Dev Kapadia ’23, Engineering, 6/5/20
This graph is representative of digital and analog signals. The red curve, which is much smoother than the blue curve, represents an analog signal because the function that represents the signal is continuous. This means that in moving from 0 to 1, the signal not only holds the value of 0 and 1 at different points in time, but it also holds the value of every value between the two numbers. The blue curve represents a digital signal because the graph jumps to each step instead of increasing or decreasing continuously. These signal representations are much like the discrete and continuous variables used as information storage methods by the French and American researchers described in this article.
Source: Wikimedia Commons
Quantum computing has been a fantasy for researchers starting from when the theory was first proposed in the 1980s.5Ever since, academic institutions and computing giants, like the hardware, software, and hosting products and consulting services company IBM, have been making breakthrough after breakthrough to make what seems like a science-fiction fantasy, a reality. In recent years, the benefits of quantum computing have seemed so close that companies outside of the computing space are showing interest. In fact, in December of 2019, Goldman Sachs, a financial institutions firm, partnered with startup QC Ware Corporation, a company with the goal of perfecting methods of quantum computing, to identify various applications and limitations of quantum computing specific to the functions of the bank.2
In classical computing, the functions on the computer are executed by switches allowing electricity in the high voltage or low voltage position. These positions correspond to 1 and 0, respectively, in the base data unit of classical computing, the “bit”. However, quantum computing relies on the assumption that its basic data unit of computing can occupy states between two discrete states (in classical computing they are 1 and 0), a property known as superposition. A computing unit in superposition can be both on, in the 1 state, and off, in the 0 state, at the same time. These quantum computing units are referred to as “qubits.”1 And, because the switches of classical computing cannot occupy this in-between state of being both on or off, qubits are commonly represented as particles, like light photons or electrons, that have properties that can occupy this superimposed state.
Quantum computing allows for an immense improvement in computing speed, which is useful for a variety of applications from environmental analysis, to stock trading at banks, to model computation in physics.4 These faster speeds correspond to benefits for communication with an added improvement: a sharp increase in communication security.3 The big idea here is that quantum networking can revolutionize the way that humans communicate, and French researchers at the Kastler Brossel Laboratory in Paris and American researchers at the U.S. National Institute of Standards and Technology have just taken another huge step toward the reality of quantum networking.
For the first time, the French and American team have experimentally demonstrated “hybrid” quantum networking. Traditional methods have relied on encoding information in waves using either discrete variables (DVs) or continuous variables (CVs). However, the researchers encoded their qubits in the form of photons (light particles) that were in both CVs and DVs in the waves.6
To explain DVs and CVs, it helps to understand the classical, non-quantum, equivalent, which are analog and digital signals. The curve of an analog signal is a smooth, continuous, and time-varying signal – like a sine wave. Digital signals, on the other hand, take incremental steps, instantaneously “stepping” from numbers, such as 0 to 1 to 6 to 3 and back to 0.6,7 In quantum computing, DVs act much like digital signals, where information is stored in variables with specific values by counting photons, and CVs act much like analog signals, information is stored in a continuous storage method through photons of varying intensity.6
Compared to CVs, DVs have a number of advantages and disadvantages. While DVs are less error-prone and more fault-tolerant than their CV counterparts, they are harder to apply to current technologies. CVs are more efficient than DVs, but they also are more delicate and can lose signal easier. Therefore, by combining both methods of information storage, researchers can combine the advantages of both methods into one system.6
In addition to superposition, data distribution within quantum networks uses another property of quantum theory called entanglement. If two particles are “entangled,” then when the state of one particle is changed, it will instantaneously cause the other particle to change its state.6 Not only will this capacity for instantaneous change in state allow for faster communication between two quantum computers, but it will also allow for increased security. For example, if a computer has hacked into the quantum network and it accesses the data stored by a qubit, then that qubits state will be changed because, under the theory of quantum mechanics, qubits change to either the on or off state if they are observed. Because this observation causes a change in the entangled particles, a security computer could observe this change and identify that the communication network has been breached. The security computer could then work on fixing the security fault instantly.6
Working on a quantum network, the French and American research team established and distributed DV and CV encoded entanglement in the “states of limit,” thereby incorporating a hybrid quantum network. In short, they entangled the photons in two states. The first entangled state was generated by splitting a photon down to a CV and a DV path, and the second entangled state was generated by specifically entangling a CV qubit with a DV qubit. Then, using a procedure called Bell state measurement, the two entangled states were imposed on the particles in the network, providing the researchers with a successful CV and DV entangled system.6
Despite the excitement around the current study, there are several current limitations. The biggest issue currently is that the process is very inefficient, only creating a hybrid entanglement between a CV qubit and a DV qubit three times per minute across a distance.6 This problem, along with others surrounding the scalability of the process need to be addressed in order for this accomplishment to have practical value. Nonetheless, it brings the world one step closer towards a future of quantum computing.
References:
- (2019, December 10). Quantum Computing: How it differs from classical computing? NEWS BBVA. https://www.bbva.com/en/quantum-computing how-it differs-from-classical-computing/
- Castellanos, S. (2019, December 10). Goldman Taps Startup to Explore Quantum Wall Street Journal. https://www.wsj.com/articles/goldman-taps- startup-to-explore-quantum computing-11575986520
- Explainer: What is quantum communication? (2019, February 14). MIT Technology Retrieved July 5, 2020, from https://www.technologyreview.com/2019/02/14/103409/what-is-quantum communications/
- Gamble, S. (2019, January 28). Quantum Computing: What It Is, Why We Want It, and How We’re Trying to Get It. In Frontiers of Engineering: Reports on Leading Edge Engineering from the 2018 Symposium. National Academies Press (US). https://www.ncbi.nlm.nih.gov/books/NBK538701/
- How Quantum Computers Work. (2000, December 8). HowStuffWorks. https://computer.howstuffworks.com/quantum-computer.htm
- Khadilkar, D. (2020, July 2). ‘Hybrid’ Quantum Networking Demonstrated for First Time. Scientific American. Retrieved July 5, 2020, from https://www.scientificamerican.com/article/hybrid-quantum-networking– demonstrated-for-first-time/
- What are Analog and Digital Signals, and Their Differences. (2019, March 18). ElProCus – Electronic Projects for Engineering Students. https://www.elprocus.com/differences-between-analog-signal-and-digital-signal/