During this time of lockdown, the centre for quantum software and information (QSI) at the University of Technology Sydney has launched an online seminar series.  With talks once or twice a week from leading researchers in the field, meQuanics is supporting this series by mirroring the audio from each talk.  I would encourage if you listen to this episode, to visit and subscribe to the UTS:QSI YouTube page to see each of these talks with the associated slides to help it make more sense.

https://youtu.be/4KIXVQtR9Qw

Free-Fermion Solutions and Frustration Graphs  

TITLE: Characterization of free-fermion-solvable spin models via graph invariants 

SPEAKER: Dr Adrian Chapman 

AFFILIATION: ARC Centre of Excellence for Engineered Quantum Systems (EQUS), University of Sydney, Australia 

HOSTED BY: A/Prof Chris Ferrie, UTS Centre for Quantum Software and Information  

ABSTRACT:  Finding exact solutions to spin models is a fundamental problem of many-body physics. A workhorse technique for exact solution methods is mapping to an effective description by noninteracting fermions. The paradigmatic example of this is the Jordan-Wigner transformation for finding an exact solution to the one-dimensional XY model. Another important example is the exact free-fermion solution to the two-dimensional Kitaev honeycomb model.  I will describe a framework for recognizing general models which can be solved this way by utilizing the tools of graph theory. Our construction relies on a connection to the graph-theoretic problem of recognizing line graphs, which has been solved optimally. A corollary of this result is a complete set of constant-sized frustration structures which obstruct a free-fermion solution. We classify the kinds of Pauli symmetries which can be present in models for which a free-fermion solution exists, and we find that they correspond to either: (i) gauge qubits, (ii) cycles on the free-fermion hopping graph, or (iii) the fermion parity. Clifford symmetries, except in finitely-many cases, must be symmetries of the free-fermion Hamiltonian itself. We expect our characterization to motivate a renewed exploration of free-fermion-solvable models, and I will close with an elaborate discussion of how we expect to generalize our framework beyond generator-to-generator mappings.  

RELATED ARTICLES: Characterization of solvable spin models via graph invariants. Quantum 4, 278 (2020). Characterization of solvable spin models via graph invariants: quantum-journal.org/papers/q-2020-06-04-278/  

OTHER LINKS: Adrian Chapman Webpage: https://equs.org/users/adrian-chapman

During this time of lockdown, the centre for quantum software and information (QSI) at the University of Technology Sydney has launched an online seminar series. With talks once or twice a week from leading researchers in the field, meQuanics is supporting this series by mirroring the audio from each talk. I would encourage if you listen to this episode, to visit and subscribe to the UTS:QSI YouTube page to see each of these talks with the associated slides to help it make more sense. 

https://youtu.be/2syrO_asU5Y

Hardness of Random Circuit Sampling (Google's supremacy experiment) 

TITLE: Cayley Path and Quantum Supremacy SPEAKER: Dr Ramis Movassagh 

AFFILIATION: MIT-IBM Watson AI Lab, Cambridge MA, USA 

HOSTED BY: Prof Michael Bremner, UTS Centre for Quantum Software and Information 

ABSTRACT: Given the large push by academia and industry (e.g., IBM and Google), quantum computers with hundred(s) of qubits are at the brink of existence with the promise of outperforming any classical computer. Demonstration of computational advantages of noisy near-term quantum computers over classical computers is an imperative near-term goal. The foremost candidate task for showing this is Random Circuit Sampling (RCS), which is the task of sampling from the output distribution of a random circuit. This is exactly the task that recently Google experimentally performed on 53-qubits. Stockmeyer's theorem implies that efficient sampling allows for estimation of probability amplitudes. Therefore, hardness of probability estimation implies hardness of sampling. We prove that estimating probabilities to within small errors is #P-hard on average (i.e. for random circuits), and put the results in the context of previous works. Some ingredients that are developed to make this proof possible are construction of the Cayley path as a rational function valued unitary path that interpolate between two arbitrary unitaries, an extension of Berlekamp-Welch algorithm that efficiently and exactly interpolates rational functions, and construction of probability distributions over unitaries that are arbitrarily close to the Haar measure. 

