Shot-frugal and Robust quantum kernel classifiers

• URL: http://arxiv.org/abs/2210.06971v3
• Date: Sun, 31 Dec 2023 18:33:24 GMT
• Title: Shot-frugal and Robust quantum kernel classifiers
• Authors: Abhay Shastry, Abhijith Jayakumar, Apoorva Patel, Chiranjib Bhattacharyya
• Abstract summary: Quantum kernel methods are a candidate for quantum speed-ups in machine learning. We show that for classification tasks, the aim is reliable classification and not precise kernel evaluation. We motivate a new metric that characterizes the reliability of classification.
• Score: 12.146571029233435
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantum kernel methods are a candidate for quantum speed-ups in supervised machine learning. The number of quantum measurements N required for a reasonable kernel estimate is a critical resource, both from complexity considerations and because of the constraints of near-term quantum hardware. We emphasize that for classification tasks, the aim is reliable classification and not precise kernel evaluation, and demonstrate that the former is far more resource efficient. Furthermore, it is shown that the accuracy of classification is not a suitable performance metric in the presence of noise and we motivate a new metric that characterizes the reliability of classification. We then obtain a bound for N which ensures, with high probability, that classification errors over a dataset are bounded by the margin errors of an idealized quantum kernel classifier. Using chance constraint programming and the subgaussian bounds of quantum kernel distributions, we derive several Shot-frugal and Robust (ShofaR) programs starting from the primal formulation of the Support Vector Machine. This significantly reduces the number of quantum measurements needed and is robust to noise by construction. Our strategy is applicable to uncertainty in quantum kernels arising from any source of unbiased noise.

Related papers

• In Search of Quantum Advantage: Estimating the Number of Shots in Quantum Kernel Methods [30.565491081930997]
  We develop an approach for estimating desired precision of kernel values, which is translated into the number of circuit runs. We stress that quantum kernel methods should not only be considered from the machine learning performance perspective, but also from the context of the resource consumption.
  arXiv  Detail & Related papers  (2024-07-22T16:29:35Z)
• Power Characterization of Noisy Quantum Kernels [52.47151453259434]
  We show that noise may make quantum kernel methods to only have poor prediction capability, even when the generalization error is small. We provide a crucial warning to employ noisy quantum kernel methods for quantum computation.
  arXiv  Detail & Related papers  (2024-01-31T01:02:16Z)
• Quantum Conformal Prediction for Reliable Uncertainty Quantification in Quantum Machine Learning [47.991114317813555]
  Quantum models implement implicit probabilistic predictors that produce multiple random decisions for each input through measurement shots. This paper proposes to leverage such randomness to define prediction sets for both classification and regression that provably capture the uncertainty of the model.
  arXiv  Detail & Related papers  (2023-04-06T22:05:21Z)
• Noisy Quantum Kernel Machines [58.09028887465797]
  An emerging class of quantum learning machines is that based on the paradigm of quantum kernels. We study how dissipation and decoherence affect their performance. We show that decoherence and dissipation can be seen as an implicit regularization for the quantum kernel machines.
  arXiv  Detail & Related papers  (2022-04-26T09:52:02Z)
• Improved Quantum Algorithms for Fidelity Estimation [77.34726150561087]
  We develop new and efficient quantum algorithms for fidelity estimation with provable performance guarantees. Our algorithms use advanced quantum linear algebra techniques, such as the quantum singular value transformation. We prove that fidelity estimation to any non-trivial constant additive accuracy is hard in general.
  arXiv  Detail & Related papers  (2022-03-30T02:02:16Z)
• Training Quantum Embedding Kernels on Near-Term Quantum Computers [0.08563354084119063]
  Quantum embedding kernels (QEKs) constructed by embedding data into the Hilbert space of a quantum computer are a particular quantum kernel technique. We first provide an accessible introduction to quantum embedding kernels and then analyze the practical issues arising when realizing them on a noisy near-term quantum computer.
  arXiv  Detail & Related papers  (2021-05-05T18:41:13Z)
• Robust quantum classifier with minimal overhead [0.8057006406834467]
  Several quantum algorithms for binary classification based on the kernel method have been proposed. These algorithms rely on estimating an expectation value, which in turn requires an expensive quantum data encoding procedure to be repeated many times. We show that the kernel-based binary classification can be performed with a single-qubit measurement regardless of the number and the dimension of the data.
  arXiv  Detail & Related papers  (2021-04-16T14:51:00Z)
• Towards understanding the power of quantum kernels in the NISQ era [79.8341515283403]
  We show that the advantage of quantum kernels is vanished for large size datasets, few number of measurements, and large system noise. Our work provides theoretical guidance of exploring advanced quantum kernels to attain quantum advantages on NISQ devices.
  arXiv  Detail & Related papers  (2021-03-31T02:41:36Z)
• Quantum noise protects quantum classifiers against adversaries [120.08771960032033]
  Noise in quantum information processing is often viewed as a disruptive and difficult-to-avoid feature, especially in near-term quantum technologies. We show that by taking advantage of depolarisation noise in quantum circuits for classification, a robustness bound against adversaries can be derived. This is the first quantum protocol that can be used against the most general adversaries.
  arXiv  Detail & Related papers  (2020-03-20T17:56:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.