The European Commission’s Vision for Quantum Engineering: Challenges and Opportunities in EU-Funded Projects

This report provides an overview of the development and potential use of Fault-Tolerant Quantum Computers (FTQC) to reliably perform complex computations by overcoming the challenges posed by errors and noise inherent in quantum systems.

After outlining the current status of quantum advantage and the requirements to achieve it, the report describes the role of quantum error correction codes in the design of FTQC architectures. It then reviews the progress made in the five most advanced physical technologies worldwide for building such systems, along with the key challenges they face in scaling up to support practical, real-world applications.

The report also examines the technological and economic environment surrounding quantum computing, explores how to benchmark and assess performance, and discusses the future coexistence of quantum computers with other computing technologies such as 3D silicon architectures, artificial intelligence, and high-performance computing (HPC) systems.

Benchmarking quantum computers helps to quantify them and bringing the technology to the market. Various application-level metrics exist to benchmark a quantum device at an application level.

This paper presents a revised holistic scoring method called the Quantum Application Score (QuAS) incorporating strong points of previous metrics, such as QPack and the Q-score.

We discuss how to integrate both and thereby obtain an application-level metric that better quantifies the practical utility of quantum computers.

We evaluate the new metric on different hardware platforms such as D-Wave and IBM as well as quantum simulators of Quantum Inspire and Rigetti.

Quantum computers and algorithms are often analyzed using benchmarking and/or resource consumption using unrelated methodologies. The metric-noise-resource (MNR) framework is a holistic framework to analyze the metric of performance and the corresponding resource consumption using a unified methodology while also accounting for the effect of noise.
We use MNR for VQE algorithm to explore the tradeoffs between M-N-R by identifying the key algorithmic control parameters namely the number of gates and number of iterations, and deriving scaling laws using a toy model of 1D Heisenberg spin chain.
This novel methodology enables us to define the algorithmic efficiency, identify the algorithmic parameters corresponding to maximal efficiency, and study the change in efficiency with algorithmic resource cost. We compare the energetic cost of classical methods with the corresponding quantum resource cost converted into physical energy consumption for typical hardware platforms.
We conclude that classical energy consumption is much lower than the quantum one, rendering quantum energetic advantage to be unlikely for VQEs.

Application-oriented benchmarking of quantum computing involves measuring multiple and often conflicting Key Performance Indicators (KPIs). These KPIs must be combined to assess the overall quality of a solution or to compare two different solutions. Multi-Criteria Decision Aiding (MCDA) is a suitable methodology for this purpose.
We begin by discussing the state of the art in MCDA. Then we identify an appropriate MCDA approach for quantum benchmarking, which involves two steps to generate an overall score from the vector of KPIs: first, normalizing the KPIs, and then aggregating the normalized scores. We will also discuss on some specificities of Quantum Benchmarking in the application of MCDA.

Utility-scale quantum computing refers to quantum computers that are powerful and reliable enough to solve real-world problems that classical computers cannot.
However, advancements in quantum emulation, particularly through tensor-network methods, enable efficient simulation of quantum algorithms with hundreds of qubits and high entanglement on classical computers, making the concept of « quantum utility » a moving target.
For this reason we present comprehensive benchmark results on utility scale quantum emulators for evaluating complex quantum algorithms with 100 qubits or more.
This provides insights into the performance of such emulators on real-world quantum computing tasks, highlights possible areas to improve performance and applicability of quantum emulators and sets a reference point for application oriented benchmarking of quantum computer systems beyond the limits of classical computing.

Over the past decade, quantum computing systems have grown in both scale and capability and are now capable of executing increasingly complex quantum applications. Benchmarking tools and methodologies play a crucial role in reliably gauging how these systems are progressing towards true quantum utility.
In this talk, we briefly review the multiple classes of quantum benchmarks, distinguishing between rigorous scientific techniques for evaluating component and system-level performance and the application-focused benchmarking methodologies that provide insight into viability, cost, and solution quality. Our analysis focuses particularly on application-oriented benchmarks developed by the Quantum Economic Development Consortium (QED-C) and other emerging benchmark projects. We discuss lessons learned from working with the quantum community, including how device producers and users have different perspectives and priorities.
We share insights gained from studying the performance of applications executed on large-scale ideal quantum simulators compared to results obtained from execution on noisy quantum hardware. We highlight a few of the challenges we expect to see as quantum computation advances from intermediate-scale (NISQ) devices to large-scale fault-tolerant and error-corrected quantum computers (FTQC) that augment classical high-performance computing systems.
To accelerate progress, we are making exploration tools available to a broader user community through an integrated framework that supports diverse use cases and multiple quantum computing platforms.

It is crucial to assess the utility of quantum computers to evaluate their potential to bring computational advantage for near and long-term business decisions.
While several companies developing or using quantum chips claim that the quantum utility era will begin soon (or has already begun), others remain more cautious about the near-term potential of quantum computers, waiting for the fault-tolerance era to develop useful applications.
This talk will present the Quantum Benchmark Zoo initiative, an open-source and free website aiming at objectively and critically summarizing existing benchmarking approaches and results.

Today’s Quantum Computing (QC) horizon is focused on Quantum Utility. To that end, application oriented benchmarks are on the ranks to play a key role in identifying in what domains QC will sooner rather than later reach the quantum utility threshold.

In this talk I will present some elements on how to build metrics toward the measurement of application kernels in QC, what are the early results and what reflections they convey.

The European Quantum Computing Benchmarking Coordination Committee (EQCBC) was established to coordinate European activities in benchmarking quantum computers (https://qt.eu/working-groups/european-quantum-computing-benchmarking-coordination-committee). Architectures for quantum computing can only be scaled up when accompanied by suitable benchmarking techniques. Benchmarking becomes increasingly important as quantum technologies mature.

The European Quantum Computing Benchmarking Coordination Committee (EQCBC) has released a paper providing a comprehensive overview of the current state and recommendations for systematic benchmarking of quantum computers. The document discusses component-level, system-level, software-level, HPC-level, and application-level benchmarks. Recommendations for future steps emphasise the need to develop standardised evaluation routines and integrate benchmarks with broader quantum technology activities:

Systematic benchmarking of quantum computers: status and recommendations

https://arxiv.org/pdf/2503.04905

Quantum computing is reaching a tipping point, but access and evaluation remain fragmented. Aqora is driving democratization in the field by launching open quantum challenges and hosting quantum-specific datasets for benchmarking progress. By fostering a competitive yet collaborative ecosystem Aqora helps accelerate algorithmic innovation, identify promising talent, and enable reproducible research across the quantum community. This talk will highlight how these efforts support both industrial applications and foundational research.

We introduce BenchQC, a quantum computing benchmarking project that focuses on application-centric benchmarking using diverse industry use cases.

Building upon the open-source platform QUARK, we evaluate key metrics across the quantum software stack to identify trends towards quantum utility, and distinguish viable research directions.

This initiative contributes to the broader effort of establishing reliable benchmarking standards that drive the transition from experimental demonstrations to practical quantum advantages.