Mitigating noise in digital and digital-analog quantum computation
- URL: http://arxiv.org/abs/2107.12969v3
- Date: Thu, 10 Oct 2024 14:41:57 GMT
- Title: Mitigating noise in digital and digital-analog quantum computation
- Abstract summary: Digital-analog quantum computing (DAQC) offers a more resilient alternative quantum computing paradigm.
DAQC consistently surpasses digital approaches in fidelity, particularly as processor size increases.
These results establish DAQC as a viable alternative for quantum computing in the NISQ era.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Noisy Intermediate-Scale Quantum (NISQ) devices lack error correction, limiting scalability for quantum algorithms. In this context, digital-analog quantum computing (DAQC) offers a more resilient alternative quantum computing paradigm that outperforms digital quantum computation by combining the flexibility of single-qubit gates with the robustness of analog simulations. This work explores the impact of noise on both digital and DAQC paradigms and demonstrates DAQC's effectiveness in error mitigation. We compare the quantum Fourier transform and quantum phase estimation algorithms under a wide range of single and two-qubit noise sources in superconducting processors. DAQC consistently surpasses digital approaches in fidelity, particularly as processor size increases. Moreover, zero-noise extrapolation further enhances DAQC by mitigating decoherence and intrinsic errors, achieving fidelities above 0.95 for 8 qubits, and reducing computation errors to the order of $10^{-3}$. These results establish DAQC as a viable alternative for quantum computing in the NISQ era.
Related papers
- Digital Quantum Simulation of the Holstein-Primakoff Transformation on Noisy Qubits [40.8066152850216]
We study the digital quantum simulation of bosonic modes on a cloud-based superconducting quantum processor.<n>We examine the interplay between algorithmic and hardware-induced errors to identify optimal simulation parameters.
arXiv Detail & Related papers (2026-02-19T20:14:04Z) - Digital quantum simulation of many-body systems: Making the most of intermediate-scale, noisy quantum computers [51.56484100374058]
This thesis is centered around simulating quantum dynamics on quantum devices.<n>We present an overview of the most relevant quantum algorithms for quantum dynamics.<n>We identify relevant problems within quantum dynamics that could benefit from quantum simulation in the near future.
arXiv Detail & Related papers (2025-08-29T10:37:19Z) - Sequential Quantum Computing [41.94295877935867]
We propose and experimentally demonstrate sequential quantum computing (SQC), a paradigm that utilizes multiple or heterogeneous quantum processors.<n>SQC overcomes the limitations of each type of quantum computer by combining their complementary strengths.<n>These results highlight SQC as a powerful and versatile approach for addressing complex quantum optimization problems.
arXiv Detail & Related papers (2025-06-25T17:51:29Z) - VQC-MLPNet: An Unconventional Hybrid Quantum-Classical Architecture for Scalable and Robust Quantum Machine Learning [60.996803677584424]
Variational Quantum Circuits (VQCs) offer a novel pathway for quantum machine learning.<n>Their practical application is hindered by inherent limitations such as constrained linear expressivity, optimization challenges, and acute sensitivity to quantum hardware noise.<n>This work introduces VQC-MLPNet, a scalable and robust hybrid quantum-classical architecture designed to overcome these obstacles.
arXiv Detail & Related papers (2025-06-12T01:38:15Z) - RhoDARTS: Differentiable Quantum Architecture Search with Density Matrix Simulations [44.13836547616739]
Variational Quantum Algorithms (VQAs) are a promising approach to leverage Noisy Intermediate-Scale Quantum (NISQ) computers.<n> choosing optimal quantum circuits that efficiently solve a given VQA problem is a non-trivial task.<n>Quantum Architecture Search (QAS) algorithms enable automatic generation of quantum circuits tailored to the provided problem.
arXiv Detail & Related papers (2025-06-04T08:30:35Z) - Quantum Data Centers in the Presence of Noise [0.0]
Single-processor monolithic quantum computers are affected by increased cross talk and difficulty of implementing gates when the number of qubits is increased.
In a QDC, multiple quantum processing units (QPUs) are linked together over short distances, allowing the total number of computational qubits to be increased without increasing the number of qubits on any one processor.
