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A variational eigenvalue solver on a photonic quantum processor
The proposed approach drastically reduces the coherence time requirements and combines this method with a new approach to state preparation based on ansätze and classical optimization, enhancing the potential of quantum resources available today and in the near future. Expand
Quantum supremacy using a programmable superconducting processor
Quantum supremacy is demonstrated using a programmable superconducting processor known as Sycamore, taking approximately 200 seconds to sample one instance of a quantum circuit a million times, which would take a state-of-the-art supercomputer around ten thousand years to compute. Expand
Barren plateaus in quantum neural network training landscapes
It is shown that for a wide class of reasonable parameterized quantum circuits, the probability that the gradient along any reasonable direction is non-zero to some fixed precision is exponentially small as a function of the number of qubits. Expand
Scalable Quantum Simulation of Molecular Energies
We report the first electronic structure calculation performed on a quantum computer without exponentially costly precompilation. We use a programmable array of superconducting qubits to compute theExpand
OpenFermion: the electronic structure package for quantum computers
The key motivations behind design choices in OpenFermion are outlined and some basic OpenFermanion functionality is discussed which are believed to aid the community in the development of better quantum algorithms and tools for this exciting area of research. Expand
Quantum approximate optimization of non-planar graph problems on a planar superconducting processor
The application of the Google Sycamore superconducting qubit quantum processor to combinatorial optimization problems with the quantum approximate optimization algorithm (QAOA) is demonstrated and an approximation ratio is obtained that is independent of problem size and for the first time, that performance increases with circuit depth. Expand
Encoding Electronic Spectra in Quantum Circuits with Linear T Complexity
We construct quantum circuits which exactly encode the spectra of correlated electron models up to errors from rotation synthesis. By invoking these circuits as oracles within the recently introducedExpand
The theory of variational hybrid quantum-classical algorithms
This work develops a variational adiabatic ansatz and explores unitary coupled cluster where it is shown how the use of modern derivative free optimization techniques can offer dramatic computational savings of up to three orders of magnitude over previously used optimization techniques. Expand
TensorFlow Quantum: A Software Framework for Quantum Machine Learning
This framework offers high-level abstractions for the design and training of both discriminative and generative quantum models under TensorFlow and supports high-performance quantum circuit simulators. Expand
Strategies for quantum computing molecular energies using the unitary coupled cluster ansatz
The variational quantum eigensolver (VQE) algorithm combines the ability of quantum computers to efficiently compute expectation values with a classical optimization routine in order to approximateExpand