Microwave signal processing using an analog quantum reservoir computer

• URL: http://arxiv.org/abs/2312.16166v2
• Date: Fri, 6 Sep 2024 02:10:09 GMT
• Title: Microwave signal processing using an analog quantum reservoir computer
• Authors: Alen Senanian, Sridhar Prabhu, Vladimir Kremenetski, Saswata Roy, Yingkang Cao, Jeremy Kline, Tatsuhiro Onodera, Logan G. Wright, Xiaodi Wu, Valla Fatemi, Peter L. McMahon,
• Abstract summary: We show how a superconducting circuit can be used as an analog quantum reservoir for a variety of classification tasks. Our work does not attempt to address the question of whether QRCs could provide a quantum computational advantage.
• Score: 5.236242306967409
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantum reservoir computing (QRC) has been proposed as a paradigm for performing machine learning with quantum processors where the training is efficient in the number of required runs of the quantum processor and takes place in the classical domain, avoiding the issue of barren plateaus in parameterized-circuit quantum neural networks. It is natural to consider using a quantum processor based on superconducting circuits to classify microwave signals that are analog -- continuous in time. However, while theoretical proposals of analog QRC exist, to date QRC has been implemented using circuit-model quantum systems -- imposing a discretization of the incoming signal in time, with each time point input by executing a gate operation. In this paper we show how a quantum superconducting circuit comprising an oscillator coupled to a qubit can be used as an analog quantum reservoir for a variety of classification tasks, achieving high accuracy on all of them. Our quantum system was operated without artificially discretizing the input data, directly taking in microwave signals. Our work does not attempt to address the question of whether QRCs could provide a quantum computational advantage in classifying pre-recorded classical signals. However, beyond illustrating that sophisticated tasks can be performed with a modest-size quantum system and inexpensive training, our work opens up the possibility of achieving a different kind of advantage than a purely computational advantage: superconducting circuits can act as extremely sensitive detectors of microwave photons; our work demonstrates processing of ultra-low-power microwave signals in our superconducting circuit, and by combining sensitive detection with QRC processing within the same system, one could achieve a quantum sensing-computational advantage, i.e., an advantage in the overall analysis of microwave signals comprising just a few photons.

Related papers

• Efficient Learning for Linear Properties of Bounded-Gate Quantum Circuits [63.733312560668274]
  Given a quantum circuit containing d tunable RZ gates and G-d Clifford gates, can a learner perform purely classical inference to efficiently predict its linear properties? We prove that the sample complexity scaling linearly in d is necessary and sufficient to achieve a small prediction error, while the corresponding computational complexity may scale exponentially in d. We devise a kernel-based learning model capable of trading off prediction error and computational complexity, transitioning from exponential to scaling in many practical settings.
  arXiv  Detail & Related papers  (2024-08-22T08:21:28Z)
• 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)
• Quantum Parametric Amplification and NonClassical Correlations due to 45 nm nMOS Circuitry Effect [2.0481796917798407]
  This study unveils a groundbreaking exploration of using semiconductor technology in quantum circuitry. The novel quantum device serves not only as a quantum parametric amplifier to amplify quantum signals but also enhances the inherent quantum properties of the signals.
  arXiv  Detail & Related papers  (2023-10-25T05:55:50Z)
• Quantum circuit debugging and sensitivity analysis via local inversions [62.997667081978825]
  We present a technique that pinpoints the sections of a quantum circuit that affect the circuit output the most. We demonstrate the practicality and efficacy of the proposed technique by applying it to example algorithmic circuits implemented on IBM quantum machines.
  arXiv  Detail & Related papers  (2022-04-12T19:39:31Z)
• Recompilation-enhanced simulation of electron-phonon dynamics on IBM Quantum computers [62.997667081978825]
  We consider the absolute resource cost for gate-based quantum simulation of small electron-phonon systems. We perform experiments on IBM quantum hardware for both weak and strong electron-phonon coupling. Despite significant device noise, through the use of approximate circuit recompilation we obtain electron-phonon dynamics on current quantum computers comparable to exact diagonalisation.
  arXiv  Detail & Related papers  (2022-02-16T19:00:00Z)
• Dispersive qubit readout with machine learning [0.08399688944263842]
  Open quantum systems can undergo dissipative phase transitions, and their critical behavior can be exploited in sensing applications. A recently introduced measurement protocol uses the parametric (two-photon driven) Kerr resonator's driven-dissipative phase transition to reach single-qubit detection fidelity of 99.9%. We use machine learning-based classification algorithms to extract information from this critical dynamics.
  arXiv  Detail & Related papers  (2021-12-10T04:25:43Z)
• Parity measurement in the strong dispersive regime of circuit quantum acoustodynamics [1.7673364730995766]
  We show direct measurements of the phonon number distribution and parity of nonclassical mechanical states. These measurements are some of the basic building blocks for constructing acoustic quantum memories and processors. Our results open the door to performing even more complex quantum algorithms using mechanical systems.
  arXiv  Detail & Related papers  (2021-10-01T08:40:26Z)
• Exploiting dynamic quantum circuits in a quantum algorithm with superconducting qubits [0.207811670193148]
  We build dynamic quantum circuits on a superconducting-based quantum system. We exploit one of the most fundamental quantum algorithms, quantum phase estimation, in its adaptive version. We demonstrate that the version of real-time quantum computing with dynamic circuits can offer a substantial and tangible advantage.
  arXiv  Detail & Related papers  (2021-02-02T18:51:23Z)
• Information Scrambling in Computationally Complex Quantum Circuits [56.22772134614514]
  We experimentally investigate the dynamics of quantum scrambling on a 53-qubit quantum processor. We show that while operator spreading is captured by an efficient classical model, operator entanglement requires exponentially scaled computational resources to simulate.
  arXiv  Detail & Related papers  (2021-01-21T22:18:49Z)
• Circuit Quantum Electrodynamics [62.997667081978825]
  Quantum mechanical effects at the macroscopic level were first explored in Josephson junction-based superconducting circuits in the 1980s. In the last twenty years, the emergence of quantum information science has intensified research toward using these circuits as qubits in quantum information processors. The field of circuit quantum electrodynamics (QED) has now become an independent and thriving field of research in its own right.
  arXiv  Detail & Related papers  (2020-05-26T12:47:38Z)
• Expressibility and trainability of parameterized analog quantum systems for machine learning applications [0.0]
  We show how interplay between external driving and disorder in the system dictates the trainability and expressibility of interacting quantum systems. Our work shows the fundamental connection between quantum many-body physics and its application in machine learning.
  arXiv  Detail & Related papers  (2020-05-22T14:59:42Z)

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.