A Scalable 5,6-Qubit Grover's Quantum Search Algorithm

• URL: http://arxiv.org/abs/2205.00117v1
• Date: Sat, 30 Apr 2022 00:35:54 GMT
• Title: A Scalable 5,6-Qubit Grover's Quantum Search Algorithm
• Authors: Dinesh Reddy Vemula, Debanjan Konar, Sudeep Satheesan, Sri Mounica Kalidasu, and Attila Cangi
• Abstract summary: Grover's quantum search algorithm is one of the well-known applications of quantum computing. In this paper, a scalable Quantum Grover Search algorithm is introduced and implemented using 5-qubit and 6-qubit quantum circuits. The accuracy of the proposed 5-qubit and 6-qubit circuits is benchmarked against the state-of-the-art implementations for 3-qubit and 4-qubit.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Recent studies have been spurred on by the promise of advanced quantum computing technology, which has led to the development of quantum computer simulations on classical hardware. Grover's quantum search algorithm is one of the well-known applications of quantum computing, enabling quantum computers to perform a database search (unsorted array) and quadratically outperform their classical counterparts in terms of time. Given the restricted access to database search for an oracle model (black-box), researchers have demonstrated various implementations of Grover's circuit for two to four qubits on various platforms. However, larger search spaces have not yet been explored. In this paper, a scalable Quantum Grover Search algorithm is introduced and implemented using 5-qubit and 6-qubit quantum circuits, along with a design pattern for ease of building an Oracle for a higher order of qubits. For our implementation, the probability of finding the correct entity is in the high nineties. The accuracy of the proposed 5-qubit and 6-qubit circuits is benchmarked against the state-of-the-art implementations for 3-qubit and 4-qubit. Furthermore, the reusability of the proposed quantum circuits using subroutines is also illustrated by the opportunity for large-scale implementation of quantum algorithms in the future.

Related papers

• Supervised binary classification of small-scale digits images with a trapped-ion quantum processor [56.089799129458875]
  We show that a quantum processor can correctly solve the basic classification task considered. With the increase of the capabilities quantum processors, they can become a useful tool for machine learning.
  arXiv  Detail & Related papers  (2024-06-17T18:20:51Z)
• Quantum Subroutine for Variance Estimation: Algorithmic Design and Applications [80.04533958880862]
  Quantum computing sets the foundation for new ways of designing algorithms. New challenges arise concerning which field quantum speedup can be achieved. Looking for the design of quantum subroutines that are more efficient than their classical counterpart poses solid pillars to new powerful quantum algorithms.
  arXiv  Detail & Related papers  (2024-02-26T09:32:07Z)
• A Quantum-Classical Collaborative Training Architecture Based on Quantum State Fidelity [50.387179833629254]
  We introduce a collaborative classical-quantum architecture called co-TenQu. Co-TenQu enhances a classical deep neural network by up to 41.72% in a fair setting. It outperforms other quantum-based methods by up to 1.9 times and achieves similar accuracy while utilizing 70.59% fewer qubits.
  arXiv  Detail & Related papers  (2024-02-23T14:09:41Z)
• QuantumSEA: In-Time Sparse Exploration for Noise Adaptive Quantum Circuits [82.50620782471485]
  QuantumSEA is an in-time sparse exploration for noise-adaptive quantum circuits. It aims to achieve two key objectives: (1) implicit circuits capacity during training and (2) noise robustness. Our method establishes state-of-the-art results with only half the number of quantum gates and 2x time saving of circuit executions.
  arXiv  Detail & Related papers  (2024-01-10T22:33:00Z)
• Practical Quantum Search by Variational Quantum Eigensolver on Noisy Intermediate-scale Quantum Hardware [0.0]
  We propose a hybrid quantum-classical architecture that replaces quantum iterations with updates from a classical parameterized quantum state. Our approach still maintains usable success probability, while the success probability of Grover search is at the same level as random guessing.
  arXiv  Detail & Related papers  (2023-04-07T17:32:55Z)
• Iterative Qubits Management for Quantum Index Searching in a Hybrid System [56.39703478198019]
  IQuCS aims at index searching and counting in a quantum-classical hybrid system. We implement IQuCS with Qiskit and conduct intensive experiments. Results demonstrate that it reduces qubits consumption by up to 66.2%.
  arXiv  Detail & Related papers  (2022-09-22T21:54:28Z)
• Entanglement and coherence in Bernstein-Vazirani algorithm [58.720142291102135]
  Bernstein-Vazirani algorithm allows one to determine a bit string encoded into an oracle. We analyze in detail the quantum resources in the Bernstein-Vazirani algorithm. We show that in the absence of entanglement, the performance of the algorithm is directly related to the amount of quantum coherence in the initial state.
  arXiv  Detail & Related papers  (2022-05-26T20:32:36Z)
• Quantum search on noisy intermediate-scale quantum devices [7.147209811770232]
  Grover's algorithm is designed without considering the physical resources, such as depth, in the real implementations. We present detailed benchmarks of the five-qubit quantum search algorithm on different quantum processors, including IBMQ, IonQ, and Honeywell quantum devices. Our results show that designing the error-aware quantum search algorithms is possible, which can maximally harness the power of NISQ computers.
  arXiv  Detail & Related papers  (2022-01-31T22:25:58Z)
• Quantum circuit synthesis of Bell and GHZ states using projective simulation in the NISQ era [0.0]
  We studied the viability of using Projective Simulation, a reinforcement learning technique, to tackle the problem of quantum circuit synthesis for noise quantum computers with limited number of qubits. Our simulations demonstrated that the agent had a good performance but its capacity for learning new circuits decreased as the number of qubits increased.
  arXiv  Detail & Related papers  (2021-04-27T16:11:27Z)
• Hardware Efficient Quantum Search Algorithm [17.74233101199813]
  We propose a novel hardware efficient quantum search algorithm to overcome this challenge. Our key idea is to replace the global diffusion operation with low-cost local diffusions. The circuit cost reduction leads to a remarkable improvement in the system success rates.
  arXiv  Detail & Related papers  (2021-03-26T01:08:50Z)
• Efficient unstructured search implementation on current ion-trap quantum processors [0.0]
  We present experimental results on running unstructured search in spaces defined by 4, 5 and 6 qubits on a quantum processor. Our best circuits obtained respectively 66%, 26% and 6% average probability of measuring the marked element. Viability of the theoretical result by Grover at these qubit counts is, to authors' knowledge demonstrated experimentally for the first time.
  arXiv  Detail & Related papers  (2020-10-08T08:43:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.