Quantum subspace expansion in the presence of hardware noise
- URL: http://arxiv.org/abs/2404.09132v1
- Date: Sun, 14 Apr 2024 02:48:42 GMT
- Title: Quantum subspace expansion in the presence of hardware noise
- Authors: João C. Getelina, Prachi Sharma, Thomas Iadecola, Peter P. Orth, Yong-Xin Yao,
- Abstract summary: Finding ground state energies on current quantum processing units (QPUs) continues to pose challenges.
Hardware noise severely affects both the expressivity and trainability of parametrized quantum circuits.
We show how to integrate VQE with a quantum subspace expansion, allowing for an optimal balance between quantum and classical computing capabilities and costs.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Finding ground state energies on current quantum processing units (QPUs) using algorithms like the variational quantum eigensolver (VQE) continues to pose challenges. Hardware noise severely affects both the expressivity and trainability of parametrized quantum circuits, limiting them to shallow depths in practice. Here, we demonstrate that both issues can be addressed by synergistically integrating VQE with a quantum subspace expansion, allowing for an optimal balance between quantum and classical computing capabilities and costs. We perform a systematic benchmark analysis of the iterative quantum-assisted eigensolver of [K. Bharti and T. Haug, Phys. Rev. A {\bf 104}, L050401 (2021)] in the presence of hardware noise. We determine ground state energies of 1D and 2D mixed-field Ising spin models on noisy simulators and on the IBM QPUs ibmq_quito (5 qubits) and ibmq_guadalupe (16 qubits). To maximize accuracy, we propose a suitable criterion to select the subspace basis vectors according to the trace of the noisy overlap matrix. Finally, we show how to systematically approach the exact solution by performing controlled quantum error mitigation based on probabilistic error reduction on the noisy backend fake_guadalupe.
Related papers
- Extending Quantum Perceptrons: Rydberg Devices, Multi-Class Classification, and Error Tolerance [67.77677387243135]
Quantum Neuromorphic Computing (QNC) merges quantum computation with neural computation to create scalable, noise-resilient algorithms for quantum machine learning (QML)
At the core of QNC is the quantum perceptron (QP), which leverages the analog dynamics of interacting qubits to enable universal quantum computation.
arXiv Detail & Related papers (2024-11-13T23:56:20Z) - Mitigating Errors on Superconducting Quantum Processors through Fuzzy
Clustering [38.02852247910155]
A new Quantum Error Mitigation (QEM) technique uses Fuzzy C-Means clustering to specifically identify measurement error patterns.
We report a proof-of-principle validation of the technique on a 2-qubit register, obtained as a subset of a real NISQ 5-qubit superconducting quantum processor.
We demonstrate that the FCM-based QEM technique allows for reasonable improvement of the expectation values of single- and two-qubit gates based quantum circuits.
arXiv Detail & Related papers (2024-02-02T14:02:45Z) - 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) - Variational Denoising for Variational Quantum Eigensolver [0.28675177318965045]
The variational quantum eigensolver (VQE) is a hybrid algorithm that has the potential to provide a quantum advantage in practical chemistry problems.
VQE faces challenges in task-specific design and machine-specific architecture, particularly when running on noisy quantum devices.
We propose variational denoising, an unsupervised learning method that employs a parameterized quantum neural network to improve the solution of VQE.
arXiv Detail & Related papers (2023-04-02T14:56:15Z) - Noise-robust ground state energy estimates from deep quantum circuits [0.0]
We show how the underlying energy estimate explicitly filters out incoherent noise in quantum algorithms.
We implement QCM for a model of quantum magnetism on IBM Quantum hardware.
We find that QCM maintains a remarkably high degree of error robustness where VQE completely fails.
arXiv Detail & Related papers (2022-11-16T09:12:55Z) - Model-Independent Error Mitigation in Parametric Quantum Circuits and
Depolarizing Projection of Quantum Noise [1.5162649964542718]
Finding ground states and low-lying excitations of a given Hamiltonian is one of the most important problems in many fields of physics.
quantum computing on Noisy Intermediate-Scale Quantum (NISQ) devices offers the prospect to efficiently perform such computations.
Current quantum devices still suffer from inherent quantum noise.
arXiv Detail & Related papers (2021-11-30T16:08:01Z) - Variational quantum eigensolver for the Heisenberg antiferromagnet on
the kagome lattice [0.0]
We give a detailed proposal for a Vari Quantum Eigensolver (VQE) intending to solve a quantum problem on a quantum computer.
We emulate noiseless and noisy quantum computers with either 2D-grid or all-to-all connectivity and simulate patches of the kagome HAFM of up to 20 sites.
VQEs for the HAFM on any graph can inherently perform their quantum computations in a decoherence-free subspace that protects against collective longitudinal and collective noise.
arXiv Detail & Related papers (2021-08-04T17:03:42Z) - Adiabatic Quantum Graph Matching with Permutation Matrix Constraints [75.88678895180189]
Matching problems on 3D shapes and images are frequently formulated as quadratic assignment problems (QAPs) with permutation matrix constraints, which are NP-hard.
We propose several reformulations of QAPs as unconstrained problems suitable for efficient execution on quantum hardware.
The proposed algorithm has the potential to scale to higher dimensions on future quantum computing architectures.
arXiv Detail & Related papers (2021-07-08T17:59:55Z) - Variational Quantum Eigensolver for SU($N$) Fermions [0.0]
Variational quantum algorithms aim at harnessing the power of noisy intermediate-scale quantum computers.
We apply the variational quantum eigensolver to study the ground-state properties of $N$-component fermions.
Our approach lays out the basis for a current-based quantum simulator of many-body systems.
arXiv Detail & Related papers (2021-06-29T16:39:30Z) - Pulse-level noisy quantum circuits with QuTiP [53.356579534933765]
We introduce new tools in qutip-qip, QuTiP's quantum information processing package.
These tools simulate quantum circuits at the pulse level, leveraging QuTiP's quantum dynamics solvers and control optimization features.
We show how quantum circuits can be compiled on simulated processors, with control pulses acting on a target Hamiltonian.
arXiv Detail & Related papers (2021-05-20T17:06:52Z) - 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)
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.