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  3. Top quantum algorithms papers — Summer 2024 edition

October 01, 2024

Top quantum algorithms papers — Summer 2024 edition

Juan Miguel Arrazola

Juan Miguel Arrazola

Top quantum algorithms papers — Summer 2024 edition

In this blog post we share our favourite papers released in the third quarter of 2024. The selection is based on relevance to quantum algorithms and applications; these are results that we admire and that have been influential to our research. Xanadu papers won’t appear in the selection due to an obvious conflict of interest, but we take the opportunity to share our latest work at the end of this blog post.

Contents

  • The Top 5
    • 1. Fullerene-encapsulated Cyclic Ozone for the Next Generation of Nano-sized Propellants via Quantum Computation
    • 2. Optimization by Decoded Quantum Interferometry
    • 3. Lower T-count with faster algorithms
    • 4. Diagonalization of large many-body Hamiltonians on a quantum processor
    • 5. Experimental Quantum Simulation of Chemical Dynamics
  • Honourable mentions
  • Xanadu papers from summer 2024

The Top 5

1. Fullerene-encapsulated Cyclic Ozone for the Next Generation of Nano-sized Propellants via Quantum Computation

Image taken from the paper Fullerene-encapsulated Cyclic Ozone for the Next Generation of Nano-sized Propellants via Quantum Computation

A thorough and serious proposal for a new application of quantum computing to develop better rocket fuels.

2. Optimization by Decoded Quantum Interferometry

Image taken from the paper Optimization by Decoded Quantum Interferometry

Highly original idea proposing a new type of quantum algorithm for optimization based on interference (not Hamiltonians) for objective functions with a sparse Fourier spectrum.

3. Lower T-count with faster algorithms

Image taken from the paper Lower T-count with faster algorithms

Impressive work by a single author establishing what is likely the new state of the art for T-gate quantum compilation.

4. Diagonalization of large many-body Hamiltonians on a quantum processor

Image taken from the paper Diagonalization of large many-body Hamiltonians on a quantum processor

Combination of algorithms and experiments demonstrating the practical feasibility of Krylov subspace methods for up to 56-qubit systems.

5. Experimental Quantum Simulation of Chemical Dynamics

Image taken from the paper Experimental Quantum Simulation of Chemical Dynamics

Tour-de-force demonstration of how quantum analog simulators can be used to study photoinduced non-adiabatic dynamics of molecules.

Honourable mentions

  1. Unravelling quantum dynamics using flow equations

    Image taken from the paper Unravelling quantum dynamics using flow equations
    This is a classical algorithm, yet an intriguing new method for quantum dynamics simulations with impressive performance.

  2. Practical quantum advantage on partially fault-tolerant quantum computer

    Image taken from the paper Practical quantum advantage on partially fault-tolerant quantum computer
    A compelling case that massive savings in physical qubits and runtimes can be obtained through partial fault-tolerance.

  3. Efficient simulation of quantum chemistry problems in an enlarged basis set

    Image taken from the paper Efficient simulation of quantum chemistry problems in an enlarged basis set
    A potentially very useful technique of artificially enlarging a system to reduce the cost of Hamiltonian simulation.

Xanadu papers from summer 2024

Here we share our publications from this summer. You can find the full list on our website.

  • Geometric Quantum Machine Learning with Horizontal Quantum Gates

    Image taken from the paper Geometric Quantum Machine Learning with Horizontal Quantum Gates
    A new paradigm for the symmetry-informed construction of variational quantum circuits that relaxes the stringent requirement of equivariance.


  • Linear-optical quantum computation with arbitrary error-correcting codes

    Image taken from the paper Linear-optical quantum computation with arbitrary error-correcting codes
    A method for creating and measuring any target graph state for photonic measurement-based quantum computing.


  • Inference, interference and invariance: How the Quantum Fourier Transform can help to learn from data

    Image taken from the paper Inference, interference and invariance: How the Quantum Fourier Transform can help to learn from data
    This paper initiates a research program to answer the question of how a quantum computer’s access to information in Fourier space can help us learn from data.



We hope you enjoyed this selection of top papers. Stay tuned for the Fall 2024 edition! You can sign up to the Xanadu newsletter or follow PennyLane on LinkedIn or Twitter/X to get notified.

About the author

Juan Miguel Arrazola
Juan Miguel Arrazola

Juan Miguel Arrazola

Making quantum computers useful

Last modified: October 01, 2024

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