RELATED ARTICLES: Unitary-valued paths, and an algebraic proof technique in complexity theory: https://ramismovassagh.wordpress.com/... Cayley path and quantum computational supremacy: A proof of average-case #P−hardness of Random Circuit Sampling with quantified robustness: https://arxiv.org/abs/1909.06210 Efficient unitary paths and quantum computational supremacy: A proof of average-case hardness of Random Circuit Sampling: https://arxiv.org/abs/1810.04681 

OTHER LINKS: Ramis Movassagh Personal Webpage: https://ramismovassagh.wordpress.com/ 

MIT-IBM Watson AI Lab: https://mitibmwatsonailab.mit.edu/

During this time of lockdown, the centre for quantum software and information (QSI) at the University of Technology Sydney has launched an online seminar series.  With talks once or twice a week from leading researchers in the field, meQuanics is supporting this series by mirroring the audio from each talk.  I would encourage if you listen to this episode, to visit and subscribe to the UTS:QSI YouTube page to see each of these talks with the associated slides to help it make more sense.

https://youtu.be/Dg6Q_F9uI8s

Silicon spin qubits gain traction for large-scale quantum computation and simulation.  

TITLE: A Scalable “Spins-Inside” Quantum Processor and Simulator SPEAKER: Prof Lieven Vandersypen 

AFFILIATION: QuTech, Kavli Institute of Nanoscience, Dept of Quantum Nanoscience, Delft University of Technology, Netherlands 

HOSTED BY: Dr JP (Juan Pablo) Dehollain, UTS Centre for Quantum Software and Information  

ABSTRACT:  Excellent control of over physical 50 qubits has been achieved, but can we also realize 50 fault-tolerant qubits? Here quantum bits encoded in the spin state of individual electrons in silicon quantum dot arrays have emerged as a highly promising avenue. In this talk, I will present our vision of a large-scale spin-based quantum processor, and our ongoing work to realize this vision. I will also show how the same platform offers a powerful platform for analog quantum simulation of Fermi-Hubbard physics and quantum magnetism.  

RELATED ARTICLES: Physics Today 72(8), 38 (2019) npj Quantum Information 3, 34 (2017) Nature 555, 633 (2018) Science 359, 1123 (2018) Phys. Rev. X 9, 021011 (2019) Nature 579, 528 (2020) Nature 580, 355 (2020)  

OTHER LINKS: Vandersypen Lab: qutech.nl/vandersypen-lab/ Delft University of Technology: https://www.tudelft.nl/

During this time of lockdown, the centre for quantum software and information (QSI) at the University of Technology Sydney has launched an online seminar series.  With talks once or twice a week from leading researchers in the field, meQuanics is supporting this series by mirroring the audio from each talk.  I would encourage if you listen to this episode, to visit and subscribe to the UTS:QSI YouTube page to see each of these talks with the associated slides to help it make more sense.

https://youtu.be/rOKpLd4X9jE

Finding the ground state of the Hubbard model using hybrid quantum-classical computing.  

TITLE: Strategies for solving the Fermi-Hubbard model on near-term quantum computers 

SPEAKER: Lana Mineh 

AFFILIATION: Quantum Engineering Technology Labs, University of Bristol, UK 

HOSTED BY: Prof Michael Bremner, UTS Centre for Quantum Software and Information  

ABSTRACT:  The Fermi-Hubbard model is of fundamental importance in condensed-matter physics, yet is extremely challenging to solve numerically. Finding the ground state of the Hubbard model using variational methods has been predicted to be one of the first applications of near-term quantum computers. Here we carry out a detailed analysis and optimisation of the complexity of variational quantum algorithms for finding the ground state of the Hubbard model, including costs associated with mapping to a real-world hardware platform. The depth complexities we find are substantially lower than previous work. We performed extensive numerical experiments for systems with up to 12 sites. The results suggest that the variational ansätze we used -- an efficient variant of the Hamiltonian Variational ansatz and a novel generalisation thereof -- will be able to find the ground state of the Hubbard model with high fidelity in relatively low quantum circuit depth. Our experiments include the effect of realistic measurements and depolarising noise. If our numerical results on small lattice sizes are representative of the somewhat larger lattices accessible to near-term quantum hardware, they suggest that optimising over quantum circuits with a gate depth less than a thousand could be sufficient to solve instances of the Hubbard model beyond the capacity of classical exact diagonalisation.  

