Department of Physics

The origin of the observed pattern of fermion masses, mixings, and CP violation remains one of the central unresolved problems of particle physics. At the same time, the remarkable suppression of flavor-changing processes places extremely strong constraints on new physics at the TeV scale. Understanding how new physics can respect these constraints while still producing observable signals is therefore a major challenge. This project addresses the flavor puzzle through a research program that combines effective field theory, collider and flavor phenomenology, model building, and early-universe cosmology.

The project is organized into two closely connected parts. Part I develops Minimal Flavor Protection (MFP) into a sufficiently general yet predictive framework for the interpretation of flavor and collider data beyond Minimal Flavor Violation (MFV). MFV has long served as the standard paradigm for flavor-safe new physics, but it is highly restrictive and may not capture the broader range of possibilities accessible to present and future experiments. MFP instead identifies the minimal approximate flavor symmetry required to protect TeV-scale new physics from existing constraints. This opens a new parameter space in which sizable violations of flavor universality and symmetry-allowed flavor-charged currents can arise while remaining consistent with present data. The main objectives are to classify the space of MFP-like symmetry–spurion structures, determine the experimentally viable region of maximal universality violation, identify which flavor-charged currents can arise at leading order, and apply the MFP framework to representative classes of new-physics models.

Part II investigates the cosmological dynamics of flavor symmetry breaking and the resulting stochastic gravitational-wave backgrounds (SGWB). Flavor models often involve scalar sectors whose symmetry-breaking dynamics can trigger strong first-order phase transitions in the early universe. Such transitions may generate gravitational-wave signals potentially observable by future detectors. This project will determine which classes of flavor models can produce observable signals, characterize the predicted gravitational-wave spectra for both TeV-scale and high-scale flavor dynamics, and explore the complementarity between gravitational-wave observations and laboratory probes of flavor physics.

The proposed research advances the state of the art in two ways. First, it develops MFP from a recently proposed concept into a systematic framework for BSM flavor interpretation, extending and complementing existing symmetry-based approaches such as MFV. Second, it explores a largely unexplored connection between flavor physics and gravitational-wave astronomy by establishing a program to study SGWB signals from flavor model building.

The research approach combines symmetry-based analyses, effective field theory, perturbative quantum field theory, collider and flavor phenomenology, finite-temperature field theory, and gravitational-wave predictions. The project will be carried out by the PI together with one postdoctoral researcher and one doctoral student. The postdoctoral researcher will focus on formal developments and precision phenomenology, while the doctoral student will work on benchmark implementations, numerical analyses, and sensitivity forecasts. Together, the two parts of the project aim to advance a unified perspective in which approximate flavor symmetries guide both the search for new physics in laboratory experiments and the exploration of the dynamical origin of flavor in the early universe.

Fleck will be a digital platform composed of three layers; from the bottom up: (i) hardware and hybrid solutions; (ii) open-source community; (iii) end-users.

At the bottom of the stack we are building Switzerland's first quantum computer based on novel, scalable 2D ion-trap architecture. User access will be provided through the platform, which will also contribute additional quantum and classical resources (CPU, GPU). New open-source software developed by the project will allow users to submit hybrid quantum-AI jobs as single workloads via a centralized system. In the layer above (public domain) contributors will be able to upload their code to execute on the hardware underneath, with the first contributions coming from the project. Finally, in the top layer, end-users can take the code and outputs from the open-source community and use them to pioneer their own projects or industrial use cases.

Our partners populate each layer of the platform: from hardware producers/providers to software developers (ZuriQ, QuantumBasel, Phoenix Technologies), algorithms designers (University of Basel, University of Fribourg, ETH) and end-users (Multiwave Technologies, Novartis Pharma). Together we will address three key application domains: quantum computing for drug design, brain cancer detection and ultra-low field (ULF) MRI imaging. The correlated nature of our collaboration spans the spectrum from fundamental research to tech transfer. Our team will be the first in the world to create a 2D Penning trap quantum computer (ZuriQ) and connect it for external use (QuantumBasel, Phoenix). Our quantum calculations (Uni Fribourg, Uni Basel) will suggest: metal-based contrast agents (which image at improved resolution), and anticancer drugs (with high specificity towards target proteins). We will rank the candidates using quantum-AI methods (ETH) and select the best ones to be sent for fabrication in the lab (Novartis). After synthesis, the contrast agent drug complexes will be empirically tested in Switzerland's first ULF MRI scanner (Multiwave).

