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Preparing for the quantum revolution -- what is the role of higher education?

Michael F. J. Fox, Benjamin M. Zwickl, H. J. Lewandowski

arXiv:2006.16444v2physics.ed-phquant-ph

TL;DR

The paper addresses how higher education can prepare a workforce for the expanding quantum industry. It profiles jobs and skills across the industry and by role, finding that preparation needs include both academic alignment and practical experience. Its conclusions are bounded by the study’s qualitative coding and Fall 2019 scope.

  • Problem

    The study asks how higher education can prepare students and retrain workers for the quantum industry, including which skills employers value and which are hard to find.

    Source: §I · p. 1, §3 · p. 2

  • Method

    The paper characterizes job roles and synthesizes skills valued across the quantum industry and within particular roles.

    Source: §1 · p. 6, S.I. Skills needed by job · p. 15

  • Results

    Theorists’ work is generally well matched to Ph.D. preparation, while engineering training can include manufacturing best practices and continuing professional development.

    Source: S.B. Theorists · p. 19, S.C. Engineers · p. 19

  • Takeaways & Limitations

    An introductory quantum course focused on hardware or algorithms could serve general-interest learners and students across STEM majors.

    Source: §IV · p. 13

    Coding analyses of relationships among degree level, subject, and job type cover only cases with explicit or clearly implied same-context evidence, a subset of collected data.

    Source: A. Limitations of the research · p. 5

Abstract

from arXiv · show

Quantum sensing, quantum networking and communication, and quantum computing have attracted significant attention recently, as these quantum technologies offer significant advantages over existing technologies. In order to accelerate the commercialization of these quantum technologies the workforce must be equipped with the necessary skills. Through a qualitative study of the quantum industry, in a series of interviews with 21 U.S. companies carried out in Fall 2019, we describe the types of activities being carried out in the quantum industry, profile the types of jobs that exist, and describe the skills valued across the quantum industry, as well as in each type of job. The current routes into the quantum industry are detailed, providing a picture of the current role of higher education in training the quantum workforce. Finally, we present the training and hiring challenges the quantum industry is facing and how higher education may optimize the important role it is currently playing.

I. Introduction

The study examines how higher education can prepare students and retrain workers for a commercializing quantum industry, addressing workforce questions raised by the National Quantum Initiative. It frames the industry across five activity types and asks what jobs, skills, training routes, and hiring challenges characterize its workforce.

I. Introduction

  • The NQI Act highlights workforce development as a purpose, and the study focuses on student training and retraining existing workers rather than training academic researchers or educators.The authors describe the work as a resource for higher-education faculty and administrators, not an exhaustive quantitative account of companies or employment.Source: §I · p. 1
  • The authors define the quantum industry to include companies applying quantum information science in products or enabling such products, and the quantum workforce as people working in those companies or specialized divisions.They use broad definitions to avoid overlooking important parts of the industry.Source: §I · p. 1
  • Industry groups and federal initiatives are supporting workforce development through education working groups, university and national-laboratory collaborations, and dedicated research centers.The introduction identifies QED-C and IEEE activities, alongside federally funded Quantum Leap Challenge Institutes and Quantum Information Science Research Centers.Source: §I · pp. 1–2
  • The study asks what career opportunities and employer-valued skills exist, how employees acquired them, which training programs would help, and which skills are difficult to hire for.The authors present these questions as a basis for considering how higher education could better prepare students for quantum-industry careers.Source: §I · p. 2, §3 · p. 2, §4 · p. 2
  • The study distinguishes quantum sensors, networking and communication, computing hardware, algorithms and applications, and facilitating technologies as five industry activities.The categories were developed from interviews, public websites, and industry literature; companies may span multiple categories.Source: §4 · pp. 2–3

B. Distribution of company types

The company landscape spans varied activities and company sizes. Facilitating technologies and quantum algorithms and applications are prominent in the QED-C, while sensing, networking, and applications have distinct organizational profiles.

