Information Scrambling in Computationally Complex Quantum Circuits
- URL: http://arxiv.org/abs/2101.08870v1
- Date: Thu, 21 Jan 2021 22:18:49 GMT
- Title: Information Scrambling in Computationally Complex Quantum Circuits
- Authors: Xiao Mi, Pedram Roushan, Chris Quintana, Salvatore Mandra, Jeffrey
Marshall, Charles Neill, Frank Arute, Kunal Arya, Juan Atalaya, Ryan Babbush,
Joseph C. Bardin, Rami Barends, Andreas Bengtsson, Sergio Boixo, Alexandre
Bourassa, Michael Broughton, Bob B. Buckley, David A. Buell, Brian Burkett,
Nicholas Bushnell, Zijun Chen, Benjamin Chiaro, Roberto Collins, William
Courtney, Sean Demura, Alan R. Derk, Andrew Dunsworth, Daniel Eppens,
Catherine Erickson, Edward Farhi, Austin G. Fowler, Brooks Foxen, Craig
Gidney, Marissa Giustina, Jonathan A. Gross, Matthew P. Harrigan, Sean D.
Harrington, Jeremy Hilton, Alan Ho, Sabrina Hong, Trent Huang, William J.
Huggins, L. B. Ioffe, Sergei V. Isakov, Evan Jeffrey, Zhang Jiang, Cody
Jones, Dvir Kafri, Julian Kelly, Seon Kim, Alexei Kitaev, Paul V. Klimov,
Alexander N. Korotkov, Fedor Kostritsa, David Landhuis, Pavel Laptev, Erik
Lucero, Orion Martin, Jarrod R. McClean, Trevor McCourt, Matt McEwen, Anthony
Megrant, Kevin C. Miao, Masoud Mohseni, Wojciech Mruczkiewicz, Josh Mutus,
Ofer Naaman, Matthew Neeley, Michael Newman, Murphy Yuezhen Niu, Thomas E.
O'Brien, Alex Opremcak, Eric Ostby, Balint Pato, Andre Petukhov, Nicholas
Redd, Nicholas C. Rubin, Daniel Sank, Kevin J. Satzinger, Vladimir Shvarts,
Doug Strain, Marco Szalay, Matthew D. Trevithick, Benjamin Villalonga,
Theodore White, Z. Jamie Yao, Ping Yeh, Adam Zalcman, Hartmut Neven, Igor
Aleiner, Kostyantyn Kechedzhi, Vadim Smelyanskiy, Yu Chen
- Abstract summary: We experimentally investigate the dynamics of quantum scrambling on a 53-qubit quantum processor.
We show that while operator spreading is captured by an efficient classical model, operator entanglement requires exponentially scaled computational resources to simulate.
- Score: 56.22772134614514
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Interaction in quantum systems can spread initially localized quantum
information into the many degrees of freedom of the entire system.
Understanding this process, known as quantum scrambling, is the key to
resolving various conundrums in physics. Here, by measuring the time-dependent
evolution and fluctuation of out-of-time-order correlators, we experimentally
investigate the dynamics of quantum scrambling on a 53-qubit quantum processor.
We engineer quantum circuits that distinguish the two mechanisms associated
with quantum scrambling, operator spreading and operator entanglement, and
experimentally observe their respective signatures. We show that while operator
spreading is captured by an efficient classical model, operator entanglement
requires exponentially scaled computational resources to simulate. These
results open the path to studying complex and practically relevant physical
observables with near-term quantum processors.
Related papers
- Dynamical simulations of many-body quantum chaos on a quantum computer [3.731709137507907]
We study a class of maximally chaotic circuits known as dual unitary circuits.
We show that a superconducting quantum processor with 91 qubits is able to accurately simulate these correlators.
We then probe dynamics beyond exact verification, by perturbing the circuits away from the dual unitary point.
arXiv Detail & Related papers (2024-11-01T17:57:13Z) - 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) - The curse of random quantum data [62.24825255497622]
We quantify the performances of quantum machine learning in the landscape of quantum data.
We find that the training efficiency and generalization capabilities in quantum machine learning will be exponentially suppressed with the increase in qubits.
Our findings apply to both the quantum kernel method and the large-width limit of quantum neural networks.
arXiv Detail & Related papers (2024-08-19T12:18:07Z) - 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) - Measurement-induced entanglement and teleportation on a noisy quantum
processor [105.44548669906976]
We investigate measurement-induced quantum information phases on up to 70 superconducting qubits.
We use a duality mapping, to avoid mid-circuit measurement and access different manifestations of the underlying phases.
Our work demonstrates an approach to realize measurement-induced physics at scales that are at the limits of current NISQ processors.
arXiv Detail & Related papers (2023-03-08T18:41:53Z) - Quantifying information scrambling via Classical Shadow Tomography on
Programmable Quantum Simulators [0.0]
We develop techniques to probe the dynamics of quantum information, and implement them experimentally on an IBM superconducting quantum processor.
We identify two unambiguous signatures of quantum information scrambling, neither of which can be mimicked by dissipative processes.
We measure both signatures, and support our results with numerical simulations of the quantum system.
arXiv Detail & Related papers (2022-02-10T16:36:52Z) - Probing quantum information propagation with out-of-time-ordered
correlators [41.12790913835594]
Small-scale quantum information processors hold the promise to efficiently emulate many-body quantum systems.
Here, we demonstrate the measurement of out-of-time-ordered correlators (OTOCs)
A central requirement for our experiments is the ability to coherently reverse time evolution.
arXiv Detail & Related papers (2021-02-23T15:29:08Z) - Quantum Phases of Matter on a 256-Atom Programmable Quantum Simulator [41.74498230885008]
We demonstrate a programmable quantum simulator based on deterministically prepared two-dimensional arrays of neutral atoms.
We benchmark the system by creating and characterizing high-fidelity antiferromagnetically ordered states.
We then create and study several new quantum phases that arise from the interplay between interactions and coherent laser excitation.
arXiv Detail & Related papers (2020-12-22T19:00:04Z) - Quantum information spreading in a disordered quantum walk [50.591267188664666]
We design a quantum probing protocol using Quantum Walks to investigate the Quantum Information spreading pattern.
We focus on the coherent static and dynamic disorder to investigate anomalous and classical transport.
Our results show that a Quantum Walk can be considered as a readout device of information about defects and perturbations occurring in complex networks.
arXiv Detail & Related papers (2020-10-20T20:03:19Z) - Quantum Information Scrambling in a Superconducting Qutrit Processor [0.0]
Delocalization of quantum information in strongly-interacting many-body systems has recently begun to unite our understanding of black hole dynamics, transport in exotic non-Fermi liquids, and many-body analogs of quantum chaos.
We implement two-qutrit scrambling operations and embed them in a five-qutrit teleportation algorithm to measure the associated out-time-ordered correlation functions.
arXiv Detail & Related papers (2020-03-06T16:36:23Z)
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.