The fundamental problem in much of physics and quantum chemistry is to optimize a low-degree polynomial in certain anticommuting variables. Being a quantum mechanical problem, in many cases we do not know an efficient classical witness to the optimum, or even to an approximation of the optimum. One prominent exception is when the optimum is described by a so-called "Gaussian state", also called a free fermion state. In this work we are interested in the complexity of this optimization problem when no good Gaussian state exists. Our primary testbed is the Sachdev--Ye--Kitaev (SYK) model of random degree-$q$ polynomials, a model of great current interest in condensed matter physics and string theory, and one which has remarkable properties fr...
Fermionic Linear Optics (FLO) is a restricted model of quantum computation which in its original for...
Providing an optimal path to a quantum annealing algorithm is key to finding good approximate soluti...
The noncommutative sum-of-squares (ncSoS) hierarchy was introduced by Navascu\'{e}s-Pironio-Ac\'{i}n...
Quantum computers are expected to accelerate solving combinatorial optimization problems, including ...
We consider the problem of approximating the ground state energy of a fermionic Hamiltonian using a ...
Quantum computers promise to revolutionise electronic simulations by overcoming the exponential scal...
Achieving an accurate description of fermionic systems typically requires considerably many more orb...
Hamiltonian learning is crucial to the certification of quantum devices and quantum simulators. In t...
Gaussian fermionic matrix product states (GfMPS) form a class of ansatz quantum states for 1d system...
Semidefinite programs can be constructed to provide a non-perturbative view of the zero-temperature ...
Semidefinite programs can be constructed to provide a non-perturbative view of the zero-temperature ...
We provide a reusability report of the method presented by Chen et al. in "Optimizing quantum anneal...
We describe a quantum algorithm for finding the smallest eigenvalue of a Hermitian matrix. This algo...
Simulating fermionic systems on a quantum computer requires a high-performing mapping of fermionic s...
We consider the problem of approximating the ground state energy of a fermionic Hamiltonian using a ...
Fermionic Linear Optics (FLO) is a restricted model of quantum computation which in its original for...
Providing an optimal path to a quantum annealing algorithm is key to finding good approximate soluti...
The noncommutative sum-of-squares (ncSoS) hierarchy was introduced by Navascu\'{e}s-Pironio-Ac\'{i}n...
Quantum computers are expected to accelerate solving combinatorial optimization problems, including ...
We consider the problem of approximating the ground state energy of a fermionic Hamiltonian using a ...
Quantum computers promise to revolutionise electronic simulations by overcoming the exponential scal...
Achieving an accurate description of fermionic systems typically requires considerably many more orb...
Hamiltonian learning is crucial to the certification of quantum devices and quantum simulators. In t...
Gaussian fermionic matrix product states (GfMPS) form a class of ansatz quantum states for 1d system...
Semidefinite programs can be constructed to provide a non-perturbative view of the zero-temperature ...
Semidefinite programs can be constructed to provide a non-perturbative view of the zero-temperature ...
We provide a reusability report of the method presented by Chen et al. in "Optimizing quantum anneal...
We describe a quantum algorithm for finding the smallest eigenvalue of a Hermitian matrix. This algo...
Simulating fermionic systems on a quantum computer requires a high-performing mapping of fermionic s...
We consider the problem of approximating the ground state energy of a fermionic Hamiltonian using a ...
Fermionic Linear Optics (FLO) is a restricted model of quantum computation which in its original for...
Providing an optimal path to a quantum annealing algorithm is key to finding good approximate soluti...
The noncommutative sum-of-squares (ncSoS) hierarchy was introduced by Navascu\'{e}s-Pironio-Ac\'{i}n...