The workshop will bring together researchers at the interface of statistical mechanics, computer science, and quantum computing to discuss quantum-based technologies and quantum-inspired algorithms for the computational physics of classical complex systems.

The objective is threefold:

• Map out the state of the art of quantum hardware development and critically assess their groundbreaking potential from statistical mechanics to biophysics and bioinformatics;
• Overview quantum computing and quantum-inspired encodings are being directed at challenging problems in classical complex systems;
• Lower the conceptual barriers that are currently preventing the soft matter, stat-mech, and life sciences communities from using quantum technologies.

Quantum computing has witnessed spectacular growth in the last five years due to concurrent advancements in hardware, algorithms, and applications. Digital quantum computers with ever more numerous and robust physical qubits are opening concrete perspectives for solving otherwise untreatable computational problems [1-3]. At the same time, quantum annealing machines are becoming competitive in tackling a broad class of discrete combinatorial problems [4-10]. These platforms, designed for optimizing Ising-like models, are primed to be directed at studying complex systems.

Soft matter, encompassing complex systems like polymers, gels, and supramolecular self-assemblies, is an ideal avenue for harnessing the untapped potential of nascent quantum-based technologies. A case in point is offered by sampling dense polymer systems, which can be prohibitive for Monte Carlo and molecular dynamics simulations. Yet, once reformulated on a lattice and remapped to an Ising-like optimization problem, such systems can be characterized very efficiently by quantum annealing [11,12]. Smaller but still sizeable returns can be obtained even when the quantum-inspired optimization problem is solved with classical machines [12]. 

The paradigmatic example of polymer melts aptly illustrates the significant potential for tackling challenging soft-matter problems with bona-fide quantum computing or, perhaps more interestingly, with an allegiance between quantum and classical computing. Other pioneering applications of quantum computing and quantum annealing machines to soft matter problems include RNA folding [13], protein folding [14], and protein design [15,16].

Despite these exciting developments, a scientific community focusing on developing and integrating physics modeling, classical computational physics, and quantum computing has yet to emerge fully.

This workshop will facilitate this process by bringing together scientists working on quantum algorithms, quantum hardware, and quantum-inspired theoretical approaches and world-leading experts in theoretical and computational methods for soft matter, biophysics, and statistical mechanics.  

This will create the lacking and urgently needed setting where physicists versed in classical computing and practitioners in quantum technologies can meet and, by discussing, become aware of unfamiliar but already existing concepts, methods, and hardware that can be transformative for either community.

CECAM, which has played a crucial role in establishing computational chemistry and physics as recognized mainstream scientific disciplines, represents the ideal framework for this first international workshop on quantum computing for complex systems.

References

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