Does provable absence of barren plateaus imply classical simulability? Or, why we need to rethink variational quantum computing
- URL: http://arxiv.org/abs/2312.09121v2
- Date: Tue, 19 Mar 2024 21:12:37 GMT
- Title: Does provable absence of barren plateaus imply classical simulability? Or, why we need to rethink variational quantum computing
- Authors: M. Cerezo, Martin Larocca, Diego García-Martín, N. L. Diaz, Paolo Braccia, Enrico Fontana, Manuel S. Rudolph, Pablo Bermejo, Aroosa Ijaz, Supanut Thanasilp, Eric R. Anschuetz, Zoë Holmes,
- Abstract summary: We ask: Can the structure that allows one to avoid barren plateaus also be leveraged to efficiently simulate the loss classically?
We present strong evidence that commonly used models with provable absence of barren plateaus are also classically simulable.
Our analysis sheds serious doubt on the non-classicality of the information processing capabilities of parametrized quantum circuits for barren plateau-free landscapes.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: A large amount of effort has recently been put into understanding the barren plateau phenomenon. In this perspective article, we face the increasingly loud elephant in the room and ask a question that has been hinted at by many but not explicitly addressed: Can the structure that allows one to avoid barren plateaus also be leveraged to efficiently simulate the loss classically? We present strong evidence that commonly used models with provable absence of barren plateaus are also classically simulable, provided that one can collect some classical data from quantum devices during an initial data acquisition phase. This follows from the observation that barren plateaus result from a curse of dimensionality, and that current approaches for solving them end up encoding the problem into some small, classically simulable, subspaces. Thus, while stressing quantum computers can be essential for collecting data, our analysis sheds serious doubt on the non-classicality of the information processing capabilities of parametrized quantum circuits for barren plateau-free landscapes. We end by discussing caveats in our arguments, the role of smart initializations and the possibility of provably superpolynomial, or simply practical, advantages from running parametrized quantum circuits.
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) - The hardness of quantum spin dynamics [1.1999555634662633]
We show that sampling from the output distribution generated by a wide class of quantum spin Hamiltonians is a hard problem for classical computers.
We estimate that an instance involving about 200 spins will be challenging for classical devices but feasible for intermediate-scale quantum computers with fault-tolerant qubits.
arXiv Detail & Related papers (2023-12-12T19:00:03Z) - 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 didactic approach to quantum machine learning with a single qubit [68.8204255655161]
We focus on the case of learning with a single qubit, using data re-uploading techniques.
We implement the different proposed formulations in toy and real-world datasets using the qiskit quantum computing SDK.
arXiv Detail & Related papers (2022-11-23T18:25:32Z) - Complexity-Theoretic Limitations on Quantum Algorithms for Topological
Data Analysis [59.545114016224254]
Quantum algorithms for topological data analysis seem to provide an exponential advantage over the best classical approach.
We show that the central task of TDA -- estimating Betti numbers -- is intractable even for quantum computers.
We argue that an exponential quantum advantage can be recovered if the input data is given as a specification of simplices.
arXiv Detail & Related papers (2022-09-28T17:53:25Z) - Anticipative measurements in hybrid quantum-classical computation [68.8204255655161]
We present an approach where the quantum computation is supplemented by a classical result.
Taking advantage of its anticipation also leads to a new type of quantum measurements, which we call anticipative.
In an anticipative quantum measurement the combination of the results from classical and quantum computations happens only in the end.
arXiv Detail & Related papers (2022-09-12T15:47:44Z) - Classical Splitting of Parametrized Quantum Circuits [0.0]
Barren plateaus appear to be a major obstacle to using variational quantum algorithms to simulate large-scale quantum systems.
We propose classical splitting of ans"atze or parametrized quantum circuits to avoid barren plateaus.
arXiv Detail & Related papers (2022-06-20T08:42:02Z) - Beyond Barren Plateaus: Quantum Variational Algorithms Are Swamped With
Traps [0.0]
We show that variational quantum models are untrainable if no good initial guess is known.
We also show that noisy variety of quantum models is impossible with a sub-exponential number of queries.
arXiv Detail & Related papers (2022-05-11T21:55:42Z) - 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) - Entanglement Devised Barren Plateau Mitigation [1.382143546774115]
We implicate random entanglement as the source of barren plateaus and characterize them in terms of many-body entanglement dynamics.
We propose and demonstrate a number of barren plateau ameliorating techniques.
We find that entanglement limiting, both automatic and engineered, is a hallmark of high-accuracy training.
arXiv Detail & Related papers (2020-12-22T17:49:38Z)
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.