An ultrafast quantum random number generator based on quantum phase fluctuations

Transcription

1 An ultrafast quantum random number generator based on quantum phase fluctuations Feihu Xu, Bing Qi, Xiongfeng Ma, He Xu, Haoxuan Zheng, and Hoi-Kwong Lo Center for Quantum Information and Quantum Control, Department of ECE and Department of Physics, University of Toronto QCRYPT2011, ETH, Zurich, Sep. 14 th, 2011

2 Outline I II III IV Introduction Experimental setup and results Post-processing Future directions 2

3 Applications of random numbers Scientific simulations Lottery & Gambling Market Cryptography 3

4 Random number generator (RNG) RNG Algorithm: Pseudo RNG Hardware: Physical RNG Deterministic! Chaotic process Quantum process Deterministic! Fundamentally probabilistic QRNG! 4

5 QRNG: single photon detection Polarization measurement [1] PBS 0 Single Photon Source SPD 1 SPD Commercial QRNGs up to 16 Mb/s. (Figure is from ID Quantique) Photon arrival time [2-3] or photon number counting [4] Single Photon Source t APD Even odd Time [1] T. Jennewein, et al, Rev. of Sci. Ins., 71: , [2] P. Kwiat, E. Jeffrey, P. Altepeter, US Patent Appl , [3] J. Dynes, et al, App. Phy. Lett., 93, (2008) [4] M. Furst, et, al, Opt. Exp. 18, (2010). Fully integrated QRNG. (Figure is from ref [4]) 5

6 QRNG: vacuum state fluctuations [1-2] Homodyne detection measuring the electrical field fluctuations of Vacuum state. [1] A. Trifonov and H. Vig, US Patent No. 7,284,024, 16 October 2007 [2] C. Gabriel, et al, Nature Photonics, 4, (2010) QRNG with 6.5 Mb/s [2]. (Figure is from ref [2]) 6

7 Motivation: QRNG existing problems Low generation rate Typical rates: 6.5 Mb/s using vacuum state fluctuations,16 Mb/s using polarization measurement (commercial QRNG), 152Mb/s using photon arrival time [M. Wahl, et al, APL, 98, , 2011]. High cost For example, the IDQ system (Quantis, 16Mb/s) costs Eavesdropper (Eve) may have partial information Control side information (detector noise, environmental noise, etc). Probability 0 1 Voltage 7

8 Our approach: randomness from laser phase fluctuations Physical origin: spontaneous emissions [1, 2] Diode Laser Phase noise Splitter Reflector Short path Long path Combiner Imaginary part of laser field Intensity noise Reflector Photo detector V Ref θ Δθ Comparator or A/D convertor Binary random numbers Field increment due to a SE photon Real part of laser field Measure laser phase fluctuations Sample analog data Low cost & high rate! Generate digital bits [1] A. Yariv and P. Yeh, Photonics: optical electronics in modern communications (6th edition), Oxford University Press (2007). [2] K. Petermann, Laser diode modulation and noise, (Springer, 1988). 8

9 How the system works? Probability Laser Source Δθ Phase Self- Heterodyning MZI PD Voltage Sampling Probability V( t) cos Voltage [ ωt + Δθ ] Δθ Working point ΔV Phase delay of MZI 9

10 Our previous work L: 1550nm cw DFB laser diode; C 1,2 : fiber couplers PC1,2: polarization controller; PD1: 5GHz photo-detector; PD2: 1MHz photo-receiver; PM: phase modulator; OSC: 3GHz oscilloscope; Comp: computer with DAQ. Self-heterodyne system with off-the-shelf components. 500 Mb/s B. Qi, Y.M. Chi, H.-K. Lo, L. Qian, AQIS (2009) B. Qi, Y.M. Chi, H.-K. Lo, L. Qian, Optics Letters, 35, 312 (2010). 10

11 Our new setup PLC-MZI ADC Laser Detector Temperature Controller PLC- MZI: planar lightwave circuit Mach- Zehnder interferometer; ADC: 8- bit analog- to- digital convertor. Sampling rate: 1G samples per second Extractable random bits: 6.7 bits/ sample Generation rate over 6 Gb/s! F. Xu, B. Qi, X. Ma, H. Xu, H. Zheng, H.-K. Lo, arxiv: (2011) 11

12 Measurement results PLC-MZI ADC Laser Detector 0.95 mw Temperature Controller Time-domain A real-time oscilloscope. Frequency-domain A RF spectrum analyzer. Quantum phase fluctuation is dominant! F. Xu, B. Qi, X. Ma, H. Xu, H. Zheng, H.-K. Lo, arxiv: (2011) 12

