Model-Independent Error Mitigation in Parametric Quantum Circuits and
Depolarizing Projection of Quantum Noise
- URL: http://arxiv.org/abs/2111.15522v1
- Date: Tue, 30 Nov 2021 16:08:01 GMT
- Title: Model-Independent Error Mitigation in Parametric Quantum Circuits and
Depolarizing Projection of Quantum Noise
- Authors: Xiaoyang Wang, Xu Feng, Lena Funcke, Tobias Hartung, Karl Jansen,
Stefan K\"uhn, Georgios Polykratis and Paolo Stornati
- Abstract summary: 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.
- Score: 1.5162649964542718
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Finding ground states and low-lying excitations of a given Hamiltonian is one
of the most important problems in many fields of physics. As a novel approach,
quantum computing on Noisy Intermediate-Scale Quantum (NISQ) devices offers the
prospect to efficiently perform such computations and may eventually outperform
classical computers. However, current quantum devices still suffer from
inherent quantum noise. In this work, we propose an error mitigation scheme
suitable for parametric quantum circuits. This scheme is based on projecting a
general quantum noise channel onto depolarization errors. Our method can
efficiently reduce errors in quantum computations, which we demonstrate by
carrying out simulations both on classical and IBM's quantum devices. In
particular, we test the performance of the method by computing the mass gap of
the transverse-field Ising model using the variational quantum eigensolver
algorithm.
Related papers
- Benchmarking Variational Quantum Eigensolvers for Entanglement Detection in Many-Body Hamiltonian Ground States [37.69303106863453]
Variational quantum algorithms (VQAs) have emerged in recent years as a promise to obtain quantum advantage.
We use a specific class of VQA named variational quantum eigensolvers (VQEs) to benchmark them at entanglement witnessing and entangled ground state detection.
Quantum circuits whose structure is inspired by the Hamiltonian interactions presented better results on cost function estimation than problem-agnostic circuits.
arXiv Detail & Related papers (2024-07-05T12:06:40Z) - 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) - Practical limitations of quantum data propagation on noisy quantum processors [0.9362259192191963]
We show that owing to the noisy nature of current quantum processors, such a quantum algorithm will require single- and two-qubit gates with very low error probability to produce reliable results.
Specifically, we provide the upper bounds on how the relative error in variational parameters' propagation scales with the probability of noise in quantum hardware.
arXiv Detail & Related papers (2023-06-22T17:12:52Z) - Quantum Error Mitigation via Quantum-Noise-Effect Circuit Groups [0.0]
Near-term quantum computers are fragile against quantum noise effects.
Traditional quantum-error-correcting codes are not implemented on such devices.
We propose quantum error mitigation (QEM) scheme for quantum computational errors.
arXiv Detail & Related papers (2022-05-27T11:21:35Z) - An Introduction to Quantum Machine Learning for Engineers [36.18344598412261]
Quantum machine learning is emerging as a dominant paradigm to program gate-based quantum computers.
This book provides a self-contained introduction to quantum machine learning for an audience of engineers with a background in probability and linear algebra.
arXiv Detail & Related papers (2022-05-11T12:10:52Z) - Characterizing quantum instruments: from non-demolition measurements to
quantum error correction [48.43720700248091]
In quantum information processing quantum operations are often processed alongside measurements which result in classical data.
Non-unitary dynamical processes can take place on the system, for which common quantum channel descriptions fail to describe the time evolution.
Quantum measurements are correctly treated by means of so-called quantum instruments capturing both classical outputs and post-measurement quantum states.
arXiv Detail & Related papers (2021-10-13T18:00:13Z) - Quantum algorithms for quantum dynamics: A performance study on the
spin-boson model [68.8204255655161]
Quantum algorithms for quantum dynamics simulations are traditionally based on implementing a Trotter-approximation of the time-evolution operator.
variational quantum algorithms have become an indispensable alternative, enabling small-scale simulations on present-day hardware.
We show that, despite providing a clear reduction of quantum gate cost, the variational method in its current implementation is unlikely to lead to a quantum advantage.
arXiv Detail & Related papers (2021-08-09T18:00:05Z) - 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) - Neural Error Mitigation of Near-Term Quantum Simulations [0.0]
We introduce $textitneural error mitigation$, a novel method that uses neural networks to improve estimates of ground states and ground-state observables.
Our results show that neural error mitigation improves the numerical and experimental VQE computation to yield low-energy errors.
Our method is a promising strategy for extending the reach of near-term quantum computers to solve complex quantum simulation problems.
arXiv Detail & Related papers (2021-05-17T18:00:57Z) - Minimizing estimation runtime on noisy quantum computers [0.0]
"engineered likelihood function" (ELF) is used for carrying out Bayesian inference.
We show how the ELF formalism enhances the rate of information gain in sampling as the physical hardware transitions from the regime of noisy quantum computers.
This technique speeds up a central component of many quantum algorithms, with applications including chemistry, materials, finance, and beyond.
arXiv Detail & Related papers (2020-06-16T17:46:18Z)
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.