Quantum Research Seminars Toronto consist of two 30 min talks about some Quantum Computation topic. Seminars are given by high-level quantum computing researchers with the focus on disseminating their research among other researchers from this field. We encourage to attend researchers regardless of their experience as well as graduate and undergraduate students with particular interest in this field. Basic notions on quantum computing are assumed, but no expertise in any particular subject of this field.

In this 3rd series of seminars, the speakers will be Maria Schuld (Xanadu/University of KwaZulu-Natal) and Nathan Lacroix (Quantum Device Lab, ETH). Their talks are titled "How to distinguish ants from bees on near-term quantum computers" and "Continuous gate-sets for variational quantum algorithms", respectively.

We will send a Zoom link to those who register for this event 2 days, 2 hours and 10 min before the event starts.

The event recording, slides and chat history will be published in our Youtube channel and sent to the registered participants.

Looking forward to seeing you all!

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Talk 1:

How to distinguish ants from bees on near-term quantum computers

A popular approach to machine learning with quantum computers is to interpret the quantum device as a machine learning model that loads input data and produces predictions. By optimizing the quantum circuit, the "quantum model" can be trained like a neural network. This talk will present a new approach to design and understand these types of quantum models. The basic idea is to find a quantum representation of data so that different classes are embedded as quantum states that are "far away" from each other. Discriminating measurements are used to produce optimal predictions. The framework is similar in spirit to "metric learning" strategies in classical machine learning, and links quantum machine learning to the well-known problem of quantum state distrimination.

Ref: https://arxiv.org/abs/2001.03622

About the speaker:

Dr. Maria Schuld is a researcher at the Toronto-based quantum machine learning start-up Xanadu, as well as at the Big Data and Informatics Flagship of the University of KwaZulu-Natal in Durban, South Africa. She received her PhD degree in 2017 for her contributions to the intersection of quantum computing and machine learning. Maria co-authored the book "Supervised Learning with Quantum Computers" (Springer, 2018), and is especially passionate about promoting emerging technologies on the African continent.

Talk 2:

Continuous gate-sets for variational quantum algorithms

Variational quantum algorithms are believed to be promising for solving computationally hard problems and are often comprised of repeated layers of quantum gates. An example thereof is the quantum approximate optimization algorithm (QAOA), an approach to solve combinatorial optimization problems on noisy intermediate-scale quantum (NISQ) systems. Gaining computational power from QAOA critically relies on the mitigation of errors during the execution of the algorithm, which for coherence-limited operations is achievable by reducing the gate count. Here, we demonstrate an improvement of up to a factor of 3 in algorithmic performance as measured by the success probability, by implementing a continuous hardware-efficient gate set using superconducting quantum circuits. This gate set allows us to perform the phase separation step in QAOA with a single physical gate for each pair of qubits instead of decomposing it into two CZ-gates and single-qubit gates. With this reduced number of physical gates, which scales with the number of layers employed in the algorithm, we experimentally investigate the circuit-depth-dependent performance of QAOA applied to exact-cover problem instances mapped onto three and seven qubits, using up to a total of 399 operations and up to 9 layers. Our results demonstrate that the use of continuous gate sets may be a key component in extending the impact of near-term quantum computers.

Ref: https://arxiv.org/abs/2005.05275

About the speaker:

Nathan Lacroix joined the Quantum Device Lab at ETH Zurich in 2019. His work focuses on variational quantum algorithms for combinatorial optimization and on quantum software development.