In doing so, the error incurred by operations at each QPU can be kept small, however additional noise will be added to the system due to the latency cost and errors incurred during inter-QPU entanglement distribution.
arXiv Detail & Related papers (2024-07-15T14:50:20Z) - Bias-field digitized counterdiabatic quantum optimization [39.58317527488534]
We call this protocol bias-field digitizeddiabatic quantum optimization (BF-DCQO)
Our purely quantum approach eliminates the dependency on classical variational quantum algorithms.
It achieves scaling improvements in ground state success probabilities, increasing by up to two orders of magnitude.
arXiv Detail & Related papers (2024-05-22T18:11:42Z) - Quantum Resonant Dimensionality Reduction and Its Application in Quantum Machine Learning [2.7119354495508787]
We propose a quantum resonant dimension reduction (QRDR) algorithm based on the quantum resonant transition to reduce the dimension of input data.
After QRDR, the dimension of input data $N$ can be reduced into desired scale $R$, and the effective information of the original data will be preserved.
Our algorithm has the potential to be utilized in a variety of computing fields.
arXiv Detail & Related papers (2024-05-21T09:26:18Z) - The impact of noise on the simulation of NMR spectroscopy on NISQ devices [0.1625256372381793]
We present a simulation of nuclear magnetic resonance (NMR) spectroscopy of small organic molecules on publicly available cloud quantum computers.<n>We analyze the impact of noise on the obtained NMR spectra, and we formulate an effective decoherence rate that quantifies the threshold noise that our proposed algorithm can tolerate.
arXiv Detail & Related papers (2024-04-29T17:40:06Z) - Enhancing Quantum Variational Algorithms with Zero Noise Extrapolation
via Neural Networks [0.4779196219827508]
Variational Quantum Eigensolver (VQE) is a promising algorithm for solving complex quantum problems.
The ubiquitous presence of noise in quantum devices often limits the accuracy and reliability of VQE outcomes.
This research introduces a novel approach by utilizing neural networks for zero noise extrapolation (ZNE) in VQE computations.
arXiv Detail & Related papers (2024-03-10T15:35:41Z) - 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) - Digital-Analog Quantum Computing and Algorithms [0.0]
The digital-analog quantum computing paradigm is promising for practical quantum computing applications.
Its ability to deliver greater accuracy in implementing quantum algorithms on real hardware is a significant advantage over traditional digital approaches.
arXiv Detail & Related papers (2024-01-19T10:54:40Z) - 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) - 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) - Optimal Stochastic Resource Allocation for Distributed Quantum Computing [50.809738453571015]
We propose a resource allocation scheme for distributed quantum computing (DQC) based on programming to minimize the total deployment cost for quantum resources.
The evaluation demonstrates the effectiveness and ability of the proposed scheme to balance the utilization of quantum computers and on-demand quantum computers.
arXiv Detail & Related papers (2022-09-16T02:37:32Z) - Quantum circuit architecture search for variational quantum algorithms [88.71725630554758]
We propose a resource and runtime efficient scheme termed quantum architecture search (QAS)
QAS automatically seeks a near-optimal ansatz to balance benefits and side-effects brought by adding more noisy quantum gates.
We implement QAS on both the numerical simulator and real quantum hardware, via the IBM cloud, to accomplish data classification and quantum chemistry tasks.
arXiv Detail & Related papers (2020-10-20T12:06:27Z) - An Application of Quantum Annealing Computing to Seismic Inversion [55.41644538483948]
We apply a quantum algorithm to a D-Wave quantum annealer to solve a small scale seismic inversions problem.
The accuracy achieved by the quantum computer is at least as good as that of the classical computer.
arXiv Detail & Related papers (2020-05-06T14:18:44Z) - Mitigating realistic noise in practical noisy intermediate-scale quantum
devices [0.5872014229110214]
Quantum error mitigation (QEM) is vital for noisy intermediate-scale quantum (NISQ) devices.
Most conventional QEM schemes assume discrete gate-based circuits with noise appearing either before or after each gate.
We show it can be effectively suppressed by a novel QEM method.
arXiv Detail & Related papers (2020-01-14T16:51:35Z)
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.