Quantum Remote Sensing: Review and Perspective
Bi,
S. W.1* Jaffrès, H.2 Roychoudhuri, C. S.3
1.
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101,
China;
2. Centre National de la Recherche Scientifique,
Paris 75794, France;
3. University of Connecticut, Connecticut 06269,
America
Abstract: Quantum remote
sensing (QRS) is a new RS technology of the quantum world, both the theory and
method reflecting the laws of RS at the quantum level. The research elements
mainly include QRS theory, QRS information, experimentation, imaging and
calculation, quantum spectral RS and QRS applications on how to express and
convey information at the quantum state level to meet people’s perception of
such information. In March 2000, Prof. Siwen Bi instigated QRS research and in
early 2001 proposed QRS for the first time. Over the past 20 years, research in
QRS has resulted in breakthroughs with much progress and problems being
overcome in theoretical research, scientific experiments and key technologies.
The first SPIE QRS Session was held in San Diego in August 2019, with Prof. Bi
in the chair. Over 30 academics and experts in relevant fields from more than
10 countries attended the session and had in-depth discussion and deliberation
on QRS developments and applications. The session gave recognition to the new
status of QRS and its development in moving to a new level. This paper first
discusses the status of QRS from the standpoints of basic theory, research
methods and technology, applications and security, then it points out that as a
technology leading the direction of subjects development, QRS has significant
effects in improving spatial, spectral and time resolution, and in-depth
applications. Besides, the paper also refers to the status of overseas
research, thus aiming to strengthen Sino-International cooperation and
exchanges and hence promote joint research, development and new applications of
QRS and in so doing attain new levels of scientific excellence.
Keywords: Quantum remote
sensing, quantum images, technology and development, applications
1 Introduction
The first SPIE Quantum Remote Sensing (QRS)
Session was held in San Diego on August 13-14, 2019 with Prof. Siwen Bi,
researcher of the Aerospace Information Research Institute, Chinese Academy of
Sciences, in the chair.
Over 30 academics and experts
in relevant fields from more than 10 countries attended the session: Prof.
Henri Jeffery, Unité Mixte de Physique CNRS/Thales (France) who focused on
electromagnetic effects; Prof. ChandraSekhar Roychoudhuri, an SPIE fellow from
the University of Connecticut, who concerned the nature of light,
non-interaction of waves, superposition phenomena, and mode-locking vs.
time-gating; Prof. Narasimha Prasad from Langley Research Center NASA, aims to improving
the accuracy of optical tools including sensors in RS and laser radar and other
areas; Prof. Allen Huang from University of Wisconsin-Madison, who discussed RS
and atmospheric environment monitoring; Prof. Marija Strojnik, chair of the SPIE
Infrared
Remote Sensing and Instrumentation Conference, from Centro de
Investigaciones en Óptica, Leon, Mexico, whose research is about infrared RS
and photon research; Prof. Gabriele Arnold, the vice-chair of the SPIE Infrared
Remote Sensing and Instrumentation Conference, from Deutsches
Zentrum für Luft-und Raumfahrt eV (Germany), whose research was about planetary
RS; other key contributions were made by Prof. Jeffrey Zheng from Yunnan
University China, Prof. Weixiang Cui from China National Petroleum Corporation
and Prof. Chao Zheng from North China University of Technology.
The basic theory of QRS,
research methods and technology, application and safety, and future
developments were the key discussion topics.
2 Basic Theory of QRS
QRS refers to RS in the quantum world, which
possesses the singularity that the real world does not, such as non-locality,
wave-particle duality, tunneling, state coexistence (superposition state) and
quantum entanglement, which are the important elements of QRS research. The
emergence of QRS helps us understand more deeply the RS information
mechanism, RS computing, RS technology and applications such that we are better
able to grasp the underlying chemistry and physics information, mechanisms and
laws.
2.1 Science and Technology of QRS
Further development of RS requires higher
resolution, spectral and time resolution and novel applications, and QRS performs
well in these aspects[1] after 20 years of development.