B. Distribution of company types

  • Facilitating technologies account for the largest number of QED-C companies; these firms are often small or medium-sized component specialists serving quantum and other industries.Some manufacture broadly used components, while others supply specialized equipment to university research laboratories.Source: B. Distribution of company types · p. 3
  • Quantum algorithms and applications are also among the largest QED-C activity groups, but their products and employee skills differ from those of computing-hardware firms.Some small application firms consult for larger companies and remotely access hardware built by medium-to-large firms, avoiding the barrier of building and maintaining that hardware.Source: B. Distribution of company types · p. 3
  • Quantum networking and communication companies are mostly medium-to-large, and the field was described as recently reinvigorated partly due to the NQI.Source: B. Distribution of company types · p. 3
  • Quantum sensing firms in the sample are small or small divisions of large companies, and sensing is described as the most established activity besides facilitating technologies.Atomic clocks have been commercially available since 1956; recent developments are moving precision improvements into commercial devices with reduced size, weight, and power.Source: B. Distribution of company types · p. 3

II. Methodology

The study used semi-structured interviews with U.S. quantum-industry companies and coded transcripts to characterize jobs, skills, and workforce preparation. A second inter-rater check found high agreement, including 98% average agreement on skills and knowledge codes.

II. Methodology

  • Interviews followed an approximately one-hour semi-structured protocol tested on a colleague and refined between interviews for clarity.Scientific and technical skills used as prompts came from the 2016 Joint Task Force on Undergraduate Physics Programs report.Source: §II · p. 3
  • The researchers conducted 22 interviews with 26 individuals, representing 21 companies after two interviews proved to involve the same company.Recruitment began with a QED-C request and expanded through snowball sampling; some interviews included more than one person from a company.Source: §II · p. 4
  • Transcripts were checked against audio, coded with an a priori codebook and emergent skill codes, then refined and recoded after an initial eight transcripts.An independent coder first checked whether existing codes had been applied appropriately; a later independent coding pass assessed agreement.Source: §II · p. 4
  • Confirmatory coding checks found 98% average agreement on skills and knowledge codes, while the second inter-rater test assessed agreement on job type, degree subject, and degree level.The authors state that the reported agreement measures indicate the robustness of the analysis and its numerical uncertainty.Source: §II · p. 4

A. Limitations of the research

The authors caution that the study provides qualitative, sample-based evidence rather than workforce estimates. Coding rules, recruitment, respondent coverage, and the rapidly changing industry constrain how broadly and how long the findings apply.

A. Limitations of the research

  • The qualitative design reveals depth and variation in required skills but cannot estimate current software-engineer headcounts or future employment numbers.The authors note that this approach also made companies more willing to participate because it avoided strategic information.Source: A. Limitations of the research · p. 5
  • Snowball sampling may reflect existing personal connections, and interviews with at most three people per company may not represent the whole company.The authors also acknowledge possible physics-related bias in identifying interviewees, while noting that technical familiarity enabled detailed accounts.Source: A. Limitations of the research · p. 5
  • Analyses linking degree level, subject, and job type include only cases where those codes were explicitly stated or clearly implied together, so they represent a subset of collected data.The codebook required all three details to occur in the same context for a case to receive all three codes.Source: A. Limitations of the research · p. 5
  • The sample reflects self-identified quantum-industry companies interested in workforce development, and the findings are only a Fall 2019 snapshot as technologies and skills evolve.The authors note that the industry lacks an agreed definition and that changing courses may also alter workforce supply.Source: A. Limitations of the research · p. 5

III. Results

The reported results count companies assigning each code rather than code occurrences; the job-type figure shows company shares, while the counts are sample-specific and not direct measures of importance.

III. Results

  • Numerical results count companies assigned a code, not the total number of times that code appeared across interviews.The authors give an example of a transcript segment labeled with a skill.Source: §III · p. 5
  • Each bar reports the percentage of 21 interviewed companies with employees in a job type; types identified by only one company are omitted.Panel (a) covers major job types and panel (b) engineering types only.Source: Figure 2 · p. 6
  • The numbers represent this interview sample, not necessarily the industry population, and response frequency does not always equal importance.Source: §III · p. 6

A. What are the career opportunities that exist in the quantum industry?

The study identifies five technical career types, with engineering positions especially common, but job boundaries overlap and company-level job distributions were not collected because they could reveal product-development stage.

A. What are the career opportunities that exist in the quantum industry?

  • The study groups technical careers into engineer, experimental scientist, theorist, technician, and application researcher; almost all interviewed companies reported engineers.Engineering jobs are further classified by sub-discipline.Source: A. What are the career opportunities… · p. 6
  • These are the authors’ job-position categories, not necessarily company titles or employees’ degrees, and responsibilities can overlap across roles.An experimental scientist may need skills usually associated with engineering, depending on the company.Source: A. What are the career opportunities… · p. 6
  • The study withheld within-company job distributions because they could reveal product-development stage; later-stage firms have more engineers and technicians to support manufacturing.Source: A. What are the career opportunities… · p. 6

B. What are the skills valued by employers?

The paper distinguishes valued workplace skills from deep quantum-information theory: deep theoretical knowledge is neither necessary nor sufficient for industry work, while classical skills are highly valued.

