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An International Network on Quantum Annealing

The International Network on Quantum Annealing (INQA) will for the first time establish a mechanism by which four global collaborations come together to share technical and intellectual know-how and critically analyse developments in theoretical and experimental research in quantum annealing.

Upcoming Seminars

17 SeptemberÌý2024Ìý| 08:00ÌýUTC | Vrinda Mehta |ÌýÌýForschungszentrum Jülich

Quantum annealing and its variants: Application to quadratic unconstrained optimization

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We study and compare the performance of the numerical implementation of quantum annealing and physical quantum annealing systems from D-Wave Quantum Systems Inc. for solving a specially constructed set of hard 2-SAT problems using three metrics: the probability of the algorithm to solve the problem, its ability to find all the solutions to the problem if the problem has more than one solution, and the scaling of the time to solution as a function of the problem size. Furthermore, by means of simulations, we introduce two modifications in the standard quantum annealing algorithm, and gauge the performance of the modified algorithms. These modifications are the addition of a trigger Hamiltonian to the standard quantum annealing Hamiltonian, or a change in the initial Hamiltonian of the annealing Hamiltonian. We choose the trigger Hamiltonian to have either ferromagnetic or antiferromagnetic transverse couplings, while the additional higher-order couplings added to the typically chosen initial Hamiltonian are ferromagnetic.

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24ÌýSeptemberÌý2024Ìý| 16:00ÌýUTC | Wojtek FedorkoÌý|ÌýÌýTriumf

Quantum Variational Autoencoder for Simulation of the Calorimetric Detectors at the Large Hadron Collider Experiments

As CERN approaches the launch of the High Luminosity Large Hadron Collider (HL-LHC) by the decade’s end, the computational demands of traditional simulations have become untenably high. Projections show millions of CPU-years required to create simulated datasets - with a substantial fraction of CPU time devoted to calorimetric simulations. This presents unique opportunities for breakthroughs in computational physics. We show how Quantum Variational Autoencoder can be used for the purpose of creating synthetic, realistically scaled calorimetry dataset. The model is constructed by combining D-Wave’s Quantum Annealer processor with a hierarchical Deep Learning architecture, increasing the timing performance with respect to first principles simulations and Deep Learning models alone, while maintaining current state-of-the-art data quality. Some details on new strategies for the mitigation of freezout effects and effective temperature scaling will be presented.

Visit past seminars to view a list of all of our past seminars and theirÌýabstracts.

If you miss any of our live seminars you can watch our previous sessions on our .

About INQA

The INQA network unifies the research activities ofÌýmajor global collaborations in quantum annealing in North America, Japan, the European Union and the United Kingdom.

By hosting weekly on-line seminars and annual international conferences and by funding exchange visits, the INQA network will address the key topics which will enable quantum annealing to move towards a true quantum scaling advantage over classical approaches to NP-hard computational problems.Ìý

TheÌýtopics INQA will focus on include:

• Exploiting quantum coherence
• Extending the order and degree of qubit interactions
• Strategies for error correctionÌý
• Exploiting diabaticity and non-stoquasticity in a systematic way

The network will be led by ProfessorÌýPaul Warburton of Â鶹´«Ã½ÊÓƵÍøÕ¾, who is a co-investigator in the UK’s Quantum Computation and Simulation (QCS) Hub and in the recently-announced QEVEC project. He was also previously a co-investigator in the US-led QEO and QAFS collaborations.

Members of the management board include:Ìý

• Prof Paul Warburton (Â鶹´«Ã½ÊÓƵÍøÕ¾, UK)
• Dr Pol Forn-Díaz (IFAE, Spain)
• Dr Shiro Kawabata (AIST, Japan)
• Prof Viv Kendon (University of Strathclyde, UK)
• Dr Jamie Kerman (MIT Lincoln Lab, USA)

INQA is supported by a International Network Grant from the UK Engineering and Physical Sciences Research Council.Ìý

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