Quantum Optimization Benchmarking Library – The Intractable Decathlon(opens in a new tab)
Koch, D., Neira, D.E.B., Chen, Y. et al. | arXiv:2504.03832 (2025)
Optimization Working Group
Unlocking advantage with quantum
IBM is collaborating with partners across industry and academia to advance quantum research—developing new algorithms and applications that go beyond classical computing.
Partner organizations include
Advancing computation with algorithm research
Algorithms bring quantum advantage closer, enabling known applications to be realized sooner and new use cases to be discovered. By working with our partners to develop algorithms in four key areas and test their performance on 100+ qubit quantum computers, we are learning where quantum can provide near-term value.
Optimization
Quantum could help solve combinatorial, convex, and other difficult optimization problems, with broad applications.
Quantum approximate multi-objective optimization(opens in a new tab)
Kotil, A., Pelofske, E., Riedmüller, S. et al. | Nat Comput Sci (2025)
Los Alamos National Laboratory
Zuse Institute Berlin
Runtime Quantum Advantage with Digital Quantum Optimization(opens in a new tab)
Chandarana, P., Gomez Cadavid, A., Romero, S. V. et al. | arXiv:2505.08663 (2025)
Kipu Quantum
Hamiltonian simulation
Simulating properties such as ground state energies and time dynamics enables the study of quantum systems.
Chemistry beyond the scale of exact diagonalization on a quantum-centric supercomputer(opens in a new tab)
Robledo-Moreno, J., Motta, M., Haas, H. et al. | Science Advances Vol. 11, No. 25 (2025)
Riken
Quantum-Centric Algorithm for Sample-Based Krylov Diagonalization(opens in a new tab)
Yu, J., Moreno, J.R., Iosue, J.T. et al. | arXiv:2501.09702 (2025)
Oak Ridge National Laboratory
Real-Time Dynamics in a (2+1)-D Gauge Theory: The Stringy Nature on a Superconducting Quantum Simulator(opens in a new tab)
Jesús, C., Fraxanet, J., César, B. et al. | arXiv:2507.08088 (2025)
BasQ
Partial differential equations
Quantum algorithms could help solve partial differential equations in hard problems such as fluid and plasma dynamics.
Quantum simulation of a noisy classical nonlinear dynamics(opens in a new tab)
Bravyi, S., Manson-Sawko, R., Zayats, M. et al. | arXiv:2507.06198 (2025)
Hamiltonian Simulation for Advection-Diffusion Equation with arbitrary transport field(opens in a new tab)
Gomes, N., Sharma, G., Pathak, J. et al. | arXiv:2508.16728 (2025)
Ansys
H-DES: A Quantum-Classical Hybrid Differential Equation Solver(opens in a new tab)
Jaffali, H., de Araujo, J.B., Milazzo, N. et al. | arXiv:2410.01130 (2024)
ColibriTD
Machine learning
Quantum enables new data representations for machine learning, with potential use in AI applications.
Covariant quantum kernels for data with group structure(opens in a new tab)
Glick, J.R., Gujarati, T.P., Córcoles, A.D. et al. | Nat. Phys. 20, 479-483 (2024)
Mitigating exponential concentration in covariant quantum kernels for subspace and real-world data(opens in a new tab)
Agliardi, G., Cortiana, G., Dekusaret, A. et al. | arXiv:2412.07915 (2024)
E.ON
Dynamic parameterized quantum circuits: expressive and barren-plateau free(opens in a new tab)
Deshpande, A., Hinsche, M., Najafi, K., Sharma, K., Sweke, R., Zoufal, C. et al. | arXiv:2411.05760 (2024)
Get support for your quantum research
IBM supports open exploration and research that advances the field of quantum computing. Explore how you can get additional time on our QPUs via the Open Plan or the IBM Quantum Credits program.
Accelerate discovery with IBM Quantum Platform
IBM Quantum Platform provides access to quantum computers, documentation, and learning resources. Sign up today to get 10 free minutes of execution time per month on our 100+ qubit quantum processing units (QPUs).
