2026 CAP Congress / Congrès de l'ACP 2026

Session

(DQI) T3-11 | (DIQ)

T3-11

23 Jun 2026, 16:15

U. Ottawa - Learning Crossroads (CRX) Building

1033. TERRA: Tensor-network Error-mitigated Robust Randomized Algorithm

Prof. Cunlu Zhou (Universite de Sherbrooke)

23/06/2026, 16:15

Invited Speaker / Conférencier(ère) invité(e)

We introduce TERRA (Tensor-network Error-mitigated Robust Randomized Algorithm), a practical and versatile algorithmic framework that unifies tensor-network error mitigation with robust shallow shadows to enable scalable and noise-resilient quantum algorithm development on current quantum devices. We demonstrate TERRA within the recently proposed multi-observable dynamic mode decomposition...

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...