Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Digital-Analog Quantum Machine Learning

Lucas Lamata

TL;DR

The paper surveys digital-analog quantum machine learning (DAQML), a hybrid approach that mixes large analog quantum blocks with digital gates to address ML tasks on near-term devices. It argues that fault-tolerant quantum computers are not yet available, but DAQML could yield practical advantages by exploiting NISQ hardware across VQE, QAOA, kernels, and other primitives. Through a selective review of recent works in molecular simulations, quantum genetics, kernel methods, and quantum transforms, the author highlights potential scalability and resource efficiencies. The outlook suggests that as hardware matures, DAQML could accelerate quantum-assisted data analysis and drive early industrial relevance.

Abstract

Machine Learning algorithms are extensively used in an increasing number of systems, applications, technologies, and products, both in industry and in society as a whole. They enable computing devices to learn from previous experience and therefore improve their performance in a certain context or environment. In this way, many useful possibilities have been made accessible. However, dealing with an increasing amount of data poses difficulties for classical devices. Quantum systems may offer a way forward, possibly enabling to scale up machine learning calculations in certain contexts. On the other hand, quantum systems themselves are also hard to scale up, due to decoherence and the fragility of quantum superpositions. In the short and mid term, it has been evidenced that a quantum paradigm that combines evolution under large analog blocks with discrete quantum gates, may be fruitful to achieve new knowledge of classical and quantum systems with no need of having a fault-tolerant quantum computer. In this Perspective, we review some recent works that employ this digital-analog quantum paradigm to carry out efficient machine learning calculations with current quantum devices.

Digital-Analog Quantum Machine Learning

TL;DR

The paper surveys digital-analog quantum machine learning (DAQML), a hybrid approach that mixes large analog quantum blocks with digital gates to address ML tasks on near-term devices. It argues that fault-tolerant quantum computers are not yet available, but DAQML could yield practical advantages by exploiting NISQ hardware across VQE, QAOA, kernels, and other primitives. Through a selective review of recent works in molecular simulations, quantum genetics, kernel methods, and quantum transforms, the author highlights potential scalability and resource efficiencies. The outlook suggests that as hardware matures, DAQML could accelerate quantum-assisted data analysis and drive early industrial relevance.

Abstract

Machine Learning algorithms are extensively used in an increasing number of systems, applications, technologies, and products, both in industry and in society as a whole. They enable computing devices to learn from previous experience and therefore improve their performance in a certain context or environment. In this way, many useful possibilities have been made accessible. However, dealing with an increasing amount of data poses difficulties for classical devices. Quantum systems may offer a way forward, possibly enabling to scale up machine learning calculations in certain contexts. On the other hand, quantum systems themselves are also hard to scale up, due to decoherence and the fragility of quantum superpositions. In the short and mid term, it has been evidenced that a quantum paradigm that combines evolution under large analog blocks with discrete quantum gates, may be fruitful to achieve new knowledge of classical and quantum systems with no need of having a fault-tolerant quantum computer. In this Perspective, we review some recent works that employ this digital-analog quantum paradigm to carry out efficient machine learning calculations with current quantum devices.

Paper Structure

This paper contains 5 sections, 1 figure.

Table of Contents

  1. Quantum Machine Learning
  2. Digital-Analog Quantum Paradigm
  3. Digital-Analog Quantum Machine Learning
  4. Implementations of the Digital-Analog Quantum Paradigm
  5. Outlook

Figures (1)

  • Figure 1: scheme of a generic extreme digital-analog quantum protocol (EDAQP). This consists of a combination of the largest analog blocks possible, i.e., a set of global native interactions, namely, global unitary gates on all qubits based on the quantum platform native Hamiltonian, $U_1(t_1)$, $U_2(t_2)$,...,$U_k(t_k)$,..., and the smallest digital gates possible, i.e., single-qubit gates, which can be done with the best fidelities in most quantum platforms.