Quantitative consulting
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Hello! I'm Francesco, and you've just landed on my personal website. 😁 I'm a physicist who loves to solve complex problems by combining multiple methods, merging mathematics, programming, and statistics. Here you can find information about me and the projects I'm working on. If you have any questions or would like to collaborate, feel free to contact me!
Currently, Consultant at d-fine s.r.l. · Milan
01 The through-line
Francesco Slongo studied physics at the University of Trento, spending his third year abroad on a 10-month Erasmus exchange at the University of Oslo. He went on to a master's in theoretical physics in the joint Trento–SISSA programme, and then a PhD at SISSA in Trieste, under the supervision of Cristian Micheletti.
His doctoral research focused on developing new algorithms for sampling dense polymer systems, which are notoriously difficult to simulate with conventional methods such as Monte Carlo. These techniques drew both on strategies from statistical mechanics and on ideas borrowed from quantum computing. Part of his research also involved studying the topological properties of such systems, in particular the knotting and linking of polymer rings and the equilibrium properties of self-assembling chains.
After his PhD, Francesco spent a year teaching mathematics and physics to high-school students in Belluno, before joining d-fine in Milan as a consultant, where he now applies Monte Carlo methods to financial risk management.
Francesco enjoys tackling complex problems from different angles, combining mathematics, programming, and statistics.
02 News
03 Education & jobs
Quantitative consulting
Teaching physics and maths to secondary students. I enjoyed writing exercises and preparing lab experiments, always trying to make the material accessible but engaging.
I worked on the development of advanced Monte Carlo methods and alternative sampling techniques based on quantum annealers for self-assembling polymers, under the supervision of Cristian Micheletti.
04 Publications
The quadratic unconstrained binary optimization (QUBO) encoding makes it possible to sample classical many-body systems that are otherwise intractable for conventional Monte Carlo, specifically self-assembled melts of rigid lattice ring polymers, where high density, chain stiffness and topological constraints drive real-space autocorrelation times to diverge.
Our quantum-inspired encoding overcomes this problem and enables sampling melts of lattice rings with fixed curvature and compactness, unveiling counterintuitive topological effects. Tackling the same problems with the D-Wave quantum annealer leads to substantial performance improvements and advantageous scaling of sampling cost with the size of the self-assembled ring melts. DOI: 10.1126/sciadv.adi0204 ↗
A general method for computing canonical averages of physical models sampled via quantum or classical quadratic unconstrained binary optimization (QUBO), built on a histogram-reweighting scheme applicable to QUBO-based sampling restricted to specific intervals of an order parameter such as physical energy.
The scheme accurately recovers the density of states, in turn allowing expectation values to be computed in the conjugate ensemble (e.g. at fixed temperature) for systems otherwise intractable with real-space sampling. Applied to space-filling melts of lattice ring polymers mapped in QUBO form, the method reveals that the ring catenation probability is nonmonotonic with bending rigidity. DOI: 10.1103/PhysRevResearch.7.023116 ↗
A Monte Carlo method for studying topological entanglements in polymer melts, overcoming the computational limitations associated with equilibrium sampling of long polymer chains. The approach exploits a self-assembly ensemble and strictly local moves to efficiently propagate backbone reconnections across scales while preserving the number of linear chains, achieving near-linear scaling of the decorrelation time with system size.
Applied to fully-packed lattice melts, the method enables the equilibration of systems containing more than one billion monomers and provides access to the universal melt regime. The characterization of intra- and inter-chain entanglements reveals that they arise from localized knots and links rather than global tangles, with the Gauss linking integral between neighbouring chains growing only as N1/4. DOI: 10.1038/s41467-026-74480-4 ↗
Full list on Google Scholar ↗ · ORCID ↗
05 What I work on
I am interested in the dynamic and equilibrium properties of melts of linear chains and rings. In particular, I look at the self and mutual entanglement of the components: the knotting probability of a single chain and the linking probability of multiple chains.
I am interested in developing new techniques for improving the sampling of physical problems, both by working on new Monte Carlo moves and by applying statistical mechanics ideas to the sampled configurations, to extract as much information as we can.
I have worked on alternatives to sampling algorithms, exploring in which cases optimization can be used in place of Monte Carlo. Among the several possibilities, I have looked at classical techniques, from in-house algorithms such as simulated annealing to commercially available solvers like Gurobi and CPLEX. I have also worked on quantum annealers, looking for use cases where they can offer a speed-up over classical techniques.
More recently I have become interested in quantitative finance, delving into the topic with the tools I developed during my career in physics.
06 In the classroom
Material that I developed while teaching at the high school.
07 My little projects
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08 Off the clock
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09 Behind the scenes
10 Guestbook
12 Problem of the month
13 Found a bug?
Something broken or out of place? Tell me; it helps me fix it.