2026 CAP Congress / Congrès de l'ACP 2026

Session

(DQI) T3-11 Quantum Algorithms II | Algorithmes quantiques II (DIQ)

T3-11

23 Jun 2026, 16:15

U. Ottawa - Learning Crossroads (CRX) Building

Conveners

(DQI) T3-11 Quantum Algorithms II | Algorithmes quantiques II (DIQ)

• Thomas Baker (Department of Physics & Astronomy and also of Chemistry, University of Victoria)

1033. Generative Learning of Optimal Quantum Measurements

Prof. Cunlu Zhou (Universite de Sherbrooke)

23/06/2026, 16:15

Invited Speaker / Conférencier(ère) invité(e)

Efficient measurement of quantum states is a fundamental task in quantum computing. In particular, for many near-term quantum algorithms, a key challenge is how to accurately estimate a large number of non-commuting Pauli observables with as few measurements as possible. One common strategy is to group observables into commuting sets, but measuring general commuting groups often requires deep...

642. Simulating Qubit Systems with Tensor Network Algorithms

Anne Najdzionek (Department of Physics & Astronomy, University of Victoria, Victoria, British Columbia V8P 5C2, Canada)

23/06/2026, 16:45

Division for Quantum Information / Division de l'information quantique (DQI / DIQ)

Oral Competition (Graduate Student) / Compétition orale (Étudiant(e) du 2e ou 3e cycle)

In the quest for robust quantum computers with large number of qubits one the roadblocks is the predictability of qubit design. Exact diagonalization techniques for the simulation of quantum computing systems can only handle a handful of qubits. We are quickly surpassing this qubit number in both superconducting circuit and other quantum systems. To simulate these systems and predict the...

904. Hybrid Quantum Genetic Algorithm for Stable Hyperparameter Optimization in Flood Prediction Models

Mahkame Salimi Moghadam (University of Calgary)

23/06/2026, 17:00

Division for Quantum Information / Division de l'information quantique (DQI / DIQ)

Oral Competition (Graduate Student) / Compétition orale (Étudiant(e) du 2e ou 3e cycle)

Quantum computing promises new ways to tackle high-dimensional, combinatorial optimization problems that appear throughout scientific modelling. Flood prediction is one such area: modern neural networks can support large-scale, data-driven flood mapping, but their performance is highly sensitive to hyperparameter choices, and repeated tuning is computationally expensive. In this work, we...

605. Variational Imaginary-Time Polynomial Filtering with Ancilla-Based Implementation

Dr Bahman Seifi (Department of Physics and Physical Oceanography, Memorial University of Newfoundland and Labrador)

23/06/2026, 17:15

Division for Quantum Information / Division de l'information quantique (DQI / DIQ)

Oral (Non-Student) / Orale (non-étudiant(e))

Imaginary-time evolution (ITE) provides a direct route to ground-state preparation by exponentially suppressing excited-state contributions, but practical implementations on quantum hardware are limited by rapidly growing circuit depth and intrinsically low per-step success probabilities. To address these limitations, we develop a variational imaginary-time evolution framework based on...