B. What are the skills valued by employers?

1. Shared skills across the quantum industry

Across the industry, employers value coding, statistical data analysis, and laboratory experience alongside electronics, troubleshooting, materials knowledge, and quantum algorithms or computer science.

Shared skills across the quantum industry

  • 90% of companies mentioned coding skills and 90% mentioned statistical methods for data analysis.Coding supports apparatus control, data collection and analysis, and software environments; data analysis spans analog signals and abstracted quantum-system outputs.Source: §1 · p. 6
  • 81% of companies expected laboratory experience, with pure quantum algorithm and application companies as the stated exception.For junior employees, a quantum-lab capstone or similar internship is a major plus; teaching labs can provide experience documenting work and proposing hypotheses.Source: §1 · pp. 6–7
  • 76% of companies valued electronics knowledge, while 71% valued troubleshooting and problem solving.Electronics controls and powers hardware; practical experience can help employers assess troubleshooting and problem-solving skills.Source: §1 · p. 7
  • 67% of companies sought materials knowledge relevant to their hardware, ranging from employee expertise to teams developing new materials.For the latter work, the paper notes a need for materials science, quantum mechanics, and condensed-matter background, but not necessarily quantum-information science.Source: §1 · p. 7
  • 62% of companies valued quantum algorithms and computer science, a skill category tied almost exclusively to quantum-computing companies.The paper distinguishes algorithm development, implementation on hardware, and application to specific problems.Source: §1 · p. 7

2. Variation in skills required by the quantum industry

The study groups technology-specific skills into potential quantum-information courses, organizing them from theoretical to experimental. Its tables are intended to help instructors develop courses or connect existing material to quantum-industry applications.

Variation in skills required by the quantum industry

  • The authors grouped skills into seven potential course areas, from traditional quantum theory to optics and opto-mechanics, using coding analysis of interview transcripts.An independent coder categorized the skills separately; the comparison showed good agreement and led to minor refinements.Source: §2 · p. 7
  • Instructors can use the skill tables either to inspire new course topics or to connect existing course material with quantum-industry applications.The example links Schrödinger-equation time evolution to open-system qubit dynamics and decoherence.Source: §2 · p. 7
  • Table II illustrates how a real-world quantum information theory course could address statistical data analysis and noise in quantum-computation results.The table includes skills shared across multiple courses and interview quotes showing their industry relevance.Source: §2 · p. 7, Table II · p. 8
  • The section documents how employees acquire skills through both higher-education courses and on-the-job learning, including their academic routes and company training.Source: §2 · p. 8

1. Routes into the quantum industry through higher

Employees enter the quantum industry through physics, engineering, computer science, mathematics, and chemistry degrees. Physics Ph.D.s are common and work across job types, while degree-level patterns differ between physics and engineering.

Routes into the quantum industry through higher education

  • Physics, engineering, computer science, mathematics, and chemistry are routes into the quantum industry, and most sampled companies reported at least one employee with a physics Ph.D.Figure 3 presents the top degree-and-subject combinations by the percentage of companies reporting at least one employee with each combination.Source: §1 · p. 8, Figure 3 · p. 9
  • Companies reported Ph.D. physicists in every job type except technician, including engineering roles.The authors note that employees trained in physics may move into engineering roles after joining a company.Source: §1 · p. 8, Figure 4 · p. 9

2. Training provided by companies

Companies expect new hires to learn skills on the job, while higher education can contribute through research experience, targeted courses, and practical training. Product-specific knowledge learned in employment limits how fully universities can prepare students before hiring.

Training provided by companies

  • Company training is essential because product-specific knowledge, including proprietary design, manufacture, and operation, is learned only in employment.This sets an upper limit on how far higher education can prepare students before they enter the workforce.Source: §2 · pp. 9–10
  • 95% of interviewed companies lacked formal training structures, relying on on-the-job learning, especially independent study and mentoring.Independent learning was reported by 76% of companies and mentoring by 62%.Source: §2 · pp. 9–10
  • Ph.D. programs were generally viewed as the best preparation, with graduate physics knowledge and research experience among their key strengths.Suggested additions included collaborative software development, engineering and system design, and more awareness of business.Source: §2 · p. 10
  • 33% of companies requested a one- or two-semester quantum-awareness course, with distinct hardware and software tracks for different student backgrounds.The passage links broader recruitment across degree subjects and levels to the growing need for engineers as products move toward production.Source: §2 · p. 10
  • Research experience is valued: 19% of companies cited internships for students, while 14% favored a quantum-focused master’s with laboratory projects or internships.48% of companies reported hosting interns, mainly graduate students, and using internships to support recruitment.Source: §2 · pp. 10–11
  • 52% of companies wanted short, tailored courses for existing employees, and 33% valued hands-on experience with laboratory technology.University facilities can provide access to equipment that is costly to purchase and set up, with risk if it does not work.Source: §2 · p. 11

E. What are the skills that are currently hard to find when hiring for the quantum workforce?

Interviewed companies reported hiring difficulties for quantum information theorists, engineers with analog-electronics experience, and senior employees. The challenges varied across companies and can reflect differences in reputation, location, and hiring networks.

What are the skills that are currently hard to find when hiring for the quantum workforce?

  • Hiring challenges may vary with companies’ reputations, locations, and networks, so the interview sample may not represent every company’s hiring situation.Source: E. What are the skills that are… · p. 11
  • 33% of companies reported difficulty finding quantum information theorists who understand algorithm applications, with some describing the specialty as highly constrained.The passage suggests that expectations for candidates to fill specific roles immediately, alongside limited company resources for training, may contribute.Source: E. What are the skills that are… · p. 11
  • 29% of companies found engineers with analog-electronics experience challenging to hire, amid demand for skills that electrical-engineering degrees emphasize less.The passage notes competition for these skills from other industries.Source: E. What are the skills that are… · p. 11
  • 19% of companies reported difficulty hiring senior employees with quantum-industry experience who could drive programs forward.Interviewees linked this challenge to the industry’s relative newness and the small number of people with more than a decade in it.Source: E. What are the skills that are… · p. 11

IV. Discussion and Conclusions

The discussion finds that hardware-focused quantum work values classical physics and engineering skills alongside quantum knowledge, while job titles and training needs vary across the industry. It argues that higher education should align course choices with student needs, institutional expertise, and industry connections.

IV. Discussion and Conclusions

  • As quantum technologies mature into products, the discussion identifies increasing needs for engineers to improve reliability and cost, and for users able to program quantum computers.The need for those users depends on the timeline and success of hardware efforts; physics graduates remain needed in research and development.Source: §IV · p. 11
  • Classical physics and engineering skills are valued as much as, or more than, quantum information science knowledge for most current industry roles.The authors connect this to hardware development: implementing quantum theory requires precise control and isolation of physical systems.Source: §IV · pp. 11–12
  • “Quantum engineer” has no consistent meaning across companies, spanning Ph.D. physicists, bachelor’s-level hardware engineers, and quantum-software programmers.The phrase was mentioned by 29% of interviewed companies, and the authors recommend clarifying the intended role.Source: §IV · p. 12
  • The authors identify possible bias in the reported shortage of computer-science and mathematics employees: the sample may matter, and physicist-framed questions may have steered responses.Their inference about workforce composition is conditional on the interview sample being representative.Source: §IV · p. 12
  • 38% of companies mentioned machine learning, usually for analyzing data or optimizing hardware, but only 14% mentioned quantum machine learning without detail.The authors say companies generally do not seek quantum-machine-learning skills until the required hardware exists.Source: §IV · p. 12
  • 14% of interviewed companies mentioned quantum information science master's degrees as a workforce-pipeline strategy, while some preferred one- or two-semester quantum courses.The authors contrast this with higher-education institutions' interest in introducing master's degrees; they say most job growth will be in positions requiring less quantum expertise if Ph.D. supply does not increase.Source: §IV · p. 12
  • The study does not quantify company numbers, company sizes, or job distributions, and the authors note that the field lacks agreed terminology for quantum-industry jobs.They present their qualitative descriptions as a basis for vocabulary and definitions in future research and policy work.Source: §IV · pp. 12–13
  • Introductory courses can focus on hardware or algorithms, but broader programs must choose technologies, depth, audience, and department based on local expertise and student needs.Industry connections can provide routes into jobs and resources such as access to quantum computers and training content.Source: §IV · p. 13

S.I. Skills needed by job.

This section synthesizes interview findings on skills valued across quantum-industry jobs and the academic preparation and company training that support them. It outlines role-specific combinations of expertise and presents skill examples relevant to six possible courses.

S.A. Engineer (N = 20).

  • Engineers prioritize designing efficient, manufacturable systems; electrical, mechanical, optical, and systems roles add specialties in controls, low-noise hardware, optics, and whole-device integration.Electrical engineers build microwave or radio-frequency circuits and precision control systems; mechanical engineers support low-vibration, vacuum, or cryogenic systems; optical engineers work with beam modeling and lasers; systems engineers coordinate device-wide engineering projects.Source: S.A. Engineer (N = 20) · p. 15, §S.1 · p. 15, §S.3 · p. 15

S.2. Software engineer (N = 11).

  • Software engineers mainly write classical code for device control or user-facing quantum-computing tools, and companies reported classical-systems coding as their leading skill need (82%).Hardware-control work can overlap with electrical engineering and experimental science, while programming-language development may call for a computer-science background.Source: §S.2 · p. 15

S.B. Experimental Scientist (N = 18).

  • Experimental scientists, often physics Ph.D.s, lead hardware teams and rely most on laboratory experience, alongside hardware-specific quantum knowledge and broad experimental skills.Reported core skills include laboratory experience (67%), generic electronics (56%), statistics and data analysis (50%), and classical coding (44%); expected quantum knowledge is focused on the qubit hardware and system Hamiltonian.Source: S.B. Experimental Scientist (N = 18) · pp. 15–16

S.C. Theorist (N = 13).

  • Quantum information theorists focus on algorithms and their hardware implementation, while a smaller theorist group models hardware and optimizes practical systems.The first group may need algorithm knowledge (85%), circuit design (38%), error correction (38%), and quantum-systems coding (38%); hardware-modeling theorists report modeling, material-property, and Hamiltonian skills (each 50%).Source: S.C. Theorist (N = 13) · p. 16, physics · p. 16, §S.2 · p. 16

S.D. Technician (N = 9).

  • Technicians manufacture devices or operate systems through defined procedures, whereas application researchers advise clients on quantum-computing use and need algorithm, hardware, and coding literacy.Technicians typically need careful, patient handling rather than quantum-mechanics expertise; application researchers may need quantum algorithms (86%), linear algebra (57%), hardware approaches (57%), and demonstration coding (57%).Source: S.D. Technician (N = 9) · p. 16, S.E. Application researcher (N = 7) · pp. 16–17

S.1. Ph.D.

  • Physics Ph.D.s contribute especially to experimental science, theorist, and application-research roles through research-project experience and specialized quantum knowledge; physics bachelor’s holders more often enter engineering roles.A physics master’s holder’s fit for research work depends on research experience, and their job distribution resembles bachelor’s holders more than Ph.D.s.Source: §S.1 · p. 17, §S.2 · p. 17, §S.3 · p. 17

S.C. Computer science.

  • Computer-science Ph.D.s can enter theorist or application-research roles in quantum computing, while bachelor’s and master’s computer-science graduates mainly work as software engineers.Computer-science training also supports high-performance simulation; chemistry and mathematics degrees provide relevant domain or quantum-information theory preparation.Source: S.C. Computer science · p. 18, S.D. Chemistry · p. 18, S.E. Math · p. 18

S.III. Training provided by companies by job type

  • Companies train employees by role: experimental scientists learn proprietary hardware details and may need engineering or collaborative-coding skills, while theorists are more likely to apply closely related Ph.D. work immediately.Engineer training covers company products and processes, system physics, and manufacturing practices; it may include short quantum-awareness courses, manufacturing instruction, or employer-supported further study.Source: S.A. Experimental scientists · p. 19, S.B. Theorists · p. 19, S.C. Engineers · p. 19

S.IV. Interview protocol

  • The interview protocol asks how job descriptions and expected skills differ by education level, and whether employees can advance from BS-level positions through training or further study.It also asks what formal education is expected for each position.Source: Part 2a - Skills as a function of… · p. 21
  • Interview questions cover quantum-specific knowledge and scientific or technical skills, including what employees should bring to the job and what they may learn during employment.Examples include linear algebra and the Schrödinger equation, instrumentation, troubleshooting, coding, and statistical or uncertainty analysis.Source: Part 2b - Specific skills and knowledge · pp. 21–22
  • The protocol also probes hiring deficiencies, skills hardest to train, workplace training, and ways universities could prepare students or help employees fill gaps.It asks employers about the maximum time they would expect employees to spend on such training.Source: Part 3 - Training and hiring · p. 22

S.V. Courses and their relationship to skills valued by the quantum industry

  • Six supplementary tables list skills and industry-relevant examples for possible courses in traditional quantum theory, quantum information theory, hardware, electronics, mechanical engineering, and optics and opto-mechanics.The supplied passages identify the tables’ topics but do not provide their skill entries.Source: S.V. Courses and their relationship to… · pp. 24–29
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