Join us for Quantum Today, where we sit down with researchers from the University of Waterloo’s Institute for Quantum Computing (IQC) to talk about their work, its impact and where their research may lead.
TQT’s Quantum For Health (Q4Health), is open to all at the University of Waterloo, seeking opportunities where quantum can advance health.
On September 19, TQT will host a Q4Health Launch Event in the Mike and Ophelia Lazaridis Quantum-Nano Centre Rm 0101. This event will include descriptions of quantum for health case studies. Following the talks, there will be a meet and greet to assist in team building. Attendees will receive information updates and an opportunity to register and learn more about upcoming Lunch and Learn sessions.
Register by September 16 (for refreshment planning purposes). There will be limited onsite registration at the event.
The Jaynes-Cummings Model (JCM) approximates the Quantum Rabi Model (QRM) in some regimes and is exactly solvable by only keeping the rotating or `energy-conserving’ terms and dropping the counter-rotating or `non-energy conserving’ terms.
Since the proposal of the JCM, questions on the effect and presence of counter-rotating terms popped up.
Using strong driving, one can induce the effects of the counter-rotating terms on a comparable timescale to the rotating terms. In such a scenario, one can create a Schrödinger cat state in a resonant manner without the need for any type of Kerr nonlinearity.
In this talk, we review the QRM and its descendant, the JCM. Then, we discuss the realization of a Schrödinger cat state, its challenges in practice and how to solve them.
Developing new quantum algorithms is a famously hard problem. The lack of intuition concerning the quantum realm makes constructing quantum algorithms that solve particular problems of interest difficult. In addition, modern hardware limitations place strong restrictions on the types of algorithms which can be implemented in noisy circuits. These challenges have produced several solutions to the problem of quantum algorithm development in the modern Near-term Intermediate Scale Quantum (NISQ) Era. One of the most prominent of these is the use of classical machine learning to discover novel quantum algorithms by minimizing a cost function associated with the particular application of interest. This quantum-classical hybrid approach, also called Variational Quantum Algorithms (VQAs), has attracted major interest from both academic and industrial researchers due to its flexible framework and expanding list of applications - most notably optimization (QAOA) and chemistry (VQE). What is still unclear is whether these algorithms will deliver on their promise when implemented at a useful scale, in fact there is strong reason to worry whether the classical machine learning model will be able to train in the larger parameter space. This phenomenon is commonly referred to as the Barren Plateaus problem, which occurs when the training gradient vanishes exponentially quickly as the system size increases. Recent results have shown that some cost functions used in training can be proven to result in a barren plateau, while other cost functions can be proven to avoid them. In this presentation, I apply these results to my 2018 paper where my group developed a new Variational Quantum State Diagonalization (VQSD) algorithm and so demonstrate that this algorithm's current cost function will encounter a Barren Plateau at scale. I then introduce a simple modification to this cost function which preserves its function while ensuring trainability at scale. I also discuss the next steps for this project where I am teaching a team of 6 quantum novices across 4 continents the core calculation I use in this work to expand my analysis to the entire literature of VQAs.
A quantum computer attains computational advantage when outperforming the best classical computers running the best-known algorithms on well-defined tasks. No photonic machine offering programmability over all its quantum gates has demonstrated quantum computational advantage: previous machines were largely restricted to static gate sequences. I will discuss a quantum computational advantage using Borealis, the latest of Xanadu’s photonic processors offering dynamic programmability and available on the cloud. This work is a critical milestone on the path to a practical quantum computer, validating key technological features of photonics as a platform for this goal.
IQC Achievement Award winner Bowen Yang sat down with us to discuss his PhD research in quantum materials, the opportunities he’s received while at IQC, and his recommendations for students interested in learning and gaining more experience with quantum.
IQC Achievement Award winner Xi Dai sat down with us to discuss his PhD research with superconducting circuits, the challenges he overcame, and his plans for the future following graduation.
IQC Achievement Award winner Shayan Majidy sat down with us to discuss his current and future research on noncommuting conserved quantities, the award, and his advice for current and aspiring students interested in quantum information.
Kimia Mohammadi's master’s thesis investigates the design of an 8-inch transceiver telescope capable of both transmitting and receiving quantum signals at 785 nm, as well as classical communications at 980 nm and 1550 nm, with higher efficiency than similar commercial options. This telescope is aimed to be one of the quantum ground stations that will test Quantum Key Distribution (QKD) protocols and other communication schemes with the Quantum Encryption and Science Satellite (QEYSSat), once it is launched in 2024.
In the NISQ era of quantum computing, as system sizes are progressively increasing, there are major concerns about the degradation of performance with increasing complexity. These can largely be reduced to the problems of crosstalk and correlations between system components, of fabrication uncertainties and drift in system parameters, and of multi-parameter optimization across multi-qubit state spaces in a fixed uptime duty cycle. In this presentation, we address inroads towards a more comprehensive, scalable approach for control theoretic solutions to maintaining (given architecture) performance that encompasses: a method to incorporate arbitrary couplings into an effective Hamiltonian frame with superexponential speedup compared to standard perturbative approaches [B. Li, T. Calarco, F. Motzoi, PRX Quantum 3, 030313 (2022)]; a control theoretic approach to tracking uncertainties in quantum circuits giving tight error bounds [M. Dalgaard, C. Weidner, F, Motzoi - Phys. Rev. Lett. 128, 150503 (2022)]; and a machine learning framework for symbolic optimization given particular Hamiltonian and associated uncertainties with a single meta-optimization permitting simultaneous tuneup of all qubits within the architecture belonging to the same class of Hamiltonians [F. Preti, T. Calarco, F. Motzoi, arXiv:2203.13594 (2022)].