13 Quantum signal and classical noise Laser phase fluctuations [1] Quantum: spontaneous emission Inversely power-dependent (Q/P) Classical: cavity instability, etc. power-independent (C) Electrical noise (detector) and EM noise (environment) F Quantify the parameters: V( t) cos < V Voltage 2 [ ωt + Δθ ] = AP >= 2 Q ( P AP Working point 2 Δθ + C) < Δθ 2 > + F + F = AQP + ACP 2 ΔV + F Phase delay of MZI [1] C. H. Henry, IEEE J. Quantum Electron. QE-18, 259 (1982). F. Xu, B. Qi, X. Ma, H. Xu, H. Zheng, H.-K. Lo, arxiv: (2011) 13

14 Quantum signal and classical noise 2 2 < V >= AQP + ACP + F F (mv²) AQ (mv²/mw) AC (mv²/mw²) 0.36± ± ±0.16 γ ACP AQP = 2 + F F. Xu, B. Qi, X. Ma, H. Xu, H. Zheng, H.-K. Lo, arxiv: (2011) 14

15 Post-processing Why we need post-processing? Eve may have partial information (by controlling classical noise). Quantum fluctuation is a non-uniform distribution (Gaussian). Extract out uniform-quantum randomness! Procedure Randomness extractor! Min-entropy evaluation Randomness extraction 15

16 Min-entropy evaluation Randomness is quantified by min-entropy H ( X ) = log (max Pr[ X = x]) 2 x {0,1} Quantum min-entropy is evaluated by a physical model. Assumptions of our physical model Quantum signal is independent of classical noise Quantum signal follows a Gaussian distribution Quantum signal to classical noise ratio can be calculated Total phase fluctuations, the mixture of quantum signal and classical noise, can be characterized by random sampling The sequence of the raw data is independent and identically distributed. n F. Xu, B. Qi, X. Ma, H. Xu, H. Zheng, H.-K. Lo, arxiv: (2011) X. Ma, et al, under preparation, (2011) 16

17 Randomness extraction Implement two extractors Universal Hashing [1] With Toeplitz matrix Trevisan s Extractor [2] Details of implementations: Xiongfeng Ma, et al, under preparation (2011) QRNG with information-theoretically proven randomness! [1] M. Wegman and J. Carter, Journal of computer and system sciences 22, 265 (1981). [2] L. Trevisan, Journal of the ACM 48, 2001 (1999). 17

18 Extraction results: universal hashing 1 GHz 6.7 bits = 6.7 Gb/s Summary Demonstrate a simple and fast QRNG over 6 Gb/s! Quantify the quantum randomness by min-entropy! Implement two randomness extractors to extract out the quantum randomness! F. Xu, B. Qi, X. Ma, H. Xu, H. Zheng, H.-K. Lo, arxiv: (2011) 18

19 Future directions Optimize system design. High-resolution, Highspeed ADC Balanced detectors with subtraction circuits Broadband source with narrowband filter High-speed electronics for real time randomness extraction. Random number storage & transfer. 19

20 Acknowledgements 20

21 Previous works: B. Qi, et al, AQIS (2009) B. Qi, et al, Opt. Lett. 35, 312 (2010) Current works: Feihu Xu, et al, arxiv: (2011) Xiongfeng Ma, et al, under preparation (2011) 21

22 Backup part 22

23 Autocorrelation of raw data 23

24 Autocorrelation of Toeplitz-hashing output 24

25 Autocorrelation of Trevisan s extractor output 25

26 Diehard 26

27 NIST 27

28 TestU01 28

29 Min-entropy evaluation: procedures Min-entropy (quantum) calculation steps: Determine the sampling range and evaluate the total variance (quantum signal + classical noise). Derive the quantum signal to classical noise ratio. Evaluate the variance of quantum signal (Gaussian distribution). Calculate the min-entropy of quantum signal (given classical noise, the quantum signal is a shifted Gaussian Distribution). 29

30 Our approach: randomness from laser phase noise Physical origin: spontaneous emissions [1, 2] Imaginary part of laser field Δθ Field increment due to a SE photon θ Real part of laser field Quantum phase change within time t can be treated as a Gaussian white noise [1,2] Δ θ ( t) ~ N(0,2πtΔf ) Laser linewidth [1] A. Yariv and P. Yeh, Photonics: optical electronics in modern communications (6th edition), Oxford University Press (2007). [2] K. Petermann, Laser diode modulation and noise, (Springer, 1988). 30

31 Laser Intensity Noise 31