RELATED ARTICLES:  Strategies for solving the Fermi-Hubbard model on near-term quantum computers: https://arxiv.org/abs/1912.06007 

OTHER LINKS: Quantum Engineering Technology Labs: bristol.ac.uk/qet-labs

During this time of lockdown, the centre for quantum software and information (QSI) at the University of Technology Sydney has launched an online seminar series.  With talks once or twice a week from leading researchers in the field, meQuanics is supporting this series by mirroring the audio from each talk.  I would encourage if you listen to this episode, to visit and subscribe to the UTS:QSI YouTube page to see each of these talks with the associated slides to help it make more sense.

https://youtu.be/a5aa-vU2AHo

Controls and frames: A new approach to quantum noise spectroscopy  

TITLE: Noise Cancellation and your Quantum Computer 

SPEAKER: Dr Gerardo Paz Silva 

AFFILIATION: Centre for Quantum Dynamics, Griffith University, Brisbane, Qld, Australia 

HOSTED BY:  A/Prof Chris Ferrie, UTS Centre for Quantum Software and Information  

ABSTRACT: Noise cancellation, as in everyday headphones, requires the ability to characterize & filter out the noise affecting a system one wants to protect. The last few years have seen the birth of increasingly more powerful Quantum Noise Spectroscopy (QNS) protocols, capable of characterizing the noise affecting a quantum system of interest. However, while many of these protocols have been experimentally verified, all demonstrations have been so far limited to characterizing injected noise. More importantly, even theoretically a fully general protocol is still non-existent. In this talk I will introduce our new approach to the problem, which overcomes these limitations. I will argue that by characterizing only the portions of the noise that are relevant a given set of control capabilities, e.g., available to a particular experiment, many of the existing difficulties in designing a fully general QNS protocol disappear. I describe the key ingredients allowing this and exemplify our results via two paradigmatic examples.  

OTHER LINKS: Centre for Quantum Dynamics, Griffith University griffith.edu.au/centre-quantum-dynamics

During this time of lockdown, the centre for quantum software and information (QSI) at the University of Technology Sydney has launched an online seminar series.  With talks once or twice a week from leading researchers in the field, meQuanics is supporting this series by mirroring the audio from each talk.  I would encourage if you listen to this episode, to visit and subscribe to the UTS:QSI YouTube page to see each of these talks with the associated slides to help it make more sense.

https://youtu.be/7wbK_9Sjnv8

Protecting and leveraging quantum machine learning algorithms on a future quantum internet  

TITLE: Introducing Adversarial Quantum Learning: Security and machine learning on the quantum internet 

SPEAKER: Assistant Professor Nana Liu 

AFFILIATION: Shanghai Jiao Tong University, PR China 

HOSTED BY: A/Prof Chris Ferrie, UTS Centre for Quantum Software and Information  

ABSTRACT:  In the classical world, there is a powerful interplay between security and machine learning deployed in a network, like on the modern internet. What happens when the learning algorithms and the network itself can be quantum? What are the new problems that can arise and can quantum resources offer advantages to their classical counterparts? We explore these questions in a new area called adversarial quantum learning, that combines the area of adversarial machine learning, which investigates security questions in machine learning, and quantum information. For the first part of the talk, I’ll introduce adversarial machine learning and some exciting potential prospects for contributions from quantum information and computation. For the second part of the talk, I’ll present two new works on adversarial quantum learning. Here we are able to quantify the vulnerability of quantum algorithms for classification against adversaries and learn how to leverage quantum noise to improve its robustness against attacks.    

