Expert Insights

Exploring
quantum
computinguse casesfor healthcare
Accelerate diagnoses,
personalize medicine,and optimize pricing

Experts on this topic
Dr. Frederik Flöther
Life Sciences & Healthcare Lead
IBM Quantum Industry Consulting
IBM Services
linkedin.com/in/frederikfloether/frederik.floether@ch.ibm.com

Judy Murphy, RN,
FACMI, FAAN
Chief Nursing Officer
IBM Global Healthcare
linkedin.com/in/judy-murphy-rnfacmi-fhimss-faan-4066442/
murphyja@us.ibm.com

John Murtha
Health Plan Industry Segment
LeaderIBM Industry Platforms
linkedin.com/in/john-g-murtha/
john.murtha@ibm.com

Dr. Daby Sow
Principal Research Staff Member
Center for Computational Health
IBM Researchlinkedin.com/in/daby-sow2a53b31
sowdaby@us.ibm.com

Dr. Frederik Flöther is an IBM Quantum Industry
Consultant and globally leads efforts for the Life Sciencesand Healthcare sector. Frederik is an IBM Academy ofTechnology member and Senior Inventor. He has deep
expertise in quantum computing and artificial intelligence
and works with clients to create value through these nextgeneration technologies. Frederik has authored more than
20 patents, peer-reviewed publications, and white papers.
Judy Murphy is Chief Nursing Officer at IBM Global
Healthcare. Previously, she was Deputy National
Coordinator at ONC in Washington DC and Vice President,
Applications at Aurora Health Care in Wisconsin. She
has more than 30 years of health informatics experienceand is a fellow in the American College of Medical
Informatics and the American Academy of Nursing.
Judy has published and lectured internationally andhas received numerous Health IT awards.

John Murtha sets the strategic direction for IBM’s Global
Health Plan Segment and works with priority accounts
envisioning next generation health plans. A twenty year
health plan industry veteran, John has led several large
scale transformation initiatives that extensively usedtechnology, and reengineered processes. Prior to joining
IBM, he was the Chief Operating Officer of VNS CHOICE
Health Plans and during his tenure their HIV Special
Needs Plan achieved the highest HIV Suppression Rate in
the country.

Dr. Daby Sow manages the Biomedical Analytics andModeling group in the Center for Computational Health.
He drives various efforts developing and applying novel
AI techniques to disease progression and intervention
modeling. Daby holds more than a dozen patents andhas authored more than 50 scientific articles and bookchapters in areas such as computational health,information theory, knowledge discovery and data
mining, middleware, and pervasive computing.

It has been proven thatquantum computing canhave an advantage overclassical approaches.

Talking points
Disruptive healthcare use cases
In the healthcare industry, quantum
computing could enable a range of
disruptive use cases for providers andhealth plans by accelerating diagnoses,
personalizing medicine, and optimizing
pricing. Quantum-enhanced machinelearning algorithms are particularly
relevant to the sector.
Benefit from multiple data sources
As access to health-relevant data sources
continues to grow, the potential for the
combination of quantum computing andclassical modeling to save lives andreduce costs increases.
Time to act is now
Healthcare is likely to benefit significantly
from quantum computing. However, muchof the early intellectual property in quantum
computing may be proprietary, raising theurgency to get started and engage withpartners and ecosystems today.

Data for improved healthcare
experiences and results
Healthcare data—such as information from clinical trials,
disease registries, electronic health records (EHRs), andmedical devices—is growing at a compound annual growthrate of 36 percent.1 Increasingly, this data helps address
challenges associated with the “quadruple aim” ofhealthcare: better health, lower cost, enhanced patient
experiences, and improved healthcare practitioner work
lives.2 At the same time, healthcare consumers are making
more decisions and have to navigate an increasingly
complex system.
Significant investments are being made to deliver the rightdata and powerful insights at the point of care. Industry
incumbents and new entrants alike are trying to create
digital experiences that reinforce healthy, preventive
behaviors. Despite that, accounting for the exponential
possibilities from this diversity of new data is stretching
the capabilities of classical computing systems.
Enter quantum computing.
A century after the birth of quantum mechanics, it has
been proven that quantum computing can have an
advantage over classical approaches.3 Quantum
computing does not merely provide an incremental
speedup. It is the only known technology that can be
exponentially faster than classical computers for certain
tasks, potentially reducing calculation times from years
to minutes.4
Quantum computing necessitates a different way of
thinking, a new and highly sought-after set of skills,
distinct IT architectures, and novel corporate strategies.
The technology also has immediate implications forsecurity.5 Security is an area of particular relevance forhealthcare, given the sector’s data privacy responsibilities
and challenges.

