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IPFS News Link • Robots and Artificial Intelligence

DARPA challenges : describe limits of quantum computing and apply QC to improve machine learning

• by brian wang

The field of Quantum Computing (QC) has seen considerable progress in recent years, both in the number of qubits that can be physically realized, and in the formulation of new quantum search and optimization algorithms. However, numerous challenges remain to be solved to usefully employ QC to solve real-world problems. These include challenges of scale, environmental interactions, input/output, qubit connectivity, quantum memory (or lack thereof), quantum state preparation and readout, and numerous other practical and architectural challenges associated with interfacing to the classical world.

Richard Feynman's original vision for quantum computing sprang from the insight that there are hard problems, e.g. in quantum physics, quantum chemistry, and materials, that are nearly intractable using classical computation platforms but that might be successfully modeled using a universal quantum computer. DARPA seeks to challenge the community to address the fundamental limits of quantum computing and to identify where quantum computing can relevantly address hard science and technology problems, thus realizing Feynman's original vision. Both near-term (next few years) and longer term (next few decades) capabilities and their limitations are of interest.