Comment comparez-vous les performances du recuit quantique et des modèles basés sur des portes sur différents domaines problématiques ?

Performance is evaluated through problem-specific benchmarks like optimization accuracy or runtime efficiency. Quantum annealing is tested on combinatorial optimization problems, while gate-based models are evaluated using quantum speedup and fidelity across various domains, including cryptography, machine learning, and chemistry simulations.

Benchmarking the performance of quantum annealing and gate-based models involves testing both approaches on various problem domains, such as optimization, machine learning, and combinatorial tasks. For quantum annealing, performance is measured by how well it finds global minima in optimization problems, typically evaluated through success probabilities, convergence speed, and solution quality. Gate-based models are benchmarked by analyzing quantum circuit depth, gate fidelity, and execution time across similar tasks. Cross-domain comparisons focus on scalability, error rates, and practical hardware constraints. Hybrid classical-quantum methods and problem-specific adaptations often provide more comprehensive benchmarking insights.

Quantum annealing is a specialized quantum computing method focused on finding the global minimum of a given objective function over a landscape of potential solutions. It is particularly effective for optimization problems, where it can explore vast solution spaces more efficiently than classical algorithms. By leveraging quantum tunneling, quantum annealing can escape local minima, thus enhancing its performance on complex, high-dimensional problems. Benchmarking its performance involves comparing solution quality and computational speed against classical methods and other quantum approaches across various problem domains such as logistics, finance, and materials science.

Quantum annealing is a quantum technique in computing for the global minimization of an objective function, particularly for optimization problems. It efficiently explores very large solution spaces by availing itself of two quantum effects: superposition and tunnelling. Hence, it suits quite well complex problems of high dimensionality, typically related to logistics, finance, or materials science. The classical version of simulated annealing conceptually inspires it; quantum annealing smoothly decreases the energy of a quantum system to find optimal or near-optimal solutions.

Gate-based quantum computing uses quantum bits (qubits) and quantum gates to perform computations, similar to classical logic gates. This model excels in solving complex, non-deterministic polynomial (NP) problems by leveraging superposition and entanglement to process vast amounts of data simultaneously. Benchmarking its performance involves assessing computational accuracy, speed, and scalability compared to classical algorithms and other quantum models. Key domains for evaluation include cryptography, material science, and complex simulations, where gate-based models show potential for significant breakthroughs in computational efficiency.

Performance metrics for quantum computing models include solution accuracy, computational speed, and scalability. Accuracy measures how closely the computed results align with the expected outcomes. Speed evaluates how quickly a quantum algorithm can solve a problem compared to classical methods. Scalability assesses the model's ability to handle increasing problem sizes and complexity. Additionally, metrics like qubit coherence time and error rates are crucial for understanding the practical feasibility and reliability of quantum computations, guiding improvements and real-world application readiness.

Benchmarking methods for quantum computing involve comparing quantum algorithms' performance against classical counterparts on standardized problem sets. Key techniques include evaluating solution quality, computational speed, and resource efficiency. Metrics such as time-to-solution, success probability, and energy consumption are analyzed. Additionally, scalability tests measure how performance changes with increasing problem sizes. Hybrid benchmarks, combining classical and quantum approaches, offer insights into practical advantages. These methods ensure a comprehensive understanding of quantum models' strengths and limitations across diverse domains, guiding future development and optimization.

One thing I don’t see mentioned here yet is the emphasis on finding the “right” problems to benchmark, arguably that’s the most non trivial part for example for quantum optimization. Just because a problem is NP-hard doesn’t make it hard. Just because a problem is hard to solve classically doesn’t mean it’s a good one for quantum. Just because it’s hard for small number of variables that COULD fit on current NISQ quantum registers doesn’t mean it’s good for quantum. It’s a subtle combination of all these aspects that make problems good for quantum and the community is actively working on open source benchmarks for example in quantum optimization.

When benchmarking quantum computing models, it's essential to consider algorithm-specific requirements, such as error correction needs and hardware compatibility. Variability in quantum hardware performance across different platforms also impacts results. Environmental factors like temperature and electromagnetic interference can affect qubit stability. Additionally, real-world applicability should be assessed, including the integration of quantum solutions with existing classical systems. Finally, continual updates in quantum technology necessitate iterative benchmarking to reflect the latest advancements and ensure relevant, up-to-date evaluations.

Important is also to note that even a problem can be solved on a quantum computer doesn't mean that there is a business case in doing so. Many NP-hard problems can be solved reasonably well using heuristics. Even though heuristics don't usually find global optima, often the found solution is good enough for business purposes. So, finding a good candidate for benchmarking requires technical considerations, but also business ones.