After Shor published a quantum algorithm that could solve the prime factorization problem efficiently, the interest in quantum computing (in general) and quantum optimization (in particular) has skyrocketed. Also according to McKinsey, quantum optimization has a bright future with billions of Euros of investments that have already been made.

We are still waiting, however, for the big breakthrough. In this presentation, we talk about the challenges of using current NISQ machines to solve optimization problems and also discuss the limitations that may not be possible to overcome even if we have a noiseless universal quantum computer.

Quantum physics and chemistry have provided well-recognized theoretical tools to predict the behavior of molecules and materials described by the Schrödinger equation. However, many problems with high industrial and societal impact remain intractable for classical computers, urging us to reconsider our preconceptions and shift gears. The world of the infinitely small obeys the laws of quantum mechanics, suggesting the need for a machine governed by the same physics: this marks the birth of quantum computers, a new technological revolution which promises a quantum advantage (speed-up) over classical computers. In this talk, I will present quantum algorithms designed to address the electronic structure problem for both ground and excited states [1-3], utilizing quantum implementations of wavefunction theory and density functional theory [4]. I will explore the practicality of these approaches for both near- and long-term applications and outline potential pathways toward achieving quantum advantage.

[1] Nakanishi, K. M., Mitarai, K., & Fujii, K. (2019). Subspace-search variational quantum eigensolver for excited states. Physical Review Research, 1(3), 033062.
[2] Yalouz, S., Senjean, B., Günther, J., Buda, F., O’Brien, T. E., & Visscher, L. (2021). A state-averaged orbital optimized hybrid quantum–classical algorithm for a democratic description of ground and excited states. Quantum Science and Technology, 6(2), 024004.
[3] Yalouz, S., Koridon, E., Senjean, B., Lasorne, B., Buda, F., & Visscher, L. (2022). Analytical nonadiabatic couplings and gradients within the state-averaged orbital-optimized variational quantum eigensolver. Journal of chemical theory and computation, 18(2), 776-794.
[4] Senjean, B., Yalouz, S., & Saubanère, M. (2023). Toward density functional theory on quantum computers?. SciPost Physics, 14(3), 055

As quantum training is flourishing in French Universities, several challenges arise. It is well-known that the hype around Quantum sometimes undermines the seriousness of quantum science. However, some universities have developed worldwide expertise for decades on the topic, which makes France one of the leading nations. This conference aims at deciphering the current state of quantum training in France.

Quantum computing (QC) aims at addressing computations that are currently intractable by conventional supercomputers. However, to be attractive for sustainable research and industrial investments, QC must not be limited to specific computations but also be seen as potential accelerator for general purpose simulations in high-performance scientific computing.

In this talk we explain how core tasks in scientific computing can be addressed by quantum algorithms, possibly combined with classical ones. In particular we describe recent advances in algorithms for decomposing and handling matrices (generic, or coming from PDE’s) in quantum computers. We also present a promising method for the solution of linear systems of equations with possible improvement in terms of accuracy and cost for the solution.

Quantum HF lines & harness is an important part of the QC enabling technologies due to the exponential growth of the number of Q-bit per cryostat (system scale-up) that impacts the overall performance of the system. These microwave links include Cables/Assemblies, Connector, Attenuator, Board to board and switches with specific requirements for each stage of the thermal dilution refrigerator.
To manage the scale up of the QC, high density harnesses are required like side loaders to support hundreds of lines.

The nanotube gatemon, Jean-Damien Pillet, QCMX Lab Ecole Polytechnique
Many hardware platforms already provide qubits with remarkable performance. Yet, despite impressive advancements in recent years, these capabilities remain limited for developing fully functional quantum technologies. This drives the need to continue exploring novel quantum systems, especially by investigating the potential of emerging materials. Among these, carbon nanotubes stand out as promising candidates, as they represent the most elementary form of a quantum conductor – a single molecule – that can be integrated into an electrical circuit. They combine simplicity with controllability, offering the potential for both strong coherence and effective quantum control once mastered.

In this talk, I will present the work we realized at Ecole Polytechnique, in the QCMX group, on a carbon nanotube-based superconducting qubit, examining its performance and discussing the insights it provides into the current limitations of carbon nanotubes as quantum conductors, as well as the future challenges for this material in quantum technology.”

Carbon nanotube at C12, Quentin Schaeverbeke
Semiconductor quantum dots integrated into microwave cavities have been considered a compelling candidate for the making of a quantum processor as a controllable system achieving long range interaction. However, after the integration of the semiconducting circuit with the resonator, coherence times have been shown to degrade making the manipulation of a quantum state more difficult. Carbon nanotube implementations of the technology have recently shown the longest coherence times measured so far in these type of architectures but still need drastic improvements. We will discuss the developments made at C12 for improving carbon based qubits from fabrication to measurement of the chips.

Alistair Milne, Quantinuum

Scaling the QCCD Architecture for Trapped-Ion Quantum Computers

I will present an overview of Quantinuum’s trapped-ion quantum computing systems, highlighting key capabilities of the current generation of devices. Looking to the future, I will outline Quantinuum’s approach to scaling the trapped-ion QCCD architecture, presenting a development roadmap towards a fully fault-tolerant universal quantum computer and discussing the technical progress made towards this goal.

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Winfried Hensinger, Quantum Technologies

Trapped-ion Quantum Computing

I will provide an overview of the key concepts of trapped ion quantum computing in the context of developing quantum computing for disruptive industry applications. I will highlight opportunities of the overall hardware platform and highlight transport-based quantum computing as a powerful architecture for this purpose. I will identify practical challenges and hurdles that need to be overcome in order to build practical utility scale quantum computers.

As the global leader of quantum computing, IBM is deeply engaged through its roadmap on the scaling and industrialization of our quantum computing technology.
During this presentation, we will review our current vision of the future, and share some challenges, from both hardware, software, but also adoption views.

This presentation will highlight the main challenges involved in scaling superconducting qubit quantum computers. It will explain how the IQM addresses the different aspects related to quality, quantity, and volume. And more specifically what role the future Quantum Factory in Grenoble will play in achieving these objectives.

The Qaptiva platform from Eviden enables the deployment of a complete quantum computing environment on an HPC cluster, with the integration of quantum processors (QPU). We will review recent deployments in national computing centers and discuss the main challenges of scaling up.

Our presentation will address the challenge of building a SaaS platform that offers quantum algorithms. Simply put, we face three key challenges in scaling up:

The challenge of market relevance, in a landscape where hardware maturity has not yet been achieved.
The challenge of skills, both in theoretical aspects (mathematics, data science, and quantum) and in navigating the diverse technological offerings, each with its own development environment.
The challenge of industrialization, as we must provide a “”as-a-service”” platform capable of functioning in “”production,”” even though most quantum capabilities currently do not offer service-level guarantees.

It is a difficult and ambitious challenge but very exciting, and we have strategies to address these different aspects.

Integration of QC within HPC compute centers is on the way, making QC a new compute paradigm. QC  offers ways to address previously unreachable problems such as NP problems. On the other hand, the integration of QC in HPC  is quite challenging, requiring the development of new middleware layers.
When considering QC, challenges appears in several domains, involving system oriented features as well as high level libraries providing building blocks for end-user to build HPC/QC ready applications. In order to address this integration, middleware should be structured and interfaces should be defined.