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.
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.
Grover’s quantum search algorithm delivers quadratic speedup over classical counterparts for finding an item in unstructured database. But the accuracy of the algorithm degrades as the number of qubits are increased. Noises of various types affect the accuracy of the Grover’s algorithm. The imperfect measurement on the qubit results in measurement errors appears to be one of the factors which limit the scalability of near-term quantum devices/algorithms. This paper is focused on the implementation of measurement error mitigation technique on Grover’s algorithm up to 4-qubits. The measurement error mitigation model for Grover’s algorithm is developed and implemented on the real-time quantum computer. The accuracy of Grover’s algorithm up to 4-qubits with and without measurement errors is evaluated and compared. The results indicate that measurement error mitigation technique mitigates the measurement errors of the Grover’s algorithm and improves its accuracy.
Classical database join operations are a significant performance bottleneck in data-intensive applications, typically requiring quadratic time proportional to the size of the input. Quantum search algorithms, such as Grover’s algorithm, promise asymptotic speedups for unstructured search and can be adapted to accelerate equality-based joins. In this paper, we present a systematic simulation study of a Grover-based quantum join primitive and a hybrid classical-quantum join scheme, comparing them with a baseline classical nested-loop join. Parameterised Grover circuits have been implemented for marked-item search, together with resource estimation (qubit count, circuit depth, and gate counts), using the Qiskit Aer simulator. The classical join is modelled as a nested-loop equality join over two arrays, while the hybrid scheme uses a classical Bloom filter to reduce the candidate space, followed by a Grover search over the remaining items. To approximate near term hardware, we incorporate depolarizing gate noise and readout noise via Aer noise models and investigate two mitigation techniques: readout error calibration and zero noise extrapolation. Our simulations, conducted in a Python environment without GPU acceleration, demonstrate that the Grover based and hybrid joins exhibit the expected sub-quadratic scaling in runtime compared to the classical nested-loop join for problem sizes up to N = 215. We additionally observe that realistic noise significantly degrades the raw success probability of the quantum join, while combining readout mitigation with zero noise extrapolation partially restores accuracy. These results provide a resource-aware and noise-aware baseline for evaluating quantum-accelerated joins on future fault-tolerant or high-fidelity noisy intermediate-scale quantum (NISQ) devices.
Abstract When quantum computing becomes a wide-spread commercial reality, Quantum Search Algorithms (QSA) and especially Grover’s QSA will inevitably be one of their main applications, constituting their cornerstone. Most of the literature assumes that the quantum circuits are free from decoherence. Practically, decoherence will remain unavoidable as is the Gaussian noise of classic circuits imposed by the Brownian motion of electrons, hence it may have to be mitigated. In this contribution, we investigate the effect of quantum noise on the performance of QSAs, in terms of their success probability as a function of the database size to be searched, when decoherence is modelled by depolarizing channels’ deleterious effects imposed on the quantum gates. Moreover, we employ quantum error correction codes for limiting the effects of quantum noise and for correcting quantum flips. More specifically, we demonstrate that, when we search for a single solution in a database having 4096 entries using Grover’s QSA at an aggressive depolarizing probability of 10−3, the success probability of the search is 0.22 when no quantum coding is used, which is improved to 0.96 when Steane’s quantum error correction code is employed. Finally, apart from Steane’s code, the employment of Quantum Bose-Chaudhuri-Hocquenghem (QBCH) codes is also considered.
Grover search is a renowned quantum search algorithm that leverages quantum superposition to find a marked item with quadratic speedup. However, when implemented on Noisy Intermediate-scale Quantum (NISQ) hardware, the required repeated iterations of the oracle and diffusion operators increase exponentially with the number of qubits, resulting in significant noise accumulation. To address this, we propose a hybrid quantum-classical architecture that replaces quantum iterations with updates from a classical optimizer. This optimizer minimizes the expectation value of an oracle Hamiltonian with respect to a parameterized quantum state representing the target bit string. Our parameterized quantum circuit is much shallower than Grover search circuit, and we found that it outperforms Grover search on noisy simulators and NISQ hardware. When the number of qubits is greater than 5, our approach still maintains usable success probability, while the success probability of Grover search is at the same level as random guessing.
The Grover search algorithm performs an unstructured search of a marked item in a database quadratically faster than classical algorithms and is shown to be optimal. Here, we show that if the search space is divided into two blocks with the local query operators and the global operators satisfy certain condition, then it is possible to achieve an improvement of bi-quadratic speed-up. Furthermore, we investigate the bi-quadratic speed-up in the presence of noise and show that it can tolerate noisy scenario. This may have potential applications for diverse fields, including database searching, and optimization, where efficient search algorithms play a pivotal role in solving complex computational problems.
Abstract In quantum computing, Grover's technique is extremely vulnerable to several kinds of quantum noise in practice, which can seriously impair its functionality. This study examines how the algorithm's success probabilities is affected by several types of noise, such as rotational error, thermal noise, Two-State Purity (TSP) error, decay and decoherence, and depolarization variables. The findings demonstrate that the algorithm can accomplish flawless amplification of the goal state with negligible mistakes in non-target states when it is executed without noise. The most notable decrease among the noise kinds is caused by decay and decoherence errors and depolarization factors, which lower the likelihood of correctly detecting the target state from 1.0 to roughly 0.885 and 0.968, respectively. Thermal noise, TSP error, and rotational error, on the other hand, have less severe impacts and result in lesser deviations in both target and non-target states. These results demonstrate how crucial quantum error correction and noise reduction techniques are to guaranteeing Grover's algorithm's scalability and dependability in noisy quantum environments. To fully utilize the algorithm's promise in practical applications on both present-day and next quantum technology, these issues must be mitigated.