Variational Quantum Pulse Learning

Abstract

Quantum computing is among the most promising emerging techniques to solve problems that are computationally intractable on classical hardware. A large body of existing works focus on using variational quantum algorithms on the gate level for machine learning tasks, such as the variational quantum circuit (VQC). However, VQC has limited flexibility and expressibility due to limited number of parameters, e.g. only one parameter can be trained in one rotation gate. On the other hand, we observe that quantum pulses are lower than quantum gates in the stack of quantum computing and offers more control parameters. Inspired by the promising performance of VQC, in this paper we propose variational quantum pulses (VQP), a novel paradigm to directly train quantum pulses for learning tasks. The proposed method manipulates variational quantum pulses by pulling and pushing the amplitudes of pulses in an optimization framework. Similar to variational quantum algorithms, our framework to train pulses maintains the robustness to noise on Noisy Intermediate-Scale Quantum (NISQ) computers. In an example task of binary classification, VQP learning achieves 30% and 40% higher accuracy compared with VQC learning on the qiskit noise simulatosr (FakeMachines) and ibmq-jarkata, respectively, demonstrating its effectiveness and feasibility. Stability for VQP to obtain reliable results has also been verified in the presence of noise.

Analyzing Strategies for Dynamical Decoupling Insertion on IBM Quantum Computer

“…It is interesting to analyze other more complex DD techniques with extra phase shift such as XY 4. These techniques can further contribute to the pulse-level circuit optimization [12], [21].…”

PAN: Pulse Ansatz on NISQ Machines

“…Since pulse learning adjust d i (t) and u i (t), D i (t) and U i (t) are changed accordingly. Consequently, the drive Hamiltonian is also updated [49]. Thus, we are able to govern the quantum system using control signals.…”

“…And the results demonstrate that the coherence time required for state preparation is greatly reduced. VQP [49] uses pulses as basic components to build the QNN ansatz and exhibits latency advantages over gate-based QNN on a two-class image classification task. The experiments are mostly on Qiskit [52] pulse simulator.…”

Quantum optimal control in quantum technologies. Strategic report on current status, visions and goals for research in Europe

“…The corresponding gate sequences can be subjected to optimization, for example to minimize the depth of the circuit on noisy quantum processors [329] or to reduce the approximation error [405]. Use of variational quantum algorithms, instead of more traditional optimization tools, in order to learn pulse parameters of a quantum circuit, has recently been termed variational quantum pulse learning [379]. In order to scale to large circuits, a block-by-block optimization framework has been suggested [623].…”