1Insight: Bits and qubits
Quantum computers process information in a fundamentally different way from traditional computers. Previous
computer technology advancements—such as integrated
circuits—enabled faster computing, but were still basedon classical information processing. Quantum computers
manipulate quantum bits (qubits).
These are unlike classical bits, which store information
as either a 0 or 1, and they can display uniquely quantum
properties, such as entanglement. As a result, it becomes
possible to construct quantum algorithms that can outperform their classical counterparts which are not able toleverage quantum phenomena.

In healthcare, as in other industries, using quantum
computers in concert with classical computers is likely to
bestow substantial advantages that classical computing
alone cannot deliver. As a result, there is now a race toward
quantum applications. Following are three key potential
quantum use cases that are central to the healthcare
industry’s ongoing transformation (see Figure 1):
1. Diagnostic assistance: Diagnose patients early,
accurately, and efficiently
2. Precision medicine: Keep people healthy based on
personalized interventions/treatments
3. Pricing: Optimize insurance premiums and pricing.

Quantum computers could be particularly useful in tacklingproblems that involve:– Chemistry, machine learning/artificial intelligence (AI),
optimization, or simulation tasks. In fact, machinelearning has shown potential to be enhanced by quantum
computing and is symbiotically helping drive quantum
advances6– Complex correlations and interdependencies among
many highly interconnected elements, such as molecular
structures in which many electrons interact– Inherent scaling limits of relevant classical algorithms.
For instance, the resource requirements of classicalalgorithms may increase exponentially with problem
size, as is the case when simulating the time evolution ofquantum systems.7

Figure 1
Quantum computers may enable three key healthcare use
cases that reinforce each other in a virtuous cycle. For
instance, accurate diagnoses enable precise treatments,
as well as a better reflection of patient risks in pricing
models.

Diagnostic
assistance
Precision
medicine

Pricing2

Quantum computing has thepotential to improve theanalysis of medical images,
including processing steps,
such as edge detection andimage matching.

Together, these use cases significantly help advance
healthcare’s quadruple aim. Diagnostic assistance could
improve health, cost, experience, and jobs, while precision
medicine should enable better patient outcomes andexperiences, and pricing is expected to help reduce costs.

Use Case 1: Diagnostic assistance
Early, accurate, and efficient diagnoses usually engender
better outcomes and lower treatment costs. For example,
survival rates increase by a factor of 9 and treatment costs
decrease by a factor of 4 when colon cancer is diagnosed
early.8 At the same time, for a wide range of conditions,current diagnostics are complex and costly.9 Even once adiagnosis has been established, estimates suggest that
it is wrong in 5–20 percent of cases.10
Medical imaging techniques, such as CT, MRI, and
X-ray scans, have become a crucial diagnostic tool forpractitioners over the last century. Computer-aided
detection and diagnosis methods for medical images havebeen rapidly developing. At the same time, many of theseimages are impacted by noise, poor resolution, and low
replicability.
One of the reasons for these challenges is the need toadhere to strict safety protocols. Quantum computing has
the potential to improve the analysis of medical images,
including processing steps, such as edge detection andimage matching. These improvements would considerablyenhance image-aided diagnostics.
Furthermore, modern diagnostic procedures may include
single-cell methods.11 In particular, flow cytometry andsingle-cell sequencing data typically require advanced
analytical methods, especially when considering combining
datasets from the different techniques.12

