Assisted quantum simulation of open quantum systems
- URL: http://arxiv.org/abs/2302.13299v2
- Date: Sun, 16 Apr 2023 11:47:33 GMT
- Title: Assisted quantum simulation of open quantum systems
- Authors: Jin-Min Liang, Qiao-Qiao Lv, Zhi-Xi Wang, Shao-Ming Fei
- Abstract summary: 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.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Universal quantum algorithms (UQA) implemented on fault-tolerant quantum
computers are expected to achieve an exponential speedup over classical
counterparts. However, the deep quantum circuits makes the UQA implausible in
the current era. With only the noisy intermediate-scale quantum (NISQ) devices
in hand, we introduce the quantum-assisted quantum algorithm, which reduces the
circuit depth of UQA via NISQ technology. Based on this framework, we present
two quantum-assisted quantum algorithms for simulating open quantum systems,
which utilize two parameterized quantum circuits to achieve a short-time
evolution. We propose a variational quantum state preparation method, as a
subroutine to prepare the ancillary state, for loading a classical vector into
a quantum state with a shallow quantum circuit and logarithmic number of
qubits. We demonstrate numerically our approaches for a two-level system with
an amplitude damping channel and an open version of the dissipative transverse
field Ising model on two sites.
Related papers
- A Quantum-Classical Collaborative Training Architecture Based on Quantum
State Fidelity [50.387179833629254]
We introduce a collaborative classical-quantum architecture called co-TenQu.
Co-TenQu enhances a classical deep neural network by up to 41.72% in a fair setting.
It outperforms other quantum-based methods by up to 1.9 times and achieves similar accuracy while utilizing 70.59% fewer qubits.
arXiv Detail & Related papers (2024-02-23T14:09:41Z) - 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) - Quantum Machine Learning: from physics to software engineering [58.720142291102135]
We show how classical machine learning approach can help improve the facilities of quantum computers.
We discuss how quantum algorithms and quantum computers may be useful for solving classical machine learning tasks.
arXiv Detail & Related papers (2023-01-04T23:37:45Z) - Quantum Error Mitigation via Quantum-Noise-Effect Circuit Groups [0.0]
Near-term quantum computers are fragile against quantum noise effects.
Traditional quantum-error-correcting codes are not implemented on such devices.
We propose quantum error mitigation (QEM) scheme for quantum computational errors.
arXiv Detail & Related papers (2022-05-27T11:21:35Z) - Efficient criteria of quantumness for a large system of qubits [58.720142291102135]
We discuss the dimensionless combinations of basic parameters of large, partially quantum coherent systems.
Based on analytical and numerical calculations, we suggest one such number for a system of qubits undergoing adiabatic evolution.
arXiv Detail & Related papers (2021-08-30T23:50:05Z) - Natural parameterized quantum circuit [0.0]
We introduce the natural parameterized quantum circuit (NPQC) that can be initialised with a Euclidean quantum geometry.
For a general class of quantum circuits, the NPQC has the minimal quantum Cram'er-Rao bound.
Our results can be used to enhance currently available quantum processors.
arXiv Detail & Related papers (2021-07-29T14:54:04Z) - Variational quantum compiling with double Q-learning [0.37798600249187286]
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.
arXiv Detail & Related papers (2021-03-22T06:46:35Z) - Quantum walk processes in quantum devices [55.41644538483948]
We study how to represent quantum walk on a graph as a quantum circuit.
Our approach paves way for the efficient implementation of quantum walks algorithms on quantum computers.
arXiv Detail & Related papers (2020-12-28T18:04:16Z)
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.