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IBM quantum computer takes the next step towards moving beyond classical supercomputing architecture.

IBM quantum computing computer

IBM has announced a significant advance in quantum computing, which has been published in the scientific journal Nature. The study shows that quantum computers can produce precise results, even at a scale of over 100 qubits, which surpasses classical computing methods.

One of the main goals of quantum computing is to simulate material components that classical computers cannot efficiently simulate. This is a necessary step towards solving problems like designing more efficient fertilizers, creating better batteries, and producing new medicines. However, current quantum systems are inherently noisy and produce many errors due to the fragile nature of qubits and their environment.

The IBM team’s experiment shows that quantum computers can outperform classical simulations by learning and correcting errors in the system. They used the IBM Quantum ‘Eagle’ quantum processor, which has 127 superconducting qubits, to generate entangled states and simulate the dynamics of spins in a material model. These simulations accurately predicted properties like magnetization.

To verify the accuracy of these simulations, scientists at UC Berkeley performed the same simulations on advanced classical computers located at Lawrence Berkeley National Lab’s National Energy Research Scientific Computing Center (NERSC) and Purdue University. As the model grew larger, the quantum computer continued to produce accurate results with the help of error mitigation techniques, while the classical computing methods eventually faltered and could not match the IBM Quantum system.

To learn more about the details of the demonstration and the results, read the IBM Research blog.

IBM Commits to Utility-Scale Processors Across IBM Quantum Systems

IBM has completed groundbreaking work that will power its IBM Quantum systems. These systems will run on both the cloud and on-site at partner locations and will be powered by a minimum of 127 qubits. The processors provide access to computational power that is larger than classical methods for certain applications. Furthermore, these systems will offer better coherence times and lower error rates compared to previous IBM quantum systems. By combining these capabilities with continuously advancing error mitigation techniques, IBM Quantum systems can now meet a new threshold for the industry, which IBM has termed ‘utility-scale.’ At this point, quantum computers can serve as scientific tools to explore a new scale of problems that classical systems may never be able to solve.

IBM is expanding its quantum technology stack, and research institutions and private-sector leaders are mobilizing across industries for which quantum holds immediate potential. With more powerful quantum technology, including advanced hardware and tools to explore how error mitigation can enable accuracy today, pioneering organizations and universities are working with IBM to advance the value of quantum computing.

The working groups that are exploring the potential value of quantum computing include:

  • Healthcare and Life Sciences: led by organizations such as Cleveland Clinic and Moderna, they are exploring applications of quantum chemistry and quantum machine learning to challenges such as accelerated molecular discovery and patient risk prediction models.
  • High Energy Physics: comprised of groundbreaking research institutions such as CERN and DESY, they are working to identify the best-suited quantum calculations, for areas such as identification and reconstruction algorithms for particle collision events, and the investigation of theoretical models for high energy physics.
  • Materials: spearheaded by the teams at Boeing, Bosch, The University of Chicago, Oak Ridge National Lab, ExxonMobil and RIKEN, they aim to explore the best methods to build workflows for materials simulation.
  • Optimization: aimed at establishing collaboration across global institutions such as E.ON, Wells Fargo, and others to explore key questions that progress the identification of optimization problems best suited for quantum advantage in sustainability and finance.

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