Robert Banks
Title: Exploring Timescales in Quantum Walks and Adiabatic Optimisation
Abstract: Understanding and manipulating the timescales of quantum dynamics is crucial for the practical realisation of continuous-time quantum optimisation. This talk will explore two key continuous-time quantum algorithms: (i) continuous-time quantum walks, which are typically associated with short runtimes, though the physical origin of these timescales remains underexplored, and (ii) adiabatic quantum optimisation, where the timescale is known to depend on the minimal spectral gap. The central question in the latter case is: how can we improve the scaling of the gap to reduce the evolution time?
We will begin by discussing the role of thermalisation in continuous-time quantum walks, investigating how the system’s evolution guides it toward optimal solutions by reaching equilibrium. A key focus will be on methods for estimating the thermalisation timescale, while elucidating the physical mechanism, with a particular emphasis on the Max-Cut problem as a case study. Next, we introduce the concept of catalysts within the framework of adiabatic quantum optimisation—mechanisms that enhance the system’s evolution towards the optimal solution without altering the final state. Using a toy instance of the Weighted-Maximum Independent Set problem, we will demonstrate how the scaling of the minimal spectral gap can be improved by leveraging only standard Ising interactions and transverse-field driving to replicate the behaviour of an off-diagonal two-body catalyst. This adjustment can significantly alter the timescale required to solve the problem via adiabatic quantum optimisation.
By integrating insights from both quantum walks and adiabatic optimisation, this talk will provide fresh perspectives on how timescales in continuous-time quantum optimisation can be understood, probed, and manipulated.
Bio: Robert (Bobby) Banks is a physicist with a degree from the University of Oxford. After graduating, he completed his Ph.D. at University College London (UCL) as part of the centre for doctoral training in delivering quantum technologies. During his Ph.D. he focused on designing and analyzing quantum algorithms for solving optimization problems on analogue quantum devices. While at UCL he also got involved in outreach events and delivering tutorials to undergraduate students.
Rahul Deshpande
Title: New Modes of Operating Commercial Quantum Annealing Processors
Bio: Rahul Deshpande is a Staff Experimental Physicist at D-Wave, working on developing and characterizing the next generation of superconducting quantum processors. He completed his PhD in Physics at the University of Waterloo, where he studied nuclear spins in silicon with applications towards quantum computing and quantum sensing at the Institute for Quantum Computing (IQC).
Tim Duty
Title: Flip-chip Fluxonium for Coherent Analog Quantum Computing
Bio: Tim Duty is Chief Scientific Officer at Qilimanjaro Quantum Tech, S. L. He has worked in the field of superconducting quantum devices in both industrial and academic settings since completing his PhD at the University of British Columbia in 2000. He was one of the four initial scientists involved with starting up D-Wave Systems in Vancouver in 2000. During 2002-2007, he was a researcher at Chalmers University of Technology in Sweden, where he pioneered radio-frequency readout of Josephson charge qubits, single charge sensing, and microwave quantum optics. He established two ultra-low temperature superconducting quantum technology laboratories in Australia, first at the University of Queensland during 2008-2011, and second at the University of New South Wales (UNSW) during 2011-2021. As a Professor of Physics at UNSW he built up nanofabrication processing for superconducting quantum devices, focusing on 1D quantum transport in superconducting junction chains. His former students and postdocs from these laboratories have achieved successful careers in academia, industry and government laboratories. In 2021 he returned to D-Wave Systems where he led a project to produce state-of-the-art, high-coherence fluxonium, in cooperation with the Quantum Matter Institute at the University of British Columbia. He joined Qilimanjaro in Barcelona in 2024 to boost processor development for coherent quantum annealing and analog quantum computing.
Stephen Jordan
Title: Optimization by Decoded Quantum Interferometry
Abstract: In this talk I will describe a quantum algorithm called Decoded Quantum Interferometry (DQI), which uses the quantum Fourier transform to reduce optimization problems to decoding problems. (See: https://arxiv.org/abs/2408.08292.) For approximating optimal polynomial fits to data over finite fields, DQI efficiently achieves a better approximation ratio than any polynomial time classical algorithm known to us, thus suggesting exponential quantum speedup. This also extends to multivariate polynomials. These optimization problems are solved approximately by quantum reduction to decoding of Reed-Solomon and Reed-Muller codes, respectively. Sparse unstructured optimization problems such as max-k-XORSAT are reduced to decoding of LDPC codes. Although we have not identified quantum advantage for the sparse unstructured case, the performance of DQI can be calculated instance-by-instance based on the empirical performance of classical LDPC decoders such as belief propagation. In this way DQI has been benchmarked on a max-XORSAT instance with over 30,000 variables. This is joint work with Noah Shutty, Mary Wootters, Adam Zalcman, Alexander Schmidhuber, Robbie King, Sergei V. Isakov, and Ryan Babbush.
