Works Published in:

Nature Physics

Unravelling quantum dynamics using flow equations (July 2024)

Steven J. Thomson & J. Eisert

DOI: 10.1038/s41567-024-02549-2

The paper proposes a radically new method for classically simulating non-equilibrium phenomena in solid-state systems. This new combined numerical approach enables researchers to make accurate predictions of the long-term properties of one- and two-dimensional quantum systems.

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Exponentially tighter bounds on limitations of quantum error mitigation (July 2024)

Yihui Quek, Daniel Stilck França, Sumeet Khatri, Johannes Jakob Meyer & Jens Eisert

DOI: 10.1038/s41567-024-02536-7

The authors address a key challenge in quantum computing: dealing with errors in noisy quantum systems. They demonstrate that even with improved error mitigation techniques, fundamental limitations persist. The study shows that error mitigation does not work as a scalable scheme: While quantum error mitigation can be useful, it cannot fully replace quantum error correction codes.

As error mitigation is strategically used by many IT companies, this publication sparks a discussion on imperfect quantum computers and contributes to the understanding of the balance between error mitigation and the required computational effort.

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Article on PhysOrg

Nature Communications

Probing coherent quantum thermodynamics using a trapped ion (August 2024)

O. Onishchenko, G. Guarnieri, P. Rosillo-Rodes, D. Pijn, J. Hilder, U. G. Poschinger, M. Perarnau-Llobet, J. Eisert, F. Schmidt-Kaler

DOI: 10.1038/s41467-024-51263-3

The work shows that quantum mechanics is essential for understanding thermodynamics.

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Physical Review Letters

Semi-Device-Independently Characterizing Quantum Temporal Correlation (May 2024)

Shin-Liang Chen & Jens Eisert

DOI: 10.1103/PhysRevLett.132.220201

The authors present a framework for characterizing quantum temporal correlations in a general temporal scenario, in which an initial quantum state is measured, sent through a quantum channel, and finally measured again. The framework serves as a natural tool for quantum certification in a temporal scenario when the quantum devices involved are uncharacterized or partially characterized.

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Shallow shadows: Expectation estimation using low-depth random Clifford circuits (July 2024)

Christian Bertoni, Jonas Haferkamp, Marcel Hinsche, Marios Ioannou, Jens Eisert & Hakop Pashayan

DOI: 10.1103/PhysRevLett.133.020602

In the publication, the authors provide practical and powerful schemes for learning properties of a quantum state using a small number of measurements. Specifically, they present a randomized measurement scheme modulated by the depth of a random quantum circuit in one spatial dimension.

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Nature Communications

Robustly learning the Hamiltonian dynamics of a superconducting quantum processor (Pre-Print)

Dominik Hangleiter, Ingo Roth, Jonas Fuksa, Jens Eisert & Pedram Roushan

DOI: 10.48550/arXiv.2108.08319

The quantum physicists at Freie Universität Berlin collaborated with the Google Quantum AI team to calibrate the Google Sycamore quantum chip.

The scientists used measured time series to robustly estimate the free Hamiltonian parameters in the superconducting qubit quantum processor, minimizing noise and state-preparation and measurement errors. They developed a new technique called tensorESPRIT to extract frequencies from matrix time series and combined it with optimized algorithms to precisely identify the Hamiltonian parameters for up to 14 coupled superconducting qubits.

The publication describes a new tool for improving and calibrating analog quantum processors.

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Verifiable measurement-based quantum random sampling with trapped ions

Martin Ringbauer, Marcel Hinsche, Thomas Feldker, Paul K. Faehrmann, Juani Bermejo-Vega, Claire Edmunds, Lukas Postler, Roman Stricker, Christian D. Marciniak, Michael Meth, Ivan Pogorelov, Rainer Blatt, Philipp Schindler, Jens Eisert, Thomas Monz&  Dominik Hangleiter

DOI: 10.48550/arXiv.2307.14424

Quantum computers already offer quantum advantages on paradigmatic quantum advantages: For those problems – albeit not yet of practical relevance – existing quantum hardware already outperforms the fastest classical supercomputers. That said, the verification of such sampling tasks remains a challenge. In this work, we show how to verify the correct functioning of quantum sampling experiments, and showcase the functioning of the approach at hand of state-of-the-art data from a trapped ion quantum processor.

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