RELATED ARTICLES: Vulnerability of quantum classification to adversarial perturbations: https://arxiv.org/abs/1905.04286Quantum noise protects quantum classifiers against adversaries: https://arxiv.org/abs/2003.09416 

OTHER LINKS: nanaliu.weebly.com/

During this time of lockdown, the centre for quantum software and information (QSI) at the University of Technology Sydney has launched an online seminar series.  With talks once or twice a week from leading researchers in the field, meQuanics is supporting this series by mirroring the audio from each talk.  I would encourage if you listen to this episode, to visit and subscribe to the UTS:QSI YouTube page to see each of these talks with the associated slides to help it make more sense.

https://youtu.be/qYuxOx4Z8Yk

A growing methodological problem for practical quantum algorithms research 

TITLE: Why are quantum algorithms papers so #!@*&% long? 

SPEAKER: Dr Yuval Sanders 

AFFILIATION: Centre for Quantum Software and Information, University of Technology Sydney 

ABSTRACT: In this talk I discuss the results of two of my recent quantum algorithms papers: arXiv:2007.07391 and arXiv:2110.05708. Both of these papers are 70+ pages in length and quite dense, which needs some explanation because the underlying ideas are not particularly complicated. The reason for the length is that we, the authors, are effectively compiling quantum algorithms by hand, and we are doing a very crude job of it. I will explain that increasing paper lengths are evidence for a growing methodological problem for practical quantum algorithms research. I will also explain why that methodological problem is in large part responsible to ongoing mistakes in media when attempting to articulate the real-world applications of quantum computers.   

HOSTED BY: Associate Professor Troy Lee, Centre for Quantum Software and Information, University of Technology Sydney, Australia 

RELATED PAPERS: https://arxiv.org/abs/2007.07391; https://arxiv.org/abs/2110.05708

During this time of lockdown, the centre for quantum software and information (QSI) at the University of Technology Sydney has launched an online seminar series.  With talks once or twice a week from leading researchers in the field, meQuanics is supporting this series by mirroring the audio from each talk.  I would encourage if you listen to this episode, to visit and subscribe to the UTS:QSI YouTube page to see each of these talks with the associated slides to help it make more sense.

https://youtu.be/OfY7lFIBTGY

Self-Guided Quantum Learning: Estimation via optimisation applied to quantum estimation  

TITLE: Self-Guided Quantum Learning 

SPEAKER: Associate Professor Chris Ferrie 

AFFILIATION: Centre for Quantum Software and Information, University of Technology Sydney, Australia 

HOSTED BY: Dr Clara Javaherian, UTS Centre for Quantum Software and Information, Australia  

ABSTRACT:  Quantum state learning is often understood as a data analytics problem—large amounts of data collected from many prior repetitions of incompatible measurements need to be churned into a single estimate of a quantum state or channel. In this talk, I will present an adaptive optimisation algorithm which achieves the same goal, but at a drastic reduction in time and space complexity.   

RELATED ARTICLES: Experimental realization of self-guided quantum process tomography: https://arxiv.org/abs/1908.01082Experimental Demonstration of Self-Guided Quantum Tomography: https://arxiv.org/abs/1602.04194Self-guided quantum tomography: https://arxiv.org/abs/1406.4101 

OTHER LINKS: Chris Ferrie: csferrie.com/

During this time of lockdown, the centre for quantum software and information (QSI) at the University of Technology Sydney has launched an online seminar series.  With talks once or twice a week from leading researchers in the field, meQuanics is supporting this series by mirroring the audio from each talk.  I would encourage if you listen to this episode, to visit and subscribe to the UTS:QSI YouTube page to see each of these talks with the associated slides to help it make more sense.

https://youtu.be/L_VldJN_k-4

Bosonic mode error correcting codes: Quantum oscillators with an infinite Hilbert space  

