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This excerpt from the Stanford Emerging Technology Review (SETR) focuses on quantum technologies, one of ten key technologies studied in this educational initiative. SETR, a project of the Hoover Institution and the Stanford School of Engineering, harnesses the expertise of Stanford University’s leading science and engineering faculty. Download the full report here and subscribe here for news and updates.
Quantum technologies are based on the physics of quantum mechanics, which emerged early in the twentieth century. Since then, quantum mechanics has shaped many technologies, from nuclear weapons to the transistors in smartphones to magnetic resonance imaging (MRI) machines in hospitals. But in these applications, the constituent atoms have been controlled in aggregate, with large, uncontrolled groups of particles all in multiple states manipulated together as an ensemble.
Modern quantum technology seeks to control the components of an ensemble particle by particle. The goal is to develop real-world applications by precisely controlling many particles that are all doing many things simultaneously, which requires an enormously complex effort.
Key directions
While there are many potential technologies based on quantum principles, the three most mature are quantum computing, quantum communication, and quantum sensing. In the long run, it is most likely that these quantum technologies will complement rather than replace their classical counterparts.
- Quantum computing will be useful primarily for solving problems that classical computing cannot, but these problems for the most part will be niche problems rather than general ones of broad interest.
Quantum computing is advancing rapidly, making clear progress toward solving practical problems such as breaking existing public-key encryption algorithms, enabling new materials design, and supporting applications in chemistry. More speculative uses include machine learning, weather modeling, and financial portfolio optimization. - Quantum communication may also have niche applications, such as for cryptographic key distribution and distributed quantum computing. However, it is unlikely to be a broadly applicable technology for communications infrastructure because it requires specialized hardware to implement.
- Quantum sensors will not render classical sensors obsolete in the short to medium term. Rather, they will be used primarily in application areas where they have particular advantages, such as greater sensitivity or measurement stability. To the extent that quantum sensors push the limits of what quantum mechanics allows in terms of power usage, sensitivity, size, and so on, most sensors are likely to eventually incorporate quantum technology in some form (though not necessarily as quantum networked sensors).
Quantum networking and sensing are emerging as powerful technologies—networking may be critical for scaling computers to utility levels, while sensors are already transforming fields such as medical imaging and gravitational detection.
What quantum can do
The most commonly used algorithms in asymmetric cryptography are based on the difficulty of factoring large numbers and related problems. Classical computers factor numbers by testing one set of possible factors at a time, a process whose time to completion grows very rapidly as the numbers get very large. Shor’s algorithm, developed in 1994 by Peter Shor, exploits quantum parallelism to efficiently perform such tests over all possible inputs simultaneously. This reduces the time needed to break encryption from thousands of years to potentially hours or minutes with a sufficiently powerful quantum machine.
Shor’s algorithm is expected to provide exponential speedup over what is possible with classical computing, making it a top candidate application for quantum computing. The best current estimates suggest that the most used asymmetric cryptographic algorithm—RSA-2048—could be breakable in days to weeks on a quantum computer coming online in five to fifteen years. (Note, however, that Shor’s algorithm is not an algorithm that works against all possible asymmetric cryptography algorithms. It only works against asymmetric algorithms based on the difficulty of factoring large numbers and related problems.)
From chemistry to materials science, simulations using quantum computers are expected to be exponentially faster than ones using classical computers. They are expected to have significant positive impacts on problems like nitrogen fixation, drug development, and superconductivity and enable breakthroughs in chemical, drug, and catalyst design. The analog simulators currently solving these problems are likely to be replaced by more precise simulations on gate-based quantum computers. Ultimately, quantum computers may generate complex quantum data that classical computers can then use to efficiently manage more routine quantum chemistry simulations.
These application areas have significant economic or national security value, and gate-based quantum computing potentially offers substantial speedups for the above problems over classical computing.
It is difficult to predict which problems will be most directly affected by quantum computing. However, what seems clear from the evolution of both classical computers and neural networks is that new computing paradigms always lead to new opportunities. Further research in quantum algorithms is thus essential and is likely to accelerate as quantum computing hardware for testing the algorithms becomes more sophisticated and accessible.
Quantum supremacy
While fault-tolerant quantum computers capable of useful computation for codebreaking, quantum chemistry, and other uses remain some years out, an ongoing effort exists to demonstrate quantum supremacy. This refers to the quantum computation of any quantity sufficiently complex that a classical computer cannot replicate the same result.
The endeavor to demonstrate quantum supremacy has resulted in an extremely productive race between quantum computing teams and classical computing teams, with the former running ever more complicated “random quantum circuits” and the latter demonstrating that they can in fact predict the output of these circuits on classical computers. The latest generation of superconducting quantum processors are large enough that they can now perform certain calculations that are difficult or impossible to replicate on classical computers. However, this is a benchmark—the calculated quantity is not inherently useful, even if it is beyond the reach of a classical computer to calculate.
Nonetheless, this work points to classes of problems where quantum supremacy is indeed possible and drives progress in understanding the quantum/classical computability boundary.
Government-funded basic research in academic labs remains the foundation for breakthroughs in quantum technology, and sustained investment is essential to maintain leadership as companies push applications toward real-world utility.
