Quantum Computing for Computational Physics: Algorithms, Simulations, and Applications
Quantum computing is emerging as a new computational paradigm for simulating, analyzing, and modeling physical systems. Computational physics has traditionally driven progress in numerical algorithms, many-body modeling, high-performance computing, and predictive simulation; quantum computing introduces complementary ways to represent Hilbert spaces, perform Hamiltonian simulation, solve eigenvalue and dynamical problems, and sample quantum distributions.
This special topic aims to bring together advances in quantum algorithms, quantum simulation, and application-driven quantum computing relevant to computational physics. We welcome contributions spanning fundamental theory, methodological innovation, resource analysis, software and implementation, and benchmark studies that clarify where quantum computers may complement or outperform state-of-the-art classical approaches.
The scope is intended to be broad, covering quantum computing for computational chemistry, electronic structure, condensed matter and materials, nuclear and high-energy physics, quantum dynamics, fluids and plasma models, and related areas where computational physics and quantum information methods intersect.
Topics covered include, but are not limited to:
- Quantum algorithms for computational physics, including Hamiltonian simulation, quantum phase estimation, qubitization, block encoding, linear-combination-of-unitaries methods, quantum linear algebra, and eigenvalue problems
- Quantum algorithms for computational chemistry and electronic structure, including ground and excited states, spectroscopy, reaction dynamics, nonadiabatic processes, and strongly correlated molecules and materials
- Quantum simulation of condensed matter, lattice models, many-body systems, quantum materials, topological phases, and open quantum systems
- Quantum computing approaches for nuclear physics, high-energy physics, lattice field theory, plasma physics, and fluid dynamics
- Hybrid quantum-classical methods, including variational algorithms, quantum subspace expansion, adaptive ansatz construction, quantum embedding, error mitigation, and measurement reduction
- Fault-tolerant algorithms, resource estimation, early fault-tolerant applications, and comparisons among Hamiltonian encodings and problem representations
- Quantum algorithms and software for computational materials science, catalysis, molecular magnets, defects, photonics, quantum devices, and industrially motivated applications
- Quantum machine learning, data-driven quantum simulation, and quantum-enhanced approaches to modeling complex physical systems
- Connections between quantum computing, tensor networks, classical simulation, high-performance computing, and randomized or reduced-scaling methods
- Benchmarking, verification, reproducibility, and rigorous comparison of quantum algorithms with leading classical computational methods
- Experimental and hardware-aware demonstrations of quantum algorithms for physically motivated computational problems, including noise, compilation, connectivity, and implementation constraints
Guest Editors
Artur F. Izmaylov (University of Toronto)
Edwin Barnes (Virginia Tech)
Peter J. Love (University of Toronto)