In March 2000, Prof. Bi commenced basic research on
QRS and proposed a new discipline direction in early 2001, establishing a
theoretical and systematic technical framework for QRS[2]. In August
2006, Prof. Bi proposed the new concept of quantum spectral imaging, studying basic
quantum spectral theory to discover the characteristic spectral imaging laws. Based
on the above studies, Prof. Bi conducted researches on the wave-particle duality
of light and the nature of light, and proposed the generation mechanism of
lightstring and light-the string light effect[3-5].
Based on quantum optical field properties, Prof. Bi
proceeded to conduct experiments over a number of years, with the results
showing that the definition and edge resolution of quantum imaging was much
higher than that of laser imaging, implying that quantum imaging can break
through the quantum noise limit and the classical diffraction limit such that a
quantum radiation field imaging system can reduce quantum fluctuations of the
space radiation field and maintain the overall definition of the object. The
advantages of quantum light field squeezed light imaging include low noise,
high resolution and high imaging quality, which can help to obtain deeper,
richer and more microscopic information. Many QRS studies have resulted in
reliable experimental data, and the resolution has continuously improved, such
that a quantum light field squeezed light source may be used for imaging purposes
thus realizing the first quantum imaging experiment. Under the same conditions
as conventional optical imaging, the resolution of the quantum light field
squeezed light imaging is 2-5 times higher, which clearly has important
implications for use of QRS imaging technology in industrial and practical
problem solving applications[6-8].
Since 2013, quantum detection and identification
have been explored and quantum laser radar engineering prototype scheme has
been designed.
In December 2014, demonstration of the principle of
QRS imaging and completion of the world’s first prototype QRS imaging system
was achieved. Based on this, the research and design of a satellite borne QRS
active imaging scheme was accomplished[9-10].
2.2 QRS Data Processing Algorithm
The first QRS imaging prototype was developed after completion of many
stages of research, including the QRS theory, information, experimentation,
imaging, quantum spectral imaging and QRS calculation to QRS detection by
Prof.Bi and his team[11-12]. Based on the above results, the team
explored a new image processing method-quantum image processing algorithm,
which has been successfully applied into QRS fields and improves image
definition and signal-to-noise ratio (SNR), highlighting the advantages of QRS applications.
The
concept of quantum image processing was first proposed by Vlasov in
1997 [13]. In 2003, Beach and Venegas-Andraca independently proposed
quantum image processing algorithms [14-16] and tried to apply an
existing algorithm (Grover quantum search algorithm) to images. Since then,
quantum image processing has attracted considerable attention. In 2005, Latorre
proposed a new quantum image representation method [17]. In 2006,
the Google corporation used a quantum neural network learning method to reduce
the error rate of speech recognition (20-30% reduction) and image recognition
(26% to 15%) [18]. Since 2010, much more research has been conducted
on quantum image processing algorithms and the effects have been remarkable.
The application areas have increased year by year[19].
Quantum
superposition and entanglement can improve the efficiency of complex image
processing algorithms, and currently quantum image processing involves mainly
(1) using some concepts and methods in quantum mechanics to solve problems of
digital image processing in classical computing; (2) using quantum computers to
process digital images. To date quantum computers have not yet reached the needs
of calculation needed, so researchers have tended to conduct research in the first
topic.
Currently,
developments regarding processing algorithms include: a quantum denoising
algorithm and simulation experiment[20], a quantum enhancement
algorithm and simulation experiment[21], a quantum segmentation
algorithm and simulation experiment[22]. Besides, software (version
1.0) for a quantum image processing system has been completed and released.
Experiments show that the definition of images processed by the software are improved
some 2-3 fold such that more detailed information can be observed, and image
edge smoothness has been increased by about 2-fold, and the peak
signal-to-noise ratio (PSNR) of the images can be increased by over 10%. These
advances provide more accurate and abundant information and will result in more
fields benefiting from QRS technology[23].
