Parameterized quantum comb and simpler circuits for reversing unknown qubit-unitary operations

IF 6.6 1区 物理与天体物理 Q1 PHYSICS, APPLIED
Yin Mo, Lei Zhang, Yu-Ao Chen, Yingjian Liu, Tengxiang Lin, Xin Wang
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Abstract

Quantum combs play a vital role in characterizing and transforming quantum processes, with wide-ranging applications in quantum information processing. However, obtaining the explicit quantum circuit for the desired quantum comb remains a challenging problem. We propose PQComb, a novel framework that employs parameterized quantum circuits (PQCs) or quantum neural networks to harness the full potential of quantum combs for diverse quantum process transformation tasks. This method is well-suited for near-term quantum devices and can be applied to various tasks in quantum machine learning. As a notable application, we present two streamlined protocols for the time-reversal simulation of unknown qubit unitary evolutions, reducing the ancilla qubit overhead from six to three compared to the previous best-known method. We also extend PQComb to solve the problems of qutrit unitary transformation and channel discrimination. Furthermore, we demonstrate the hardware efficiency and robustness of our qubit unitary inversion protocol under realistic noise simulations of IBM-Q superconducting quantum hardware, yielding a significant improvement in average similarity over the previous protocol under practical regimes. PQComb’s versatility and potential for broader applications in quantum machine learning pave the way for more efficient and practical solutions to complex quantum tasks.

Abstract Image

参数化量子梳和逆转未知量子位酉运算的更简单电路
量子梳在表征和转换量子过程中起着至关重要的作用,在量子信息处理中有着广泛的应用。然而,获得理想量子梳的显式量子电路仍然是一个具有挑战性的问题。我们提出了PQComb,一个采用参数化量子电路(pqc)或量子神经网络的新框架,以利用量子梳子的全部潜力来完成各种量子过程转换任务。这种方法非常适合于近期量子器件,可以应用于量子机器学习中的各种任务。作为一个值得注意的应用,我们提出了两个简化的协议,用于未知量子位一元演化的时间反转模拟,与之前最著名的方法相比,将辅助量子位开销从6个减少到3个。我们还对PQComb进行了扩展,解决了quit酉变换和信道识别问题。此外,我们在IBM-Q超导量子硬件的真实噪声模拟下证明了我们的量子比特单一反转协议的硬件效率和鲁棒性,在实际情况下,与以前的协议相比,平均相似度有了显着提高。PQComb的多功能性和在量子机器学习中更广泛应用的潜力为复杂量子任务的更有效和实用的解决方案铺平了道路。
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来源期刊
npj Quantum Information
npj Quantum Information Computer Science-Computer Science (miscellaneous)
CiteScore
13.70
自引率
3.90%
发文量
130
审稿时长
29 weeks
期刊介绍: The scope of npj Quantum Information spans across all relevant disciplines, fields, approaches and levels and so considers outstanding work ranging from fundamental research to applications and technologies.
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