The quantum ecosystem is evolving rapidly with industry and researchers working collaboratively to advance quantum capabilities. Consider approaching a quantum startup for help looking for solutions to these problems.Ask engineers about computational bottlenecks that, if resolved, would translate into a win of potentially huge value. Start looking for high-value problems to solve.Compile a list of problems and start building a roadmap for adoption as machines become more powerful.
Create a budget and task them to research use cases or proof of concepts with problems that are difficult to solve on a classical computer. Hire or train a quantum expert for your organization and begin assembling a small in-house team.
Here are the first steps to consider when embarking on a path to quantum computing exploration:
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How To Drive Quantum Adoption In Your Company We are still discovering the potential of quantum computing and can run into solutions for problems we hadn’t initially set out to solve. Do not simply try to accelerate a pre-existing solution, but find an entirely new way of approaching the problem itself. You may want to achieve a reduction in computational time and resources, better accuracy or a combination of each. If you want to see an immediate benefit from quantum applications, invest your time in finding a suitable business problem, as today’s quantum computers can only provide value for some specific problems. Once you have identified a suitable problem, how do you match your problem to a quantum solution? Problems we can only solve through brute-force computations on classical devices are ideal targets for quantum computing applications. Finally, the problem should be difficult to solve on a classical computer. You should limit your search to problems with small inputs or dimensions, as current quantum computers have limited resources. Matching Quantum Solutions To Your ProblemĬertain kinds of problems are particularly well-suited for quantum computing, and organizations should focus on high-value problems that need to be solved frequently. This will offer solutions for personalized medicine, autonomous vehicles and enable the decryption of common encryption algorithms. In the long-term, perhaps 10 years from now, limitations due to noise and lack of resources will have been largely resolved and quantum computing will be used habitually for complex unsupervised machine learning problems. They will also enhance Monte Carlo applications used for risk simulations and solving pricing problems. They will enable advancements in battery technology and organic light-emitting diodes (OLED), help solve partial differentiated equations (PDE) that can improve weather forecasting, and facilitate the understanding of fluid dynamics. In the mid-term, the best applications will become disruptive for complex chemistry simulations, enabling new material and drug discoveries through the modeling and analysis of more complex molecules and compounds. Still limited in the number of entangled qubits, the NISQ computers will be suitable in the near-term for optimization tasks as well as many supervised machine learning tasks that offer benefits in such areas as vehicle routing, securities trading, image recognition tasks and medical image processing. The suitability of specific quantum computing hardware and applications depends on the business problem you are trying to solve.Ĭurrently, quantum-inspired algorithms are being used on classical computers to accelerate complex simulation, machine learning and optimization tasks. There are no one-size-fits-all solutions. Best Problems To Resolve With Quantum Computing
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