Quantum image processing, based on quantum mechanics, is a new processing
method for quantum mechanics-based RS images and gives full recognition to the
advantageous characteristics in the quantum domain. Quantum image processing
combines quantum mechanics theory and RS image processing technology, introducing
a new research direction for RS image processing technology [24-26]. Although the theory in this new field
is not yet mature, it is difficult to realize quantum image processing in the
real sense without the availability of quantum computers, but the advantages
afforded by exploiting quantum theory are likely to have profound impacts on
the development of future computing tools.
As an extension of quantum mechanics and quantum
information in the field of RS image processing, QRS image processing
research includes not only a simulation algorithm for quantum systems, laying a
theoretical and systematic foundation for future quantum computing technology
based on quantum physics equipment, but an expansion of some theories such as
quantum mechanics and quantum information to RS image processing, providing a
new concept and platform for theoretical research and technology realization.
2.3 Theory of Superimposed Wave Packet and Photoelectric Detection
The superimposed wave packet
is a concept based on the combination of Born’s statistical interpretation of
the wave function and the principle of quantum state superposition. During
photoelectric detection, a series of quanta, emitted by a laser, is received at
the detector surface and processed by algorithms after photoelectric
conversion; thus the required information is obtained, during which, wave
transmission follows the statistical law governing the wave function and
quantum state superposition principle. In 2014, Honggang proposed a scheme to
generate and condense the atom and the detected photon hybrid entangled state
through stimulated transition of three-level atoms interacting with a weaker
coherent probe light and a stronger classical light coupling. This represents
an application of hybrid photons in quantum communication, quantum
teleportation and other aspects [27].
Hybrid photons are used mainly in photoelectron detection in QRS as
described by Prof. Chandra, an SPIE fellow. He stated that the present
observational and causality arguments were to underscore the point and that a
better model for light is a “hybrid photon” wave packet[28]. At the
moment of quantum transitions, the electromagnetic (EM) energy is embedded in
the transient quantum, hν. However, the EM energy immediately evolves into a
diffraction spreading classical, quasi-exponential, EM wave packet. This hybrid
photon accommodates both quantum and classical optics. The quantum formalism
has demonstrated staggering successes in modeling the micro world of atoms. The
photoelectron counting statistics should vary depending upon the relative
phases, spacing and amplitudes of the superposed wave packets (hybrid photons)
as they simultaneously arrive and stimulate the quantum mechanical dipole
complexes on the surface of the photo detectors.
2.4 QRS Matrix Transformation and Variant Transformation
In quantum optics, quantum
statistics and photon statistics play a key role. From the perspective of
spectrum analysis, quantum statistics are significantly different from
classical random signal sequences. The study of quantum statistics focuses on
the hybrid ensemble, which uses the principles of quantum mechanics to describe
and explain the physical properties of the hybrid ensemble at the macro-level
and to describe the state, the density operator, the probability density
function and so on of the mixed ensemble.
From a quantum statistical viewpoint, the four typical quantum states are
Fock, sub-Poissonian, Poissonian and super-Poissonian states. Quantum
interactions are the focus among Fock and Poissonian states. Using quantum
statistics, modeling and simulation, this paper proposed two models: matrix transformation
(MT) and variant transformations (VT). The former is used in eigenvalue states
and the latter is used in invariant states to analyze three
random sequences: 1) random; 2) conditional random as a constant; 3) periodic pattern. Fast Fourier Transformation (FFT) is
applied as one of the MT schemes and two invariant schemes are applied for the
VT schemes, and three random sequences are in M segments and each segment has a
length m to generate a measuring sequence. Shifting
operations are applied on each random sequence to create m+1 spectrum
distributions. For FFT, a pair of eigenvalues are selected as the output. Two types of 1D and 2D variant maps are generated to
illustrate multiple parameter selections to produce a series of results.
Given that sequences 1) and 3) are simply related, more cases focus on
sequences 2). An improvement on FFT, VT distinguishes various Fock,
sub-Poissonian and Poissonian states in random analysis to distinguish three
random sequences as three levels of statistical ensembles: micro-canonical,
canonical and grand-canonical ensembles[29].
