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Friday, August 7, 2026 11:30 am - 12:30 pm EDT (GMT -04:00)

CombOpt ReadingGroup - David Aleman-Unsplittable multicommodity flows in fully planar instances

Speaker:

 David Aleman
Affiliation: University of Waterloo
Location: MC 6029

Abstract: 

The multicommodity flow problem involves routing multiple distinct commodities through a shared network. An instance is given by an undirected graph G=(V, E(G) ) with edge capacities, and a collection of source-sink pairs (s_i,t_i) in V with associated nonnegative demands d(s_i, t_i). It will be convenient to think of the source-sink pairs as forming the edges of a demand graph H=( V, E(H) ). A flow is feasible if it routes all demands without exceeding the edge capacities, and it is unsplittable if it routes each demand along a single path. Let C be the smallest value such that the existence of a feasible flow implies the existence of an unsplittable flow that exceeds the edge capacities by at most an additivie amount of C times the maximum demand value. 
We show that if G+H = (V, E(G) U E(H) ) is planar, then  1.5<= C <= 2.
Joint work with Kumar, Poremba, and Shepherd. 
 
Friday, August 7, 2026 3:30 pm - 4:30 pm EDT (GMT -04:00)

Tutte Colloquium -Michael Friedlander-The Measure of a Cone: Randomness and exactness in convex optimization

Speaker: Michael Friedlander
Affiliation: University of British Columbia.
Location: MC 5501

Abstract: Conic geometry encodes combinatorial properties of a convex program. Under a probabilistic model of the data, these combinatorial properties become random events. Their likelihood is the measure of a cone. We illustrate this view with a dual pair of questions. First, how much can a linear program be regularized before its solution changes? With random costs, the answer turns on the Gaussian measure of the solution's normal cone. Second, how many measurements are needed to separate a superposition of structured signals? Here, each signal's complexity is the statistical dimension of its descent cone. A convex program recovers the components once the measurement count exceeds the total complexity.

Based on joint work with Sharvaj Kubal, Yaniv Plan, and Matthew Scott; Zhenan Fan, Halyun Jeong, and Babhru Joshi; and Ives Macêdo and Ting Kei Pong.
Tuesday, August 11, 2026 2:00 pm - 3:00 pm EDT (GMT -04:00)

IQC Seminar - Calvin Liu - Recent advances in random quantum circuit sampling

Speaker: Calvin Liu
Affiliation:  University of Waterloo
Location: MC 5029

Abstract: 

In 2019, Google announced the demonstration of quantum supremacy by performing a computational task known as random circuit sampling on their 53-qubit quantum computer Sycamore. In their paper, they claimed that it would take classical computers 10000 years to perform the same task. Almost seven years has passed, and what happened to this claim since then? In this talk, I will provide a high-level update on the subsequent developments in quantum hardware experiments, classical simulation software, asymptotic classical simulation algorithms, and proving the hardness of classical simulation. This talk is aimed at a non-quantum computing audience, and no prior background in quantum computing is assumed.