One challenge is the classification of cells based on their
many physical and biochemical characteristics. These
cause the feature space, that is, the abstract space inwhich the predictor variables live, to be large (highdimensional). Such classification is important, for example,
in distinguishing cancerous from normal cells. Quantumenhanced machine learning approaches, such as quantum
support vector machines, appear poised to enhanceclassification and could boost single-cell diagnostic
methods.
Moreover, discovering and characterizing biomarkers
may necessitate analysis of complex “-omics” datasets,such as genomics, transcriptomics, proteomics, andmetabolomics.13 These can entail a large feature space,
as well as many interacting features leading tointerdependencies, correlations, and patterns thatare challenging to find with traditional computational
methods.14 Further extending biomarker insights down to
the level of the individual naturally requires even moreadvanced modeling. These characteristics suggest thatquantum computing could help discover biomarkers,
perhaps even for individuals.
Through quantum computing, care providers may be able
to improve diagnoses while simultaneously eliminating theneed for repetitive invasive diagnostic testing. They may
be able to continuously monitor and analyze the health ofindividuals. In addition to helping patients, health plansand providers could also benefit from reduced treatment
costs as a result of earlier diagnoses. It might even become
possible to carry out meta-analyses for more elaborate
diagnostic procedures in order to determine which
procedure should be carried out, and when. This could
help further cut costs and enable more data-driven
decisions by health plans and governments for providers
and individuals.

3

Quantum-enhanced machinelearning techniques could allow
earlier, more accurate, and moregranular risk predictions.

Use Case 2: Precision medicine
Precision medicine aims to tailor prevention and treatment
approaches to the individual.15 Due to the complexity ofhuman biology, individualized medicine requires taking into
account aspects that go well beyond standard medicalcare. In fact, medical care only has a relative contributionof 10 to 20 percent to outcomes; health-related behaviors,socioeconomic factors, and environmental aspects
account for the other 80 to 90 percent.16 Computationally,
the interdependencies and correlations among thesediverse contributors create formidable challenges withregard to optimizing treatment effectiveness.
As a result, many existing therapies fail to achieve theirintended effects due to individual variability. For example,
only a third of patients respond to drug-based cancer
therapies. In some cases, consequences of drug therapies
can be disastrous; in Europe alone, up to 200,000 people
die each year due to adverse drug reactions.17
A key aspect of tailoring medical approaches is proactivity.
As mentioned, early treatments and preventive
interventions tend to drastically improve outcomes andoptimize costs. Classical machine learning has alreadyshown some promise in predicting the risk of future
diseases for a range of patient groups based on EHRs.18
Nevertheless, challenges remain due to the characteristics
of EHRs and other health-relevant data, including the levelof noise, size of the relevant feature space, and complexityof interactions among the features. This suggests
supervised and unsupervised quantum-enhanced machinelearning techniques could allow earlier, more accurate, andmore granular risk predictions.19 Eventually, medical

4

practitioners might even have the tools to understand how
an individual’s risk for any given condition changes overtime, enabled by continual virtual diagnostics based on
ongoing data streams from individuals.
Knowing an individual’s disease risk is not sufficient,however. Just as important is knowing how to effectively
medically intervene for any given individual. One avenue inthis endeavor is the study of drug sensitivity at the cellularlevel. For example, by taking into account the genomic
features of cancer cells and the chemical properties ofdrugs, models that can predict the effectiveness of cancer
drugs at a granular level are already being investigated.20
Quantum-enhanced machine learning could support
further breakthroughs in this area and ultimately enablecausal inference models for drugs.
The goal of precision medicine is lofty. Identifying andexplaining relationships among interventions andtreatments on the one hand—and outcomes on the other—in order to provide the next-best medical action at theindividual level. Traditionally, diagnosing a patient’s
condition has been based heavily on patient-reported
symptoms, which is time-consuming and results in an
umbrella diagnosis and associated treatment thatfrequently fails. We are now moving toward a setting where
insights from additional health-relevant data can beobtained to efficiently arrive at a continuous and precise
health status, along with personalized interventions (seeFigure 2). While we are still a long way from realizing this,quantum computing may be able to accelerate our progress
toward such a new framework by addressing problems ofthe type described in Use Cases 1 and 2.