Applying two transformations to research on quantum statistics, modern
quantum theory and application models and simulations can facilitate further
progress in QRS.
3 QRS Research Methods and Technology
QRS is a
promising technology and will become an important frontier for the next generation
of RS. The meeting covered all aspects of RS technology from theoretical
principles to applications, reflecting the progress and broad prospects for QRS
technology.
3.1 AMR Wheatstone Bridge Sensor
The basic unit of
the AMR (anisotropic magnetoresistance) Wheatstone bridge sensor is a long and
thin Ni-Fe alloy deposited on a silicon substrate using a semiconductor
fabrication process[30]. The thin film is arranged in the form of a
strip during deposition. A planar linear array is formed to increase the area
of the magnetic field induced by the magnetic resistance. The applied magnetic
field changes the orientation of magnetic domain inside the magneto resistance,
and then the angle between the magnetic domain and the current change[31].
With the advantages of high
sensitivity, low noise and high SNR, AMR technology is widely used[32].
In the QRS imaging system, a detector with enhanced sensitivity is needed to
improve detection accuracy and imaging resolution. Therefore it is hoped that
AMR technology will be applied to QRS imaging systems.
3.2 The Latest Technology and Development of Low-light RS Detectors and
Imaging
For low-light RS detectors, especially in the near-infrared band, low
temperature cooling is required, and the components packaged with the detector
and the Stirling refrigerator may be subject to import and export restrictions.
In addition, there may be technical shortcomings in the pre-amplifier and data
processing algorithms and other aspects, which would particularly affect noise
in the signal readout. Quantum squeezed light sources, however, can effectively
solve this problem. Meanwhile, in conjunction with a phase-sensitive amplifier
with noiseless amplification, the image is amplified two-fold and more detailed
image information is obtained.
In 2004, Dorn et al.
described a 4048 × 4048 pixel InSb array detector, whose size was equivalent to
a 52 × 62 pixel InSb array detector of the 1980s and has been used in near infrared
(NIR) cameras by the National Optical Astronomical Observatory (NOAO) [33].
In 2017, the University of Science and Technology of China used AC modulation
technology to effectively remove 1/f noise, and successfully developed a
prototype system for near-infrared background light measurement of the
Antarctic sky[34]. In 2018, Xiang et al. reviewed the basic
principles and advantages of low-light RS detection and imaging. The
technology, with low-light enhanced CCD (ICCD), makes up for the shortcomings
in current imaging devices and offers many advantages including low noise, high
sensitivity and intelligent electronic control [35].
The study has crucial
practical significance given the widespread development prospects for
long-distance optical communication, satellite RS, LiDAR and atmospheric
detection. In the case of long distances, it is important to receive various
signals timely and precisely to ensure detection accuracy. In this regard, using
QRS methods and technologies to carry out research on RS detection and imaging
technology in weak light fields has promising future.
3.3 Modeling of Photon Statistical Distributions
Classical and
quantum behavior can be distinguished by various quantum states in the statistical
distribution of photons, but these technologies need advanced laser or photon
technologies, which are costly and require complex control systems. From the
perspective of state simulation, it is very important to find a method that is
easy to implement and control. Considering the comprehensive advantages, FFT
has been widely used in signal processing technology. With high-speed hardware,
signals real-time processing can be achieved. But how to use these tools to
simulate non-stationary randomness still needs exploring.
In modern photon statistics,
classical and quantum behavior can be distinguished by various quantum states
of photon statistical distributions: Poisson (coherent/semi -classical wave
behavior) and sub-Poisson (compressed state/particle behavior). Given that this
type of measurement mechanism is often associated with advanced laser/optical
or photonic techniques, can this type of distribution model be modeled using
discrete 0-1 sequences? Several sets of simulation modes were designed, and FFT
was used to extract relevant eigenvalues. Following the processing methods in
the variant construction, special filters were constructed using the quantum
random sequence provided by the ANU (Australian National University), and
conditional random sub-sequences were collected as input sequences[36].