Bio: Stephen Jordan is a researcher at Google Quantum AI. The main focus of his research is quantum algorithms. He obtained his PhD in physics in 2008 from MIT. He is also the author and maintainer of the quantum algorithm zoo, an online compendium of quantum algorithms.
Yohei Kawakami
Title: Quantum Annealing with Kerr Parametric Oscillators and the Lechner-Hauke-Zoller Scheme
Bio: Yohei Kawakami is a researcher at Secure System Platform Research Laboratories, NEC Corporation, working on the development of quantum annealers using Kerr parametric oscillators on superconducting circuits, with a particular interest in the multi-body interactions between the oscillators. He completed his PhD in Physics at Nagoya University, where he studied theoretical hadron physics.
Alberto Nocera
Title: Beyond-classical Dynamics in Annealing Simulators: What’s Next?
Bio: Dr. Alberto Nocera is a Staff Scientist at the Quantum Matter Institute of The University of British Columbia since 2018. Nocera is an expert in Tensor Network Techniques applied to models of strongly correlated quantum matter, with particular focus on the Density Matrix Renormalization Group (DMRG) method.
Anita Weidinger
Title: Performance of Parity QAOA for the Signed Max-Cut Problem
Abstract: The practical implementation of quantum optimization algorithms on noisy intermediate-scale quantum devices requires accounting for their limited connectivity. As such, the parity architecture was introduced to overcome this limitation by encoding binary optimization problems onto planar quantum chips. We investigate the performance of the Quantum Approximate Optimization Algorithm on the parity architecture (parity QAOA) for solving instances of the signed Max-Cut problem on complete and regular graphs. By comparing the algorithms at fixed circuit depth, we demonstrate that parity QAOA outperforms conventional QAOA implementations based on SWAP networks. Our analysis utilizes Clifford circuits to estimate lower performance bounds for parity QAOA for problem sizes that would be otherwise inaccessible on classical computers. For single layer circuits we additionally benchmark the recursive variant of the two algorithms, showing that their performance is equal.
Bio: Anita Weidinger pursued her Bachelor’s and Master’s degrees in Technical Physics at TU Graz, where she focused on computational physics. In 2020, she joined Prof. Wolfgang Lechner’s research group in Innsbruck to pursue her PhD in quantum optimization. Her research has been centered around the Quantum Approximate Optimization Algorithm (QAOA) applied to the parity architecture.
Roberta Zambrini
Title: Analogue Processing of Sequential Data with Quantum Reservoir Computing
Abstract: Classical time series data play a crucial role in a spectrum of applications ranging from chaotic systems forecasting to speech recognition. The inherent dynamics of analogue quantum systems can be harnessed to efficiently analyse and predict sequential data. Quantum reservoir computing (QRC) is a promising machine learning framework that utilizes a quantum system as a high-dimensional computational reservoir, leveraging quantum coherence to extract complex temporal correlations. Unlike conventional computing, this neuromorphic approach circumvents the von Neumann bottleneck, enhancing computational power while maintaining a compact physical implementation with easy-training. Notably, QRC enables the processing of both classical and quantum inputs, enhancing temporal information processing. In this talk, I will introduce the principles of QRC and discuss recent proposals that highlight its potential applications and future directions.
Bio: Roberta Zambrini is a CSIC senior scientist at the Institute for Cross-disciplinary Physics and Complex Systems (IFISC). Her research focuses on complex quantum systems, quantum optics and quantum machine learning and at present she leads a research line on quantum reservoir computing. She has published more than 90 articles in international journals, coordinated multiple projects including two European, five national and two regional projects and being at present she is guarantor of the center of excellence of IFISC (Maria de Maeztu). She played prominent roles in scientific dissemination and management. In 2020 she led the CSIC white paper on Digitalization, she has been manager of the Spanish Research Area AEI (2021-2024) and is associate editor of Physical Review Letters. She is part of the initiative Woman for Quantum.