Benchmarking Standards for Quantum Algorithms

How should we measure if there is any benefit from using quantum machine learning methods in QNNs? Raw accuracy? I discovered an interesting study that for sure will spark some ideas about how to answer this question. I recently came across an insightful study titled "QMetric: Benchmarking Quantum Neural Networks Across Circuits, Features, and Training Dimensions". This paper introduces a much-needed tool in the evolving field of hybrid quantum-classical machine learning. The core challenge addressed by this research is the lack of principled, interpretable, and reproducible tools for evaluating hybrid quantum-classical models beyond traditional metrics like raw accuracy. These standard diagnostics don't capture crucial quantum characteristics such as circuit expressibility, entanglement structure, barren plateaus, or the sensitivity of quantum feature maps. To bridge this gap, the authors present QMetric, a modular and extensible Python package (for now available for Qiskit and PyTorch). QMetric offers a comprehensive suite of interpretable scalar metrics designed to evaluate quantum neural networks (QNNs) across three complementary dimensions: * Quantum circuit behavior: Metrics like Quantum Circuit Expressibility (QCE), Quantum Circuit Fidelity (QCF), Quantum Locality Ratio (QLR), Effective Entanglement Entropy (EEE), and Quantum Mutual Information (QMI) assess a circuit's representational capacity, robustness to noise, balance of gate operations, and internal correlations. * Quantum feature space: This category includes Feature Map Compression Ratio (FMCR), Effective Dimension of Quantum Feature Space (EDQFS), Quantum Layer Activation Diversity (QLAD), and Quantum Output Sensitivity (QOS), which analyze how classical data is encoded into quantum states, the geometry of the resulting feature space, and its robustness to perturbations. * Training dynamics: Metrics such as Training Stability Index (TSI), Training Efficiency Index (TEI), Quantum Gradient Norm (QGN), and Barren Plateau Indicator (BPI), along with relative metrics like Relative Quantum Layer Stability Index (RQLSI) and Relative Quantum Training Efficiency Index (r-QTEI), provide insights into convergence behavior, parameter efficiency, and gradient issues. This study illustrates how QMetric (or this suggested set of metrics) can help researchers diagnose bottlenecks, compare architectures, and validate empirical claims beyond raw accuracy, guiding more informed model design in quantum machine learning. Here the article: https://lnkd.in/dnZdufdu Here the repo: https://lnkd.in/dpE3sB2W #qml #quantum #machinelearning #ml #quantumcomputing #datascience

We posted on the arXiv an introduction to our stochastic optimization benchmarking package for quantum and quantum-inspired solvers: https://lnkd.in/g_XfC8mf - we take an holistic look at what a solver is end-to-end and how it should be evaluated for actual impact. One aspect often swept under the rug in recent technical papers showing new quantum or analog solvers is the cost of parameter setting. This is super-misleading because you can always trade off the performance of the solver with tuning magically a large number of parameters! So we created an open-source package that forces you not to cheat: https://lnkd.in/gD4zMDJV - You need to specify how to set the parameters "on the fly" and evaluate the performance on unseen instances, like people do in ML (but surprisingly not much in quantum optimization..) The result is the ability to quantify the real-world performance of a new method - making sure that it is statistically sound - and declare which are the practical best strategies to run a given method on a given problem class with given resources. We informally call these honest visualizations of performance "windows stickers" for solution methods in analogy to car performance specs. Below a sticker of coherent Ising machines CIM-CAC vs parallel tempering PySA, using different param setting strategies on planted-solution hard problems, as a function of allowed time. See that hyperopt+CIM is the best solution method if you have enough seconds. More on paper / github: How to visualize the parameter setting strategies, automatically compute bounds etc. We hope you like it and want to use it and expand it - it's a tool to design practical new solutions. Thanks collaborators, interns at NASA, Stanford, UCLA, Purdue!

#Benchmarking #quantum #machinelearning (QML) models against their classical counterparts is a nuanced and complex process, necessitated by the fundamental differences in the underlying technology and the types of problems each is best suited to solve. The critical examination of benchmarking in QML is the subject of the latest paper of Maria Schuld's Team at Xanadu. It is proving to be a sophisticated and challenging endeavor, emphasizing the necessity for experienced researchers and rigorous scientific methodology. Benchmarking QML is complex due to the intricacies of extracting meaningful results from machine learning models trained on data. Non-robust claims can significantly influence the direction of research within the community. The importance of methodological rigor in study design and the detailed reporting of choices made during the benchmarking process cannot be overstated, as these elements are crucial for minimizing potential biases in the results. A paramount question in benchmarking QML models concerns the selection of data, which is vital for creating meaningful benchmarks. The focus should be on understanding the structure of data relevant to real-world applications and how these can be connected to the mathematical properties of quantum models. This challenge is not unique to quantum computing but is further complicated by the unclear areas where quantum computing may enhance learning capabilities. The dilemma of choosing the right data and identifying quantum models with potential advantages presents a significant "chicken and egg" problem that requires addressing from both theoretical and practical perspectives. Furthermore, benchmarking QML models is also demanding due to the limitations of current quantum software, particularly with the resource-intensive nature of hyperparameter optimization and the complexity of hybrid quantum-classical system pipelines. The scalability of results to larger datasets and the consequent demand for increased qubits present additional hurdles, highlighting the need for scalability studies. Instead of focusing solely on rankings, benchmarks should facilitate qualitative insights into the essential components of a model's design and their interchangeability. Investigating the quantum advantage by experimentally removing "quantumness" (for instance with #TensorNetwork structures) from models and comparing the outcomes with those of modified classical models can provide valuable insights into the potential of variational quantum circuits and other quantum computing concepts.