Multiple segments were separated from a random sequence, and the relevant
eigenvalues of the FFT were selected to form a special set of eigenvalues. The
shift operations were used to transform each sequence, and this showed clear
non-stationary random effects on various maps.Traditional methods require high costs
and advanced laser technology, while use QRS image technologies can speed up
technological breakthroughs.
4
Research on
QRS Application and Safety
4.1 Remote Sensor
QRS technology
has significant potential in ground observation and geological exploration.
Like classical sensors, quantum sensors consist of a sensing element that
converts the signal and a readout device that processes the signal. While the
difference is that direct measurement of the quantum state is not easy to
achieve — it requires a transformation of the measured object into a physical
quantity — it is easy to measure according to certain quantum control rules,
and then the realization of the indirect measurement of the quantum state.
Therefore, quantum control has an unquestionably central position in RS
technology.
With
Birmingham University acting as a research hub, the UK has established a
quantum sensor and measurement center which attracts key researchers from
academia and industry. Given the developments in quantum control, lasers,
cooling, magnetic fields and other related components much progress is being
made in the subject. For example, researchers are developing compact low-power
lasers, and large vacuum systems and magnetic traps for cold atoms have been
replaced by chip level devices[37]. Thus, it is becoming more
convenient for researchers to manipulate quantum states and observe their
environmental impact, which further promotes the practical use of quantum
sensors. Among them, the quantum sensing technology for measuring magnetic
fields and gravity fields has made great progress.
The quantum gravity sensor
can capture and control the quantum state of cold rubidium atoms by using laser
and magnetic fields in vacuum, whereby the atomic ratios at different energy
levels are measured to give a measure of the intensity of the gravity field.
The gravity gradient can be obtained by measuring two groups of independent
atomic clouds at different energy levels. Compared with traditional sensors,
quantum sensors are non-destructive, real-time and high sensitivity. With the
continuous development of quantum theory and associated control technology,
quantum sensors are expected to assume prominence in the fields of construction
engineering, medicine and health, mineral resource exploration, natural disaster
detection and gravitational field measurement [38-40].
4.2 QRS Communication Security
Since the 1960s,
RS has gained development as an advanced technology for acquiring geospatial
information, and has been widely used in many fields such as resource
exploration, environmental monitoring. As a carrier of RS information, the
focus on EM waves has been on its properties that are related to classical
physics and optics. While based on the principles of classical physics and
optics, there are limits to resolution and measurement[41-44].
Quantum mechanics can overcome the limitations of classical mechanics and
improve imaging and measurement resolution. There are now quantum technologies
that take advantage of the quantum properties of light. Therefore, combining RS
with quantum mechanics is a natural development to improve the level of RS
measurement and expand quantum scientific research. Many concepts, methods and
techniques can be directly applied to RS, for example, quantum imaging can be used
in RS imaging to improve resolution [45-48].
QRS in the future should be purely
quantum, so quantum technology will be applied to every process of RS, such as
quantum sensing imaging, information processing and communication, where security
is a core issue. Usually, the two communication parties take the quantum state
as information carrier and use quantum mechanical principles and various
quantum characteristics to transmit effective information between the two
communication parties in a secure and leak-free manner through the quantum
channel. Application of secure direct quantum communication in RS communication
and combining them into secure direct QRS communication can enhance the
receiver’s and user’s security performance greatly [49].
The session mainly focuses
on security communication during QRS- QRS communication. Given that both the
QRS and quantum communication use quantum states for quantum information
processing, the two disciplines are set up to establish a natural link and
adopt a secure direct quantum communication in RS. With a secure direct quantum
communication scheme, the session proposed the first QRS secure direct
communication protocol. Benefiting from advantages of secure direct quantum
communication: direct transmission of information, safely and reliability with
no information leakage, this scheme will offer more advantages and hence is a
future-oriented quantum technology and application.
5 Prospects
Experts and
academics held the meeting in high regard. The participants expressed new insights
on underlying principles, modeling, devices, technology, instrument research
and innovative applications. There was also a focus on the best and most
extensive applications of QRS data which showed how this technology can deliver
capabilities beyond the bounds of currently exploited technologies.