The beneficiaries of our platform fall into four classes of end-user groups: (1) pharmaceutical companies; (2) medical imaging community; (3) public organizations; (4) open-source community. We envisage several commercialization paths for after the project’s completion, but the code for one-job hybrid workflows will remain open-source, ensuring that the next generation of researchers inherit barrier-free access to scientific tools.

Antimicrobial resistance (AMR) is one of the most pressing threats to global health, already responsible for an estimated 1.3 million deaths each year and projected to cause up to 10 million annual deaths and a cumulative economic loss of 100 trillion dollars by 2050. Despite decades of research, no new antibiotic class reached approval for nearly thirty years, and the drugs introduced in recent decades are essentially derivatives of older scaffolds. The discovery pipeline is slow, risky, and prohibitively expensive, often exceeding a billion dollars and a decade of development time. As resistance outpaces innovation, healthcare systems are left increasingly vulnerable.

Q-ARMOR addresses this challenge by introducing the first hybrid quantum–classical pipeline for antibiotic discovery, focused on the design of metal-conjugated antimicrobial peptides (AMP–M), a class of compounds with exceptional therapeutic potential that remains largely unexplored. The project combines accurate quantum electronic-structure modeling of resistant binding sites with generative chemistry and advanced delivery strategies. Specifically, quantum kernels embedded in QM/MM enable fidelity in describing metal coordination and resistant targets. In contrast, a novel style-based quantum generative adversarial network expands the discovery space for AMPs and AMP–M hybrids beyond the limits of classical AI. The pipeline integrates resistance-aware optimization criteria to balance potency, resilience, and synthesizability, and it extends discovery to translational readiness by coupling the molecules with ionic liquid and lipid-based carriers that improve stability, bioavailability, and safety.

The innovative character of Q-ARMOR lies in its end-to-end structure, which links molecular physics to drug discovery and finally to delivery systems. Unlike AI-only pipelines, this approach enhances quantum accuracy exactly where it matters, while simultaneously addressing the formulation gap that has prevented AMPs from entering the clinic. Patent analysis confirms clear space for intellectual property protection on quantum-enhanced generative models, resistance-aware discovery frameworks, and integrated carrier design. The potential impact is substantial. From a health perspective, Q-ARMOR could deliver new AMP–M drugs capable of overcoming resistance mechanisms that cripple today’s antibiotics. For the economy, it reduces development costs, accelerates discovery, and strengthens Switzerland’s pharmaceutical and biotech sector, which is already a global leader, thanks to companies such as Novartis and Basilea that have expressed interest in the project. At the societal level, the outcomes will reduce infection-related mortality and protect healthcare systems, directly contributing to Sustainable Development Goal 3 on health, and indirectly to goals on innovation and climate through more efficient computation and reduced experimental waste.

The implementation strategy follows a clear innovation roadmap. During the first two years, the project will move from concept (IRL 2) to proof of concept (IRL 3) by developing the generative quantum models and testing AMP–M activity against WHO-priority Gram-negative pathogens in vitro. In the following phase, laboratory validation (IRL 4) will be achieved with zebrafish in vivo models, stability improvements using ionic liquid and vesicle carriers, and initial patent filings. By the end of the funding period, the project will reach IRL 5 with an end-to-end validated discovery platform, a library of optimized AMP–M candidates, and a jointly defined preclinical roadmap in partnership with Basilea. The Q-ARMOR consortium brings together world-class expertise in quantum chemistry, molecular simulation, peptide synthesis, and in vivo models, combined with strong implementation partners such as QuantumBasel and Molecular Quantum Solutions, who provide hardware, algorithms, and business development. Academic partners will generate new IP and train interdisciplinary talent, while industry partners will integrate results into product pipelines and service offerings. By uniting quantum accuracy, AI innovation, and delivery science, Q-ARMOR sets out to create a transformative discovery pipeline that can finally break the bottleneck in antibiotic innovation.