Quantum search on noisy intermediate-scale quantum devices

• URL: http://arxiv.org/abs/2202.00122v2
• Date: Tue, 27 Sep 2022 16:59:19 GMT
• Title: Quantum search on noisy intermediate-scale quantum devices
• Authors: Kun Zhang, Kwangmin Yu, Vladimir Korepin
• Abstract summary: 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.
• Score: 7.147209811770232
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantum search algorithm (also known as Grover's algorithm) lays the foundation for many other quantum algorithms. Although it is very simple, its implementation is limited on noisy intermediate-scale quantum (NISQ) processors. Grover's algorithm was designed without considering the physical resources, such as depth, in the real implementations. Therefore, Grover's algorithm can be improved for NISQ devices. In this paper, we demonstrate how to implement quantum search algorithms better on NISQ devices. We present detailed benchmarks of the five-qubit quantum search algorithm on different quantum processors, including IBMQ, IonQ, and Honeywell quantum devices. We report the highest success probability of the five-qubit search algorithm compared to previous works. Our results show that designing the error-aware quantum search algorithms is possible, which can maximally harness the power of NISQ computers.

Related papers

• 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)
• 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)
• Generalized quantum Arimoto-Blahut algorithm and its application to quantum information bottleneck [55.22418739014892]
  We generalize the quantum Arimoto-Blahut algorithm by Ramakrishnan et al. We apply our algorithm to the quantum information bottleneck with three quantum systems. Our numerical analysis shows that our algorithm is better than their algorithm.
  arXiv  Detail & Related papers  (2023-11-19T00:06:11Z)
• 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)
• A Scalable 5,6-Qubit Grover's Quantum Search Algorithm [0.0]
  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.
  arXiv  Detail & Related papers  (2022-04-30T00:35:54Z)
• Revisiting dequantization and quantum advantage in learning tasks [3.265773263570237]
  We show that classical algorithms with sample and query (SQ) access can accomplish some learning tasks exponentially faster than quantum algorithms with quantum state inputs. Our findings suggest that the absence of exponential quantum advantage in some learning tasks may be due to SQ access being too powerful relative to quantum state inputs.
  arXiv  Detail & Related papers  (2021-12-01T20:05:56Z)
• Towards understanding the power of quantum kernels in the NISQ era [79.8341515283403]
  We show that the advantage of quantum kernels is vanished for large size datasets, few number of measurements, and large system noise. Our work provides theoretical guidance of exploring advanced quantum kernels to attain quantum advantages on NISQ devices.
  arXiv  Detail & Related papers  (2021-03-31T02:41:36Z)
• 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)
• Benchmarking 16-element quantum search algorithms on superconducting quantum processors [0.0]
  We present experimental results on running 4-qubit unstructured search on IBM quantum processors. Our best attempt attained probability of success around 24.5%. We conclude that it is extremely important to design hardware-aware algorithms.
  arXiv  Detail & Related papers  (2020-07-13T17:46:19Z)

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.