This
session was an affirmation of the future of QRS. It not only attracts the wide
attention and active participation on a global scale, but also provides a
professional platform to display research results and discuss cutting-edge
technologies. QRS is a innovative and disruptive contribution, scientists
should strengthen cooperation, promote jointly the development of QRS.
Nowadays high SNR and high
spatial resolution RS technologies are urgently needed to enhance resource
exploration, weather information gathering, environmental monitoring, land
utilization and many other fields. realizing high-resolution imaging requires
increases in both sensor size and optical system sensitivity. A consequence is
a dramatic increase in sensor volume, mass, cost and complexity. Given the
classical electromagnetic wave is influenced by the diffraction limit and
quantum noise limit and that increasing the resolution has been close to the
limit of traditional RS techniques, one of the main research areas recently has
been on identifying a set of directions and ways whereby quantum properties
could be used to improve various classical RS devices performance. Although
quantum sensing technology is not as mature as quantum computing, the creation
of a full-scale quantum computer is much more difficult than designing a quantum
sensor. Recent demonstrations and prototypes using quantum optics and quantum
theory have guided our belief that quantum sensing is a promising technology
that could have a significant impact on improving the overall performance for
both societal benefit and commercial activity.
The election of Prof. Bi as
chairman and his successful hosting of the first QRS Session are not only an
affirmation of the progress in QRS made by Prof. Bi over the past 20 years, but
also of its future prospects. Today, national organizations such as SPIE and
NASA have highlighted QRS technology as a strategic scientific discipline. With
the rapid development of science and technology, it is believed that the era of
QRS is fast approaching.
References
[1]
Bi, S.
W. Research on quantum remote sensing science and technology [C] Proc. SPIE
11128, Infrared Remote Sensing and Instrumentation XXVII, 111280S, 9 September
2019.
[2]
Bi, S.
W. Research on quantum remote sensing [J]. Acta
Science, 2005, 57(6): 33–35.
[3]
Bi, S.
W, Han. J. X. Research on the Spectrum Structure of Quantum Remote Sensing [J].
Science in China E Version: Technical
Science, 2006(S1): 62–67.
[4]
Bi, S.
W. New Viewpoints in Light Quantum Research: Lightstring [M]. Nova Science
Publishers, Inc. In: Progress in String Theory Research, Editor: Fred P. Davis,
2016, 131−149.
[5]
Bi, S.
W. On Two heuristic viewpoints concerning the study of light [C]. International
Conference on Photonics and Optical Engineering, Oct.13–15, 2014.
[6]
Bi, S.
W., Lin, X. L., Yang, S. Technology study of quantum remote sensing imaging
[C]. Proc. SPIE 9755, Quantum Sensing and Nano Electronics and Photonics
XIII, 97552J, February 13, 2016.
[7]
Bi, S.
W. High-resolution imaging via quantum remote sensing [C]. SPIE Newsroom, 26
February, 2016. DOI: 10.1117/2.1201602.006298.
[8]
Bi, S.
W., Zhen, M., Yang, S. Research on active imaging information transmission
technology of satellite borne quantum remote sensing [C]. SPIE 10403, Infrared
Remote Sensing and Instrumentation XXV, 1040303, 30 August 2017. DOI: 10.1117/12.2279162.
[9]
Bi, S.
W., Zhang, Y. The study of quantum remote sensing principle prototype [C].
Proc. SPIE 9524, International Conference on Optical and Photonic
Engineering (icOPEN 2015), 95241F, 17 July 2015.
[10]
Bi, S.
W., Lin, X. L., Wu, Z. Q. Development technology of principle prototype of
high-resolution quantum remote sensing imaging [C]. Proc. SPIE 10540, Quantum
Sensing and Nano Electronics and Photonics XV, 105400Q, 26 January 2018.
[11]
Bi, S.
W. Research on the concept, framework and connotation of quantum remote sensing
[J]. Journal of Infrared and Millimater
Waves, 2003, 22: 1–9.
