Variational quantum compiling with double Q-learning
- URL: http://arxiv.org/abs/2103.11611v1
- Date: Mon, 22 Mar 2021 06:46:35 GMT
- Title: Variational quantum compiling with double Q-learning
- Authors: Zhimin He, Lvzhou Li, Shenggen Zheng, Yongyao Li, Haozhen Situ
- Abstract summary: We propose a variational quantum compiling (VQC) algorithm based on reinforcement learning (RL)
An agent is trained to sequentially select quantum gates from the native gate alphabet and the qubits they act on by double Q-learning.
It can reduce the errors of quantum algorithms due to decoherence process and gate noise in NISQ devices.
- Score: 0.37798600249187286
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Quantum compiling aims to construct a quantum circuit V by quantum gates
drawn from a native gate alphabet, which is functionally equivalent to the
target unitary U. It is a crucial stage for the running of quantum algorithms
on noisy intermediate-scale quantum (NISQ) devices. However, the space for
structure exploration of quantum circuit is enormous, resulting in the
requirement of human expertise, hundreds of experimentations or modifications
from existing quantum circuits. In this paper, we propose a variational quantum
compiling (VQC) algorithm based on reinforcement learning (RL), in order to
automatically design the structure of quantum circuit for VQC with no human
intervention. An agent is trained to sequentially select quantum gates from the
native gate alphabet and the qubits they act on by double Q-learning with
\epsilon-greedy exploration strategy and experience replay. At first, the agent
randomly explores a number of quantum circuits with different structures, and
then iteratively discovers structures with higher performance on the learning
task. Simulation results show that the proposed method can make exact
compilations with less quantum gates compared to previous VQC algorithms. It
can reduce the errors of quantum algorithms due to decoherence process and gate
noise in NISQ devices, and enable quantum algorithms especially for complex
algorithms to be executed within coherence time.
Related papers
- YAQQ: Yet Another Quantum Quantizer -- Design Space Exploration of Quantum Gate Sets using Novelty Search [0.9932551365711049]
We present a software tool for comparative analysis of quantum processing units and control protocols based on their native gates.
The developed software, YAQQ (Yet Another Quantum Quantizer), enables the discovery of an optimized set of quantum gates.
arXiv Detail & Related papers (2024-06-25T14:55:35Z) - Quantum Compiling with Reinforcement Learning on a Superconducting Processor [55.135709564322624]
We develop a reinforcement learning-based quantum compiler for a superconducting processor.
We demonstrate its capability of discovering novel and hardware-amenable circuits with short lengths.
Our study exemplifies the codesign of the software with hardware for efficient quantum compilation.
arXiv Detail & Related papers (2024-06-18T01:49:48Z) - Distributed quantum architecture search [0.0]
Variational quantum algorithms, inspired by neural networks, have become a novel approach in quantum computing.
Quantum architecture search tackles this by adjusting circuit structures along with gate parameters to automatically discover high-performance circuit structures.
We propose an end-to-end distributed quantum architecture search framework, where we aim to automatically design distributed quantum circuit structures for interconnected quantum processing units with specific qubit connectivity.
arXiv Detail & Related papers (2024-03-10T13:28:56Z) - Quantum Subroutine for Variance Estimation: Algorithmic Design and Applications [80.04533958880862]
Quantum computing sets the foundation for new ways of designing algorithms.
New challenges arise concerning which field quantum speedup can be achieved.
Looking for the design of quantum subroutines that are more efficient than their classical counterpart poses solid pillars to new powerful quantum algorithms.
arXiv Detail & Related papers (2024-02-26T09:32:07Z) - QuantumSEA: In-Time Sparse Exploration for Noise Adaptive Quantum
Circuits [82.50620782471485]
QuantumSEA is an in-time sparse exploration for noise-adaptive quantum circuits.
It aims to achieve two key objectives: (1) implicit circuits capacity during training and (2) noise robustness.
Our method establishes state-of-the-art results with only half the number of quantum gates and 2x time saving of circuit executions.
arXiv Detail & Related papers (2024-01-10T22:33:00Z) - Quantum Imitation Learning [74.15588381240795]
We propose quantum imitation learning (QIL) with a hope to utilize quantum advantage to speed up IL.
We develop two QIL algorithms, quantum behavioural cloning (Q-BC) and quantum generative adversarial imitation learning (Q-GAIL)
Experiment results demonstrate that both Q-BC and Q-GAIL can achieve comparable performance compared to classical counterparts.
arXiv Detail & Related papers (2023-04-04T12:47:35Z) - Assisted quantum simulation of open quantum systems [0.0]
We introduce the quantum-assisted quantum algorithm, which reduces the circuit depth of UQA via NISQ technology.
We present two quantum-assisted quantum algorithms for simulating open quantum systems.
arXiv Detail & Related papers (2023-02-26T11:41:02Z) - Variational Quantum Circuits for Multi-Qubit Gate Automata [0.6445605125467573]
Variational quantum algorithms (VQAs) may have the capacity to provide a quantum advantage in the Noisy Intermediate-scale Quantum (NISQ) era.
We present a quantum machine learning framework, inspired by VQAs, to tackle the problem of finding time-independent Hamiltonians that generate desired unitary evolutions.
arXiv Detail & Related papers (2022-08-31T22:05:17Z) - Quantum circuit architecture search for variational quantum algorithms [88.71725630554758]
We propose a resource and runtime efficient scheme termed quantum architecture search (QAS)
QAS automatically seeks a near-optimal ansatz to balance benefits and side-effects brought by adding more noisy quantum gates.
We implement QAS on both the numerical simulator and real quantum hardware, via the IBM cloud, to accomplish data classification and quantum chemistry tasks.
arXiv Detail & Related papers (2020-10-20T12:06:27Z) - QUANTIFY: A framework for resource analysis and design verification of
quantum circuits [69.43216268165402]
QUANTIFY is an open-source framework for the quantitative analysis of quantum circuits.
It is based on Google Cirq and is developed with Clifford+T circuits in mind.
For benchmarking purposes QUANTIFY includes quantum memory and quantum arithmetic circuits.
arXiv Detail & Related papers (2020-07-21T15:36:25Z)
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.