussian distribution) and are characterized by their statistical moments around the parameters of W. For simplicity, we assume that the noise is unbiased and isotropic, namely, all noise variables in the parametrization we use have zero average and the same standard deviation $\ensuremath{\epsilon}.$ The noise terms at different calls to U are assumed to be uncorrelated. For a search space of size N=2n{N=2}^{n}N=2n (where n is the number of qubits used to span this space) it is found that as long as $\ensuremath{\epsilon}<{O(n}^{\ensuremath{-}1/2}{N}^{\ensuremath{-}1/4})$ the algorithm maintains significant efficiency, while above this noise level its operation is hampered completely. It is also found that below this noise threshold, when the search fails, it is likely to provide a state that differs from the marked state by only a few bits. This feature can be used to search for the marked state by a classical postprocessing, even if the quantum search has failed, thus improving the success rate of the search process.
Adaptive quantum computing control is important for the stability and efficiency of quantum systems, especially in the presence of dynamic environmental noise and hardware fluctuations. We present a unified strategy for machine learning (ML) techniques to improve adaptive quantum control in two main directions: optimization speed and out-of-sample error. Here, we propose a hybrid learning method combining reinforcement learning (RL), deep neural networks (DNNs), and quantum-inspired models to actively and adaptively update quantum gate parameters and error mitigation strategies on the fly. Embedding of quantum noise models directly into the learning allows the system to predict and correct for decoherence, gate errors and measurement inaccuracy before they affect computational fidelity. Using extensive simulations as well as experimental validation on noisy intermediate-scale quantum (NISQ) processors, our improved ML model achieves a substantially higher fidelity and robustness compared to standard model-free and static control approaches. We introduce a novel feedback-loop mechanism based on online learning algorithms enabling the system to adjust for changing noise profiles and hardware drift without retraining. The optimization part uses metaheuristic methods to quickly converge to control parameters that perform well, and the error prevention mechanism features anomaly detection and prediction correction layers to minimize the effects of cumulative decoherence. The results demonstrate that our adaptive control framework can be used to not only speed up the convergence in training quantum algorithms, but also sustain the performance of gates against error at a variety of error rates with the reduction of the real-time impact of the error up to 35 %. This work highlights the promise of machine learningaugmented quantum control for improving reliability and scalability in quantum computing and serves as a stepping stone for future incorporation with fault-tolerant designs.