[12]
Bi, S.
W, Han. J. X. Experimental Research on Middle and Far Infrared of Quantum
Remote Sensing [C]. Beijing: Proceedings of the 15th National Remote Sensing
Conference, 2005: 8–15.
[13]
Vlasov,
A. Y. Quantum computations and images recognition [OL]. arXiv:
quant-ph/9703010, 1997. https://arxiv.org/abs/quant-ph/9703010.
[14]
Beach,
G, Lomont, C. Cohen, C, Quantum image processing [C]. In: Proceeding of the
2003 IEEE workshop on Applied Imagery Pattern Recognition, 2003: 39–44.
[15]
Venegas-Andraca,
S. E., Bose, S. Storing processing and retrieving an image using quantum
mechanics [C]. In: Prossings of the SPIE Conference Quantum Information and
Computation, 2003: 137–147.
[16]
Venegas-Andraca, S. E., Bose, S. Quantum computation and
image processing: new trends in artificial intelligence [C]. In: Proceedings of
the International Conference on Artificial Intelligence IJCAI-03, 2003:
1563–1564.
[17]
Wang,
Y., Feng, X. Y., Huang, Y. X., et al.
A novel quantum swarm evolutionary algorithm and its application [J]. Neuro
Computing, 2007, 70(4/5/6):
633–640.
[18]
Yu,
K., Jiang, L., Chen, Y. Q. Yesterday, today and tomorrow of deep learning [J]. Journal of Computer Research
and Development,
2013, 50(9): 1799–1804.
[19]
Jiang,
N. Quantum Image Processing [M]. Beijing: Tsinghua University Press, 2016.
[20]
Bi, S. W., Chen, H. Research on denoising algorithm of quantum remote
sensing image data [J]. Journal of Global Change Data & Discovery,
2018, 2(3): 256–270. DOI: 10.3974/geodp.2018.03.03.
[21]
Bi, S, W.，Ke, Y. X. Research on enhancement
algorithm of quantum remote sensing image data [J]. Journal of Global Change Data & Discovery, 2018, 2(4): 367–376. DOI: 10.3974/geodp.2018.04.01.
[22]
Bi, S, W.，Rao, S. W. A segmentation Algorithm for
quantum remote sensing image data [J]. Journal of Global Change Data & Discovery, 2019, 3(1): 19–26. DOI: 10.3974/geodp.2019.01.03.
[23]
Bi, S.
W., Chen, H., Ke, Y. X., et al. Processing algorithms for quantum remote
sensing image data [C]. Proc. SPIE 11128, Infrared Remote Sensing and
Instrumentation XXVII, 111281A, 9 September 2019.
[24]
Fu, X.
W. Research on Image Processing Methods Based on Quantum Mechanics [D]. Wuhan: Huazhong University of Science and Technology,
2010.
[25]
Feynman, R.
P. Simulating physics with computer [J]. International Journal of
Theoretical Physics, 1982, 21(6/7): 467–488.
[26]
Shor,
P. W. Algorithms of quantum computer: discrete logarithm and factoring [C].
Proceeding of the 35th Symposium on the Foundations of Computer Science, 1994:
124–134.
[27]
Yi, H.
G. Generating atom-photon hybrid entangled state with stimulated Raman
transition [J]. Chinese Journal of
Quantum Electronics, 2014, 31(3): 279–284.
[28]
Roychoudhuri, C. Developing causal interpretations for high
and low level light used in quantum remote sensing [C]. Proc. SPIE 11128,
Infrared Remote Sensing and Instrumentation XXVII, 111280M, 9 September 2019.
[29]
Zheng,
J., Zhang, X., Zheng, C. Generating 2D maps from Fock to Poissonian states on
variant maps using random sequences [C]. Proc. SPIE 11128, Infrared Remote
Sensing and Instrumentation XXVII, 111280N, 9 September 2019.
