By Lisa Drummond, Lead Technologist at Praxis Engineering, VA Sector
For nearly a decade, I have worked as a data scientist at Praxis Engineering and have seen how Praxis’ experts in cyber security, cyber network operations (CNO), artificial intelligence and machine learning (AI/ML), software development, containerization, and high-performance computing architectures leverage their skills to tackle national security challenges in support of numerous defense and intelligence customers. To ensure we have the skills necessary to meet customer challenges, Praxis promotes employee professional development and industry engagement. In this vein, last fall I was able to take part in an introductory training on quantum computing.
Like many who are new to quantum computing, my knowledge only extended to headlines touting the technology’s potential revolutionary impacts. The technology almost seemed magical to me. My first goal in the training was to get a better understanding of what quantum computing is and what makes it different from traditional computing. I learned that, at the simplest level, traditional computers solve algorithms and store data using bits, which can only take on two values, 0 or 1. Quantum computers use qubits for similar purposes.
However, these qubits and their interactions can hold more information than the binary data held by traditional bits. This gives quantum computers potential advantages in solving certain types of problems, but this additional complexity also comes with potential difficulty in implementation. The training also helped me understand the current limitations of quantum technologies. Quantum technology is still early in development and there are many current areas of research, ranging from effectively implementing quantum circuits in hardware to designing quantum algorithms.
As I delved deeper into the details of the quantum computing, I enjoyed learning more about the mathematics and physics underpinning the technology, but three non-technical takeaways stood out to me and surprised me. These takeaways could be helpful to you if you’re just beginning to investigate quantum technologies.
1. There are still many unanswered questions in quantum technology.
Unlike traditional computing hardware that has an accepted, standard, and reliable method for constructing bits, there are many possible ways to construct quantum hardware and implement quantum circuits. These implementations include trapped ions, superconducting materials, and photons. Quantum circuits need to be able to implement quantum algorithms and represent qubits and their interactions.
Given current hardware limitations, quantum circuits are susceptible to noise and errors, where qubits are not able to hold information accurately over time or through an algorithm’s execution leading to potentially incorrect answers. This could be due to many factors which are often related to the specific physics of the hardware implementation. Techniques are being explored to reduce this noise as well as to detect and correct any errors as they occur. However, many of these methods involve using more qubits than would be theoretically required by an algorithm. This becomes an issue in terms of execution as many quantum hardware implementations are not yet able to construct or maintain larger numbers of qubits.
Further work is also being done to refine quantum algorithm design. This encompasses constructing algorithms using quantum logic gates instead of traditional logic gates and ensuring algorithms can be implemented give current hardware limitations.
2. Quantum is not just a technology of the future – it is being applied right now!
While many pieces of quantum technology are still taking shape, the technology began being discussed as early as the 1980s with one of the most notable quantum algorithms, Shor’s algorithm, being developed in 1994. This quantum algorithm is theorized, given a large and accurate enough quantum computer, to enable the breaking of ubiquitous public-key cryptographic systems.
Beyond theoretical discussions, quantum computing algorithms are currently being applied in industry. Current real-world applications of quantum computing technology highlight areas where quantum algorithms may have advantages. These current applications include numerical optimization (e.g. supply chain design, optimal scheduling, route planning), atomic and biochemical simulations (e.g. pharmaceutical design, material research), and reduced training time for machine learning models (e.g. anomaly detection, forecasting). While providing real benefits, these applications are still of a small or of limited scale given the current state of quantum technology where quantum computers have a limited number of qubits (~1000 qubits) and are still refining error detection and correction. However, there are many researchers working to increase the size and accuracy of quantum computing technologies.
Right now, individuals can access some current quantum computing implementations through cloud services and simulate quantum algorithms using emulator software.
3. As quantum computers continue to advance, they would not replace regular computers; they would complement them.
Current quantum computing applications use both traditional and quantum computing technology, which is referred to as hybrid algorithms. This could be thought of as similar to the way graphical processing units (GPUs) are currently utilized in conjunction with traditional central processing units (CPUs) for training large machine learning models. The quantum processing units (QPUs) would only be used for specific tasks or portions of algorithms where they have an advantage. A traditional computer processor would be used to coordinate when to use the QPUs. In this coordination, the traditional computer processor would send algorithm inputs to the QPU and collect results from the QPU. As quantum computing algorithms are developed and designed, their interaction with other types of processors will need to be considered.
Learning more about quantum computing transformed in my mind a topic that seemed like magic to a real set of technologies that are having real impacts right now. This clarified for me the current state of quantum technologies, where they are having effects now, their current limitations, and in what areas current researchers are innovating. If this article has you curious about quantum computing, I encourage you to not be intimidated to learn more by taking advantage of the many tutorial resources available online or digging into current real-world implementations.
To learn more about Praxis Engineering Technologies, please visit www.praxiseng.com.
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