Figure 2
Quantum computing has the potential to accelerate the transition fromumbrella diagnosis and treatment to precision health status and intervention.

Umbrella approach

Patientreported
symptoms

Umbrelladiagnosis

Umbrellatreatment

GranularhealthstatusPreciseintervention

Precision based approachManyfactors

This framework would allow healthcare organizations to
optimize and personalize their services throughout thecontinuum of care. Moreover, adherence and patientengagement are also key aspects to be considered when adecision is made as to the next-best medical action for agiven individual. Perhaps advanced computationalmodeling might be able to address this area, too.21
Eventually, population health management on a
personal level could become possible.22

5One key area in which quantum
computing may help optimize
pricing is risk analysis.

Use Case 3: Pricing
Determining health insurance premiums is a complex
process. A number of factors need to be taken into accountby a health plan in the process of developing a generalpricing strategy (recognizing that regulations in somecountries, such as the US, may limit the number of factors
used to calculate premiums).23 These include complex
interdependencies, such as population health levels anddisease risks, treatment suitability and costs, and the risk
exposure a health plan is willing and able to accept basedon corporate strategy and regulations. While health plans
have already made considerable progress in this space byapplying classical data science methods, achieving moregranular models with lower uncertainties remains difficult.
One key area in which quantum computing may help
optimize pricing is risk analysis. Use Case 2 discussed how
quantum computing could help better assess the risk agiven patient has for a given medical condition. Leveraging
these insights about disease risk at the population level,
and combining them with quantum risk models that cancompute financial risk more efficiently, could allow healthplans to achieve improved risk and pricing models.24
Another important lever through which quantum computing
may support pricing decisions is enhanced fraud detection.
Currently, healthcare fraud costs hundreds of billions ofdollars in the US alone.25 Classical data mining techniques
already help with detecting and reducing healthcare fraud;
nevertheless, more computationally efficient methods areneeded.26 Quantum algorithms could enable superior
classification and pattern detection and thus help uncoveranomalous behavior and eliminate fraudulent medical
claims.27 This is expected to allow health plans to further
optimize pricing strategies and offer reduced premiums asa result of having lower costs associated with fraud loss andprevention schemes.

6

Quantum computing’s novel algorithms may be able tosignificantly improve pricing computations. This could
result in lower average premiums, as well as better-tailored
premium options. The complexity of healthcare is reflectedin the challenges associated with making pricing strategies
easily understood. New regulations that requiretransparency and lower average healthcare costs makeit even more important to enhance pricing models.28

Outlook
Healthcare data—collected from the many different
modalities and locations that surround patients—isunderused today. Clinical factors only account for a small
percentage of health-relevant data. Therefore, obtaining
actionable insights from other areas, including genomics,
behavior, and social and environmental influences, iscritical.
Of the different types of quantum algorithms relevant to thehealthcare sector, quantum-enhanced machine learningalgorithms stand out for their breadth of application. Thisis because we are entering an era where the characteristicsof health datasets, such as being frequently heterogeneousand unevenly distributed, are producing complex
computational challenges for modern AI. For instance,
researchers have been exploring how to speed up thecomputationally expensive algorithms that are at the coreof machine learning and AI modeling with quantum
approaches, including operations with large matrices.29

The maturity horizons of quantum computing use casesin healthcare depend not only on cross-industry quantum
algorithm and hardware developments, but also on theincreasing availability of relevant data (see Figure 3).
While certain modalities are already being leveraged foradvanced computational modeling, health-relevant
datasets are rapidly growing from increases in both thenumber of people being “instrumented,” as well as the datatypes being collected. As the number of accessible healthrelevant data sources continues to grow, the potential for
quantum computing to add value likewise increases.

Anthem: Enhancing theconsumer healthcare experience
Anthem, a leading health benefits company, is expanding
its research and development efforts to explore howquantum computing may further enhance the consumer
healthcare experience. Bringing its expertise in working
with healthcare data to the IBM Q Network, Anthem is
exploring how quantum computing may help in developing
more accurate and personalized treatment options andimproving the prediction of health conditions.30

Figure 3
Relevant healthcