In This Issue
The Bridge: 50th Anniversary Issue
January 7, 2021 Volume 50 Issue S
This special issue celebrates the 50th year of publication of the NAE’s flagship quarterly with 50 essays looking forward to the next 50 years of innovation in engineering. How will engineering contribute in areas as diverse as space travel, fashion, lasers, solar energy, peace, vaccine development, and equity? The diverse authors and topics give readers much to think about!

The Future of Quantum Computing Research

Monday, February 22, 2021

Author: Sara J. Gamble

Quantum computing emerged as a research area in the late 20th century yet has only recently experienced a dramatic rise in press coverage and corresponding popularity. While some of this is inevitably rooted more in hype than science, a look into the future suggests that quantum computers do, in fact, have the potential to greatly improve many areas of everyday life. Because a quantum computer can process data in ways that a traditional, classical computer simply cannot, certain problems that will be intractable on even future state-of-the-art supercomputers will be reasonable for quantum computers to tackle, providing benefits to government, industry, and society.

Several decades of predominantly academic research has led to the successful operation of small quantum computers, but many basic research challenges remain before the benefits of mature quantum computers can be realized.

What Are Quantum Computers?

Quantum computers and classical computers share the same goal: to manipulate data to answer questions. The way they manipulate the data, however, is fundamentally different.

Quantum Mechanics Principles: Superposition and Entanglement

Quantum computers are not simply very advanced classical computers, rather they are an inherently different computer type whose operation is rooted in some of the more exotic principles of quantum mechanics. Two of these principles are key to understanding how quantum computers manipulate data: superposition and entanglement.

Superposition is the ability of a quantum entity to simultaneously exist in multiple “states.” While traditional two-state systems, like the transistors that serve as the bits of a classical computer, must definitively be in either the off (0) or on (1) state, a quantum system with two levels can be in both simultaneously, in a superposition of 0 and 1. A quantum system in a superposition has only a probability of being in the 0 or 1 state until measurement, which definitively collapses it to 0 or 1. The ability to assume a superposition state is a key characteristic of qubits, the fundamental unit of quantum information.

Entanglement is a phenomenon in which component quantum entities are created and/or manipulated such that their individual identities are lost and only the collective (entangled) entity can be described. This collective description persists even if the entangled system is spread out over a large physical distance (the phenomenon Einstein dubbed “spooky”).

A quantum computer carries out quantum operations prescribed by a quantum algorithm on qubits that can be in superpositions and entangled. Through the algorithm, the probabilities of certain outcomes are enhanced and others depressed, even to zero, such that ultimately, when measurements are made at the end of a quantum computation, the probability of obtaining the correct answer is maximized. This exploitation of quantum mechanics and probabilities is what distinguishes quantum from classical computers.

Continued Coexistence of Quantum and Classical Computers

It is important to understand that quantum computers will (likely) never replace classical computers. The derivation of Shor’s algorithm in 1994 uncovered one type of problem intractable to a classical computer but efficient on a quantum computer: factoring. Here, “efficient” means “in a time of practical relevance.” The factoring problem underlies the security of the RSA cryptosystem, which underlies the security of nearly every online transaction, and this security is guaranteed only by the intractable nature of the factoring problem on a classical computer.

There is no reason to believe that every problem of relevance will be amenable to a quantum speed-up. Thus, the long-term vision is that quantum computers will work in consort with classical computers in various ways depending on the target application.

The exploitation of quantum mechanics and probabilities is what distinguishes quantum from classical computers.

Challenges in Building Quantum Computers for the Future

Today’s small quantum computers need to be significantly more sophisticated to execute the quantum algorithms needed for most practical applications. Thinking toward the next 50 years of research, several scientific and engineering challenges need to be overcome to realize large-scale quantum computers. Following are some examples of these challenges.

Increasing the Number of Qubits

The quantum computing model that carries out quantum operations prescribed by a quantum algorithm as described above is known as the gate-based model and is one of the most promising for large-scale quantum computations. Current state-of-the-art gate-based quantum computers have on the order of 50 qubits, whereas carrying out complex algorithms of practical relevance will require millions to billions of qubits.

All leading gate-based qubit systems have scaling challenges, which differ depending on the physical qubit type in use (e.g., trapped ions, superconductors, and semiconductors can all be used as qubits[1]). These scaling issues range from the need for an unmanageable number of lasers[2] to difficulty connecting and operating large numbers of qubits.

Increasing the Qubit and Qubit Operation Quality  

To carry out any sophisticated quantum algorithm the qubits in the system must be able to maintain their quantum states for at least an amount of time necessary to carry out quantum operations on them. This retention time is known as the coherence time and it is a challenge to extend it for most current qubit types.

Similarly, quantum algorithms require that quantum gate operations (like creating superpositions or entanglement) be carried out with high “fidelity,” which is nontrivial. Increasing coherence times and fidelity will require interdisciplinary skills in physics, materials science, and engineering.

Quantum Error Correction

Just as classical computers have error correction protocols, quantum computers need the same capability. The properties of quantum mechanics, however, preclude directly using classical error correction algorithms on quantum systems.

Error-correcting codes exist for qubit systems, but implementing them remains a major challenge. Without error correction, complex quantum calculations like those needed for practical applications are likely impossible. Achieving reliable error-corrected, fault-tolerant multiqubit operation will be a major milestone for the field in the next 50 years.

Underlying Classical Technologies

Many of the challenges associated with constructing sophisticated qubit systems extend beyond the qubits themselves. For instance, improved lasers, detectors, and classical control mechanisms will all be crucial over the next several decades of quantum computing research. It’s expected that many engineers beyond those with quantum mechanics backgrounds will be increasingly important.


A number of scientific and technical problems are likely amenable to quantum speedups. In addition to the factoring algorithm of importance for today’s encryption systems, problems related to optimization and logistics and the simulation of material and chemical systems will likely benefit from quantum-based approaches. Improvements stand to impact areas of vital importance such as targeted pharmaceutical drug and advanced material design, airline logistics, and machine learning as well as areas of (arguably) less importance such as streaming TV and movie recommendations.

Additionally, an active field of research is devoted to developing not only new quantum algorithms to expand the application space of quantum computers but also a deeper understanding of what problem types have structures that may make them favorable for quantum approaches.

With research and engineering problems spanning the quantum sciences, classical engineering, computer science, and mathematics, the next 50 years of quantum computing research and development will be exciting, challenging, and rewarding.


[1]  These are  three of the most prominent qubit types, but a significant amount of research is devoted to others as well, especially for applications beyond data manipulation (e.g., for data storage/quantum memories or quantum sensing). Different qubit types will likely be better for different applications.

[2]  The number of lasers needed scales roughly linearly with the number of qubits in the system. This is manageable for systems of tens of qubits, but not with millions or billions of them.

About the Author:Sara Gamble manages the Quantum Information Science Program in the US Army Research Office.