{"id":28744,"date":"2022-03-26T06:00:00","date_gmt":"2022-03-26T13:00:00","guid":{"rendered":"https:\/\/insidebigdata.com\/?p=28744"},"modified":"2022-03-27T11:28:05","modified_gmt":"2022-03-27T18:28:05","slug":"new-quantum-computing-research-shows-promising-path-to-commercialization","status":"publish","type":"post","link":"https:\/\/insidebigdata.com\/2022\/03\/26\/new-quantum-computing-research-shows-promising-path-to-commercialization\/","title":{"rendered":"New Quantum Computing Research Shows Promising Path to Commercialization"},"content":{"rendered":"\n

Findings confirm that the quality of quantum computing solutions have improved for practical use cases, an indication that the field is on a path toward commercialization.<\/em><\/p>\n\n\n\n

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Agnostiq, Inc.<\/a>, the quantum computing SaaS startup, announced its latest benchmark research which analyzed the state of quantum computing hardware to determine its current and future practicality as a mainstream solution. The findings<\/a> show that quantum computing hardware has improved over time and that application-specific benchmarks can serve as a more practical yardstick for comparing the capabilities of alternative types of quantum hardware.<\/p>\n\n\n\n

\u201cThis is clear evidence that things are heading in the right direction for quantum optimization,\u201d says Jack Baker, a quantum algorithms researcher at Aqnostiq. \u201cWith quantum hardware receiving increasing interest and investment every year, these performance increases are poised to accelerate,\u201d he added.<\/p><\/blockquote>\n\n\n\n

With the steady increase of quantum bits (qubits) made available by hardware providers in recent years, the relative performance and practical value of quantum computers has been difficult to assess. General benchmark studies have been conducted and deemed inconclusive, as they are not predictive of performance.<\/p>\n\n\n\n

Agnostiq utilized application-specific benchmarks and conducted its research using a portfolio optimization task to determine whether quantum computers have actually improved over time for specific use cases. The team discovered that the performance of gate-model quantum computers has improved in recent years for performing optimization problems, which is further indication the field is on a path towards commercialization. For more complex variations of the algorithm, the team also discovered that solution quality can improve as more quantum resources are added. This was a novel and previously unseen result. These findings should encourage organizations who depend on large scale optimization, simulation, or machine learning for mission critical tasks to invest in quantum computing technologies.<\/p>\n\n\n\n

Among the findings:<\/p>\n\n\n\n