Doubling the size of quantum simulators by entanglement forging
- URL: http://arxiv.org/abs/2104.10220v1
- Date: Tue, 20 Apr 2021 19:32:37 GMT
- Title: Doubling the size of quantum simulators by entanglement forging
- Authors: Andrew Eddins, Mario Motta, Tanvi P. Gujarati, Sergey Bravyi, Antonio
Mezzacapo, Charles Hadfield, Sarah Sheldon
- Abstract summary: Quantum computers are promising for simulations of chemical and physical systems.
We present a method, classical entanglement forging, that harnesses classical resources to capture quantum correlations.
We compute the ground state energy of a water molecule in the most accurate simulation to date.
- Score: 2.309018557701645
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Quantum computers are promising for simulations of chemical and physical
systems, but the limited capabilities of today's quantum processors permit only
small, and often approximate, simulations. Here we present a method, classical
entanglement forging, that harnesses classical resources to capture quantum
correlations and double the size of the system that can be simulated on quantum
hardware. Shifting some of the computation to classical post-processing allows
us to represent ten spin-orbitals on five qubits of an IBM Quantum processor to
compute the ground state energy of the water molecule in the most accurate
simulation to date. We discuss conditions for applicability of classical
entanglement forging and present a roadmap for scaling to larger problems.
Related papers
- Efficient Learning for Linear Properties of Bounded-Gate Quantum Circuits [63.733312560668274]
Given a quantum circuit containing d tunable RZ gates and G-d Clifford gates, can a learner perform purely classical inference to efficiently predict its linear properties?
We prove that the sample complexity scaling linearly in d is necessary and sufficient to achieve a small prediction error, while the corresponding computational complexity may scale exponentially in d.
We devise a kernel-based learning model capable of trading off prediction error and computational complexity, transitioning from exponential to scaling in many practical settings.
arXiv Detail & Related papers (2024-08-22T08:21:28Z) - Computational supremacy in quantum simulation [22.596358764113624]
We show that superconducting quantum annealing processors can generate samples in close agreement with solutions of the Schr"odinger equation.
We conclude that no known approach can achieve the same accuracy as the quantum annealer within a reasonable timeframe.
arXiv Detail & Related papers (2024-03-01T19:00:04Z) - 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) - Quantum data learning for quantum simulations in high-energy physics [55.41644538483948]
We explore the applicability of quantum-data learning to practical problems in high-energy physics.
We make use of ansatz based on quantum convolutional neural networks and numerically show that it is capable of recognizing quantum phases of ground states.
The observation of non-trivial learning properties demonstrated in these benchmarks will motivate further exploration of the quantum-data learning architecture in high-energy physics.
arXiv Detail & Related papers (2023-06-29T18:00:01Z) - A Herculean task: Classical simulation of quantum computers [4.12322586444862]
This work reviews the state-of-the-art numerical simulation methods that emulate quantum computer evolution under specific operations.
We focus on the mainstream state-vector and tensor-network paradigms while briefly mentioning alternative methods.
arXiv Detail & Related papers (2023-02-17T13:59:53Z) - Quantum Machine Learning: from physics to software engineering [58.720142291102135]
We show how classical machine learning approach can help improve the facilities of quantum computers.
We discuss how quantum algorithms and quantum computers may be useful for solving classical machine learning tasks.
arXiv Detail & Related papers (2023-01-04T23:37:45Z) - Differentiable matrix product states for simulating variational quantum
computational chemistry [6.954927515599816]
We propose a parallelizable classical simulator for variational quantum eigensolver(VQE)
Our simulator seamlessly integrates the quantum circuit evolution into the classical auto-differentiation framework.
As applications, we use our simulator to study commonly used small molecules such as HF, LiH and H$$O, as well as larger molecules CO$$, BeH$ and H$_4$ with up to $40$ qubits.
arXiv Detail & Related papers (2022-11-15T08:36:26Z) - A Scalable Approach to Quantum Simulation via Projection-based Embedding [0.0]
We describe a new and chemically intuitive approach that permits a subdomain of the electronic structure of a molecule to be calculated accurately on a quantum device.
We demonstrate that our method produces improved results for molecules that cannot be simulated fully on quantum computers but which can be resolved classically at a lower level of approximation.
arXiv Detail & Related papers (2022-03-02T14:27:44Z) - Recompilation-enhanced simulation of electron-phonon dynamics on IBM
Quantum computers [62.997667081978825]
We consider the absolute resource cost for gate-based quantum simulation of small electron-phonon systems.
We perform experiments on IBM quantum hardware for both weak and strong electron-phonon coupling.
Despite significant device noise, through the use of approximate circuit recompilation we obtain electron-phonon dynamics on current quantum computers comparable to exact diagonalisation.
arXiv Detail & Related papers (2022-02-16T19:00:00Z) - Holographic dynamics simulations with a trapped ion quantum computer [0.0]
We demonstrate and benchmark a new scalable quantum simulation paradigm.
Using a Honeywell trapped ion quantum processor, we simulate the non-integrable dynamics of the self-dual kicked Ising model.
Results suggest that quantum tensor network methods, together with state-of-the-art quantum processor capabilities, enable a viable path to practical quantum advantage in the near term.
arXiv Detail & Related papers (2021-05-19T18:00:02Z) - Error mitigation and quantum-assisted simulation in the error corrected
regime [77.34726150561087]
A standard approach to quantum computing is based on the idea of promoting a classically simulable and fault-tolerant set of operations.
We show how the addition of noisy magic resources allows one to boost classical quasiprobability simulations of a quantum circuit.
arXiv Detail & Related papers (2021-03-12T20:58:41Z)
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.