[30]
Yao,
X. B., Zheng, W. L., Liu, Z. Y. Development of ferromagnetic metal film
magnetoresistive sensor [J]. Journal of
Anhui University, 1995, 15(1): 45–49.
[31]
Pei,
T., Yu, N. F., Liu, Q., et al.
Principle and Application of Anisotropic Magnetoresistive Sensor [J]. Instrument Technique and Sensor,
2004(8): 26–27, 32.
[32]
Hu, L.
T. The investigation of the AMR linear magnetic sensor [D]. Chengdu: University
of Electronic Science and Technology of China, 2018.
[33]
Dorn, R. J., Finger, G., Kaeufl, H. U., et al. The CRIRES InSb megapixel focal plane array detector mosaic
[C]. SPIE Astronomical Telescopes Instrumentation. International Society for
Optics and Photonics, 2004: 510–517.
[34]
Chen,
J. Research on key technologies of imaging system in astronomy [D]. Hefei:
University of Science and Technology of China, 2017.
[35]
Xiang,
S. M., Fan, X. W., He, N., etc. Review on the development of low-light remote
sensing imaging technology [J]. Laser
& Optoelectronics Progress, 2018,
55(2): 89–100.
[36]
Zhang, X., Zheng, J. One-dimensional Eigenvalue distributions
of random sequences for FFT non-stationary randomness [J]. Proc. SPIE 11128,
Infrared Remote Sensing and Instrumentation XXVII, 1112819, 9 September 2019.
[37]
Battersby,
S. Quantum sensors probe uncharted territories, from Earth’s crust to the human
brain [J]. Proceedings of the National
Academy of Sciences, 2019, 116(34): 16663–16665.
[38]
Jensen,
K., Skarsfeldt, M. A., Stsrkind, H., et
al. Magnetocardiography on an isolated animal heart with a room-temperature
optically pumped magnetometer [J]. Scientific
Reports, 2018, 8(1): 16218.
[39]
Ménoret,
V., Vermeulen, P., Le Moigne, N., et al.
Gravity measurements below 10–9 g with a transportable absolute
quantum gravimeter [J]. Scientific
Reports, 2018, 8(1): 12300.
[40]
Tang,
S., Liu, H., Yan, S., et al. A
high-sensitivity MEMS gravimeter with a large dynamic range [J]. Microsystems & Nanoengineering, 2019, 5: 45.
[41]
Bi, S.
W., Jing, D. S. The giving and studying of quantum remote sensing and complex
calculation [J]. 3S world, 2002, 19(4):12–13.
[42]
Bi, S.
W. Exploring study on concepts, framework and connotation of quantum remote
sensing [J]. Journal of Infrared and
Millimeter Waves, 2003, 2: 1–9.
[43]
Bi, S.
W., Han, J. X. An experiment using mid and thermal infrared in quantum remote
sensing [J]. Science in China Series
E-Technology Science, 2006, 49(Sup. II): 1–10.
[44]
Bi, S.
W., Han, J. X. Study on spectral structure of quantum remote sensing [J]. Science in China Series E-Technology Science,
2006, 49(Sup.II): 64–69.
[45]
Bi, S.
W., Han, J. X. Study of information mechanism of quantum remote sensing [J]. Science Technology Review, 2006, 24(9):
38–42.
[46]
Pittman,
T. B., Shi, Y. H., Strekalov, D. V. Optical imaging by means of two-photon
quantum entanglement [J]. Physics Review
A, 1995, 52(5): R3429- R3432.
[47]
Ron,
M., Denth, S. D., Shi, Y. H. Ghost-imaging experiment by measuring reflected
photons [J]. Physics Review A, 2008,
77: 041801.
[48]
Wang,
L., Bi, S. W., Wang, G. G. Multimode squeezed light generation in a
three-plane-mirror confocal cavity [J]. Acta
Physica Sinica, 2010, 59(1): 87–89.
[49]
Zheng,
C. Quantum remote sensing secure direct communication [C] Proc. SPIE 11128,
Infrared Remote Sensing and Instrumentation XXVII, 111280R, 9 September 2019.