TITLE: Quantum computing with rotation-symmetric bosonic codes 

SPEAKER: Assistant Professor Josh Combes 

AFFILIATION: University of Colorado Boulder, CO, USA 

HOSTED BY: A/Prof Chris Ferrie, UTS Centre for Quantum Software and Information  

ABSTRACT: Bosonic mode error correcting codes are error correcting codes where a qubit (or qudit) is encoded into one or multiple bosonic modes, i.e., quantum oscillators with an infinite Hilbert space. In the first part of this talk I will give an introduction codes that have a phase space translation symmetry, i.e. the Gottesman-Kitaev-Preskill aka GKP, and codes that obey a rotation symmetry. Moreover, I will survey the impressive experimental progress on these codes. The second part of the talk I focus on single-mode codes that obey rotation symmetry in phase space, such as the the well known Cat and Binomial codes. I will introduce a universal scheme for this class of codes based only on simple and experimentally well-motivated interactions. The scheme is fault-tolerant in the sense that small errors are guaranteed to remain small under the considered gates. I will also introduce a fault-tolerant error correction scheme based on cross-Kerr interactions and  imperfect destructive phase measurement (e.g., a marginal of heterodyne). Remarkably, the error correction scheme approaches the optimal recovery map for Cat and Binomial codes when the auxiliary modes are error free. We numerically compute break-even thresholds under loss and dephasing, with ideal auxiliary systems.  If time permits I will discuss the search for optimized codes and progress towards genuine fault tolerance.  

Joint work with Arne Grimsmo, USyd and Ben Baragiola, RMIT

During this time of lockdown, the centre for quantum software and information (QSI) at the University of Technology Sydney has launched an online seminar series.  With talks once or twice a week from leading researchers in the field, meQuanics is supporting this series by mirroring the audio from each talk.  I would encourage if you listen to this episode, to visit and subscribe to the UTS:QSI YouTube page to see each of these talks with the associated slides to help it make more sense.

https://youtu.be/Vq8itsMG39w

This talk explains the quantum supremacy milestone achieved by Google.  TITLE: Quantum supremacy using a programmable superconducting processor SPEAKER: Prof Sergio Boixo AFFILIATION: Google Research, Los Angeles, USA HOSTED BY: Prof Michael Bremner, UTS Centre for Quantum Science and Information  ABSTRACT: The promise of quantum computers is that certain computational tasks might be executed exponentially faster on a quantum processor than on a classical processor. A fundamental challenge is to build a high-fidelity processor capable of running quantum algorithms in an exponentially large computational space. Here we report the use of a processor with programmable superconducting qubits to create quantum states on 53 qubits, corresponding to a computational state-space of dimension 2^53 (about 10^16). Measurements from repeated experiments sample the resulting probability distribution, which we verify using classical simulations. Our Sycamore processor takes about 200 seconds to sample one instance of a quantum circuit a million times—our benchmarks currently indicate that the equivalent task for a state-of-the-art classical supercomputer would take approximately 10,000 years. This dramatic increase in speed compared to all known classical algorithms is an experimental realization of quantum supremacy for this specific computational task, heralding a much-anticipated computing paradigm.

During this time of lockdown, the centre for quantum software and information (QSI) at the University of Technology Sydney has launched an online seminar series.  With talks once or twice a week from leading researchers in the field, meQuanics is supporting this series by mirroring the audio from each talk.  I would encourage if you listen to this episode, to visit and subscribe to the UTS:QSI YouTube page to see each of these talks with the associated slides to help it make more sense.

https://youtu.be/3OS7Pq6JoDY

Q#, a quantum-focused domain-specific language explicitly designed to correctly, clearly and completely express quantum algorithms.   

TITLE: Empowering Quantum Machine Learning Research with Q# 

SPEAKER: Dr Christopher Granade 

AFFILIATION: Quantum Systems, Microsoft, Washington, USA HOSTED BY: A/Prof Chris Ferrie, UTS Centre for Quantum Software and Information  

ABSTRACT: In this talk, I will demonstrate how the Q# quantum programming language can be used to start exploring quantum machine learning, using a binary classification problem as an example. I will describe recent work in QML algorithms for classification, and show how Q# allows implementing and using this classifier through high-level quantum development features. Finally, I will discuss how these approaches can be used as part of a reproducible research process to share your explorations with others.