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Delineating the boundary between the computational power of quantum and classical devices

CORDIS · observation · Publication date unknown

th potentially game-changing applications emerging for learning tasks. To achieve this goal, it digs deeply into computer science that provides sophisticated tools of computational complexity and of machine learning, and is instrumental in devising methods for the classical simulation of intricate quantum problems. At the same time, it draws on the physics of complex systems. This proposal suggests an interdisciplinary effort by bringing together ideas of quantum information, condensed matter physics, complexity theory, machine learning, tensor network theory, and methods that are unusual in this context such as signal processing. Individually, each objective substantially advances the respective field, but it is their combination that will permit a true breakthrough by delineating the delicate boundary between quantum and classical computations of synthetic quantum devices. Near-term quantum devices, quantum simulators, computational complexity, verification and benchmarking, classical simulation, quantum machine learning, tensor networks, sparse sampling, compressed sensing

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recordType

award

status

SIGNED

region

EU

value

1807721

unit

EUR