Building the future of quantum communications by developing integrated optics technologies.
Quantum Random Number Generation
What is a Quantum Random Number Generator?
A Quantum Random Number Generator (QRNG) uses the inherent randomness of quantum to produce random numbers.
Every other form of randomness can be influenced by hidden variables, while quantum is random by nature.
Research has shown that this can be done, and it already passes the NIST tests for randomness, what we do differently is that we are putting it on a chip. This could become a standard module in computers allowing for truly random encryptions.
Quantum Key Distribution...
The efficiency of classical communication systems is heavily dependent on the strength and clarity of an incoming signal at the receiver. Error correcting code (ECC) can help in some situations to increase or simply maintain information efficiency at great distances. However, these classical systems struggle in photon starved environments. Examples of such environments might include deep space communication applications or even a direct Earth-to-Mars communication link with a rover on Mars’ surface.
Current technologies can require exorbitant amounts of power to send a strong enough signal to be clearly received. However, we are developing a device that leverages the quantum nature of light to send and receive data long distances more efficiently in low photon environments than any other existing technology today. In fact, our device beats the ideal information efficiency limit for any classical receiver in low photon environments.
The most useful encoding system in low photon environments is Pulse Position Modulation (PPM). It’s efficient and has a low error rate. However, when sending such a pulse, we have to make sure the output power is high enough such that the received signal is above some minimum power threshold or noise floor. Consider that over great distances we may receive on average less than one photon per pulse. If we look at any form of Quadrature Amplitude Modulation (QAM) above 4-QAM where amplitude plays a large role in encoding information, it becomes evident how PPM has a significant advantage.
However, sending QAM signals is much simpler. The Greene Machine (GM) is a device which performs a transformation on a received signal that converts it from QAM to PPM.
Figure 1. Pulse position modulation demonstrated in time bins compared with our proposed method of sending a pulse in each time bin, each with a relative phase relationship.
Consider an n-bit binary phase-shift keyed (BPSK) “codeword.” Using PPM, each codeword would only have one of the n bits “active,” corresponding the information to be transmitted. Instead of attempting to send a pulse in the correct time bin (which would require not only a synchronized clock on the transmitting and receiving end, but also the ability to receive such a clear signal—the same challenge as with classical devices), we propose sending some signal in each time bin, but with a relative phase change between each time bin (see Figure 1).
Figure 2. Various input codewords with their relative phases, the Green Machine architecture, and the resulting outputs .
As mentioned above, the Green Machine is an architecture that performs a linear transformation on an incoming signal. Supposing we use some binary phase-shift keyed (BPSK) encoding system, as illustrated by the received codewords in Figure 2, the GM performs a linear transformation that converts these linearly independent, maximally orthogonal BPSK codewords into a linearly independent basis with PPM encoding. We see that the codewords both on the input and output sides of the transformation are a basis for their respective codespaces.
Therefore, instead of sending a pulse in a single time bin corresponding to the bit we’d like to transmit, we can send a pulse in every time bin, each with a relative phase relationship. On the receiving side, we can send each pulse to its own channel (perhaps with time demultiplexing) and allow each codeword to travel simultaneously through the GM architecture. The signals comprising the codeword are then allowed to interfere with each other throughout the GM structure. Even though each channel receives on average less than one photon, since the photon is not observed or detected until it has passed through the architecture (using perhaps a single photon detector), due to the quantum nature of light (namely, the principles of superposition and uncertainty), we can receive on average one photon with a very high probability of appearing at the correct PPM encoded output port.
Figure 3. Diagram and actual construction demonstrating how light travels through the green machine free-space setup from the fiber launch (FL), interfering through beam splitters (BS), reflecting off mirrors (M), and returning to the photodiodes (PD).
We were able to implement a 4-port version of this device in free-space . Previous research has demonstrated that it is possible to characterize devices whose codewords are not ideal (not maximally orthogonal) due to errors in fabrication such as path length differences. Even so, each codeword is distinct enough from the other codewords to allow for efficient encoding or decoding.
Figure 4. One of the green machine integrated photonic chips we currently perform our experimentation on.
We are in the process of replicating the free-space results on an integrated photonic chip. Using precision alignment techniques, we can test these cleanroom-fabricated chips and run the same algorithms as were used on the 4-channel free space setup to characterize these 8-channel photonic circuits and learn their codewords.
Saikat Guha. “Structured Optical Receivers to Attain Superadditive Capacity and the Holevo Limit” Physical Review Letters, 106(24):240502, June 2011.
Alec M. Hammond, Ian W. Frank, Ryan M. Camacho, "Error Correction in Structured Optical Receivers," IEEE Journal of Selected Topics in Quantum Electronics, 24, 1 (2018).