IBM Quantum And Oak Ridge National Laboratory Demonstrate Breakthrough In Ground State Energy Calculation Using New Quantum Algorithm

IBM Quantum and Oak Ridge National Laboratory (ORNL) teams have developed the Sample-Based Krylov Quantum Diagonalization (SKQD) algorithm to compute ground state energies of quantum systems in materials science and high-energy physics.

Demonstrated on IBM processors with 85 qubits and 6,000 two-qubit gates, SKQD achieves high accuracy for problems beyond classical methods’ capabilities, advancing quantum-centric supercomputing.

IBM Quantum Collaboration with Oak Ridge National Laboratory

The collaboration between IBM Quantum and Oak Ridge National Laboratory (ORNL) has yielded progress in advancing quantum computing for practical applications. Together, the teams developed a novel quantum algorithm called Sample-Based Krylov Diagonalization (SKQD), which combines classical diagonalization techniques with quantum Krylov states to approximate ground state energies of complex quantum systems. This approach addresses key challenges in quantum computing by ensuring convergence guarantees similar to phase estimation while maintaining the error mitigation benefits of sample-based methods.

The SKQD algorithm was experimentally demonstrated on IBM quantum processors, utilizing 85 qubits and up to 6,000 two-qubit gates to simulate the ground state of the Anderson impurity model. This achievement represents one of the largest implementations of quantum diagonalization to date, showcasing how quantum computing can complement classical supercomputing in solving problems beyond the reach of exact diagonalization.

The results highlight the potential for quantum-advantage applications in materials science and high-energy physics, where accurate simulations of many-body systems remain computationally intensive.

The collaboration underscores the importance of algorithmic innovation in advancing quantum-centric supercomputing architectures. By integrating quantum and classical workflows, researchers can leverage each’s unique strengths to tackle complex scientific challenges. SKQD’s success not only advances the field of quantum computing but also demonstrates the value of interdisciplinary partnerships in driving technological progress.

Introduction of Sample-Based Krylov Quantum Diagonalization (SKQD)

The SKQD algorithm represents a significant advancement in quantum computing by integrating sample-based approaches with Krylov methods. This combination enables the approximation of ground state energies for complex quantum systems, addressing critical challenges in materials science and high-energy physics. The algorithm’s design ensures convergence guarantees akin to phase estimation while incorporating error mitigation techniques inherent to sample-based methods.

The experimental validation of SKQD on IBM quantum processors demonstrates its practicality and scalability. Utilizing 85 qubits and up to 6,000 two-qubit gates, the team successfully simulated the ground state of the Anderson impurity model, achieving high accuracy for problem sizes beyond the capabilities of exact diagonalization. This milestone underscores the potential of quantum-advantage applications in solving computationally intensive problems.

Experimental Demonstration on 85 Qubits

Utilizing 85 qubits and up to 6,000 two-qubit gates, the team successfully simulated the ground state of the Anderson impurity model, achieving high accuracy for problem sizes beyond the capabilities of exact diagonalization. This achievement highlights the potential of quantum-advantage applications in solving computationally intensive problems in materials science and high-energy physics.

The SKQD algorithm’s ability to approximate ground state energies for complex quantum systems represents a major step forward in addressing critical challenges in these fields. By combining sample-based approaches with Krylov methods, the algorithm ensures convergence guarantees akin to phase estimation while incorporating error mitigation techniques inherent to sample-based methods. This design allows for practical and scalable implementations of quantum simulations.

The success of SKQD underscores the importance of algorithmic innovation in advancing quantum computing. By leveraging quantum-classical workflows, researchers can unlock new possibilities for simulating many-body systems, paving the way for breakthroughs in fields such as materials science and high-energy physics. The collaboration between IBM Quantum and ORNL exemplifies how interdisciplinary partnerships can drive progress in this rapidly evolving field.

The experimental validation of SKQD not only advances the field of quantum computing but also demonstrates the value of interdisciplinary partnerships in driving technological progress. By integrating quantum and classical workflows, researchers can leverage the unique strengths of each to tackle complex scientific challenges, showcasing the potential for quantum-advantage applications in solving problems beyond the reach of exact diagonalization.