We propose a novel, to the best of our knowledge, approach to realizing instantaneous frequency measurement with ultrahigh measurement bandwidth, which utilizes three-channel photonic compressive sensing (CS) with sub-Nyquist pseudo-random binary sequences (PRBSs). In each CS channel, an alias frequency is recovered due to the sub-Nyquist property of the applied PRBS. A frequency identification algorithm is employed to determine the frequency of the signal under measurement according to the three alias frequencies. The proposed approach significantly reduces the bit rate of the applied PRBSs and the sampling rate required by the digitizers in CS. A proof-of-concept experiment for measuring frequency in the Ku band is demonstrated using PRBSs at 1 Gb/s and digitizers with a sampling rate of 250 MS/s.
We propose a novel, to the best of our knowledge, approach to realizing instantaneous frequency measurement with ultrahigh measurement bandwidth, which utilizes three-channel photonic compressive sensing (CS) with sub-Nyquist pseudo-random binary sequences (PRBSs). In each CS channel, an alias frequency is recovered due to the sub-Nyquist property of the applied PRBS. A frequency identification algorithm is employed to determine the frequency of the signal under measurement according to the three alias frequencies. The proposed approach significantly reduces the bit rate of the applied PRBSs and the sampling rate required by the digitizers in CS. A proof-of-concept experiment for measuring frequency in the Ku band is demonstrated using PRBSs at 1 Gb/s and digitizers with a sampling rate of 250 MS/s.
Wideband frequency-hopping measurement is crucial for the stable operation of frequency-hopping communication systems. Traditional digital signal processing methods based on the Nyquist-Shannon sampling theorem are limited by bandwidth. Although compressed sensing reduces the sampling rate for analog-to-digital converters, its reliance on high-speed pseudo-random sequences restricts practical applications. In this paper, we propose a wideband frequency-hopping measurement method based on quantum compressed sensing, which enables dynamic capture of wideband frequency-hopping signals. By utilizing coherent state as quantum resources and mapping frequency-hopping signal onto photonic wave functions via an electro-optical modulator, a compressive measurement system is constructed that leverages the inherent randomness of coherent photon measurement collapse, and successfully achieved measurement and reconstruction of a frequency-hopping signal with 5 GHz bandwidth and a hopping rate of 100 kHop/s. A high data compression ratio of 2.1 × 10 3 was attained at a frequency-hopping rate of 1 kHop/s. By integrating time-frequency analysis methods with Bayesian optimization algorithm for frequency-hopping parameter estimation, a time estimation accuracy of 1 μs is achieved. Our research offers what we believe to be a new solution for wideband frequency-hopping measurement and can be extended to various fields such as radar and cognitive radio.
Real-time sensing of ultra-wideband radio-frequency signal with high frequency resolution is challenging, which is confined by the sampling rate of electronic analog-to-digital converter and the capability of digital signal processing. By combining quantum mechanics with compressed sensing, quantum compressed sensing is proposed for wideband radio-frequency signal frequency measurement. By using an electro-optical crystal as a sensor which modulates the wave function of the coherent photons with the signal to be measured. The frequency spectrum could be recovered by detecting the modulated sparse photons with a low time-jitter single-photon detector and a time-to-digital converter. More than 50 GHz real-time analysis bandwidth is demonstrated with the Fourier transform limit resolution. The further simulation shows it can be extended to more than 300 GHz with the present technologies.
With the rapid development of radio frequency technology such as radar, electronic warfare and 5G communication, the measurement and real-time spectrum characterization of broadband radio frequency signals become increasingly important. The traditional radio frequency signal real-time measurement technology is limited by the sampling rate of analog-to-digital converter and the ability to process digital signals, and encounters the problems of narrow measurement band, large data volume, and susceptibility to electromagnetic interference. This work is to study a radio frequency signal measurement technology based on quantum compression sensing, which uses integrated electro-optical crystal as radio frequency sensor, and constructs a compression sensing machine by modulating the photon wave function of the measured radio frequency signal to realize the compression measurement of broadband radio frequency signal, significantly improving the spectrum sensing bandwidth. The experiment demonstrates the long-term spectrum monitoring of power frequency and intermediate frequency high voltage signals, and the real-time spectrum measurement of high frequency radio frequency signals. Under the Fourier limit spectrum resolution, the real-time spectrum analysis bandwidth of GHz magnitude is realized, and the data compression rate reaches 1.7×10–5, which can meet the needs of 5G wireless communication, cognitive radio and other applications for broadband radio frequency signal spectrum measurement, and provide a new technical path for developing the next-generation broadband spectrum sensing technology.
Free induction decay (FID) of spin precession serves as an essential tool for quantum sensing across diverse platforms. While extending spin coherence time remains critical for sensitivity enhancement, the requisite long single-shot acquisitions narrow the resolvable frequency range, establishing a fundamental ``spin coherence limit (SCL)'', according to the Nyquist Sampling Theorem. Besides, conventional spectral analysis for FID measurement suffers from frequency alias, causing signal attenuation and positional errors that compromise the measurement validity. Here, we demonstrate a general frequency-range-extended technique that overcomes SCL by leveraging compressive sensing. By applying this method to the FID magnetometer, we expand the resolvable frequency range significantly from the Nyquist-limited range of 251\,Hz to 3000\,Hz, effectively avoiding frequency alias. Our work paves the way for implementing long-coherence-time spin systems in high-sensitivity, broad-bandwidth, and alias-free magnetic field sensing.
Rydberg-atomic receivers exhibit exceptional sensitivity yet are fundamentally constrained by the narrow instantaneous bandwidth, limiting their practical deployment in broadband scenarios. Prior approaches typically expand the bandwidth by physically broadening the atomic response, which usually requires auxiliary electromagnetic fields or stringent parameter tuning, thereby increasing overall system complexity. Here, we propose a compressive spectral multiplexing framework implemented in a waveguide-coupled Rydberg atomic receiver using a frequency-modulated local oscillator (FMLO). The FMLO creates multiple parallel sensing channels that collectively constitute a physical compressive sensing matrix, generating multiple narrowband intermediate-frequency replicas of the input signal. Thus, a broadband microwave spectrum is projected onto a set of narrowband atomic responses. It is demonstrated that spectral information spanning a bandwidth of over 640 MHz can be effectively compressed into the intrinsic atomic bandwidth of 126 kHz, achieving a spectrum compression ratio exceeding 1000. Furthermore, these output replicas offer intrinsic measurement redundancy and facilitate signal-to-noise ratio enhancement. An approximate 10 dB gain is achieved in the required bit-energy-to-noise-power-density ratio for multi-channel communication via maximal-ratio combining. This approach requires no auxiliary fields or broadband electronics, providing a simple and scalable pathway for chip-scale quantum receivers, latency-critical sensing, and next-generation wireless communications.
Quantum waveform estimation, in which quantum sensors sample entire time series, promises to revolutionize the sensing of weak and stochastic signals, such as the biomagnetic impulses emitted by firing neurons. For long duration waveforms with rapid transients, regular quantum sampling becomes prohibitively resource intensive as it demands many measurements with distinct control and readout. In this paper, we demonstrate how careful choice of quantum measurements, along with the modern mathematics of compressive sensing, achieves quantum waveform estimation of sparse signals in a number of measurements far below the Nyquist requirement. We sense synthesized neural-like magnetic waveforms with radio-frequency-dressed ultracold atoms, retrieving successful waveform estimates with as few measurements as compressive theoretical bounds guarantee.