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Articles by Bryce Fuller
Improved quantum computation using operator backpropagation - npj Quantum Information
Abstract Decoherence of quantum hardware is currently limiting its practical applications. At the same time, classical algorithms for simulating quantum circuits have progressed substantially. Here, we demonstrate a hybrid framework that integrates classical simulations with quantum hardware to improve the computation of an observable’s expectation value by reducing the quantum circuit depth.
Adiabatic state preparation from general initial states
Advanced search Molecular Physics An International Journal at the Interface Between Chemistry and Physics Latest Articles Submit an article Journal homepage Full Article Figures & data References Citations Metrics Reprints & Permissions Read this article /doi/full/10.1080/00268976.2025.2526723?needAccess=true Abstract A variety of quantum computing algorithms exist for the preparation of approximate Hamiltonian ground states.
Krylov diagonalization of large many-body Hamiltonians on a quantum processor - Nature Communications
Abstract The estimation of low energies of many-body systems is a cornerstone of the computational quantum sciences. Variational quantum algorithms can be used to prepare ground states on pre-fault-tolerant quantum processors, but their lack of convergence guarantees and impractical number of cost function estimations prevent systematic scaling of experiments to large systems. Alternatives to variational approaches are needed for large-scale experiments on pre-fault-tolerant devices.
Challenges and opportunities in quantum optimization - Nature Reviews Physics
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Abstract Quantum computers have demonstrable ability to solve problems at a scale beyond brute-force classical simulation. Interest in quantum algorithms has developed in many areas, particularly in relation to mathematical optimization — a broad field with links to computer science and physics. In this Review, we aim to give an overview of quantum optimization.
By Amira Abbas, Andreas Bärtschi, Carleton Coffrin, Giorgio Cortiana, Vedran Dunjko, Daniel Egger, Nicola R. Franco, Filippo Fratini, Bryce Fuller, Julien Gacon, Sander Gribling, Stuart Hadfield, Raoul Heese, Thorsten Koch, Georgios Korpas, Steve Lenk, Jakub Marecek, Guglielmo Mazzola, Giacomo Nannicini, Sebastian Pokutta, Emre Şahin, Sabine Tornow, Sergiy Zhuk, Gerhard Kircher, Thomas Kleinert, Stefano Mensa, Naeimeh Mohseni, Corey O’Meara, Manuel Proissl, Sheir Yarkoni
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Nature
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[2304.09899] Quantum Kernel Alignment with Stochastic Gradient Descent
Quantum support vector machines have the potential to achieve a quantum speedup for solving certain machine learning problems. The key challenge for doing so is finding good quantum kernels for a given data set -- a task called kernel alignment.
Second-Quantized Fermionic Operators with Polylogarithmic Qubit and Gate Complexity
Abstract We present a method for encoding second-quantized fermionic systems in qubits when the number of fermions is conserved, as in the electronic structure problem. When the number F of fermions is much smaller than the number M of modes, this symmetry reduces the number of information-theoretically required qubits from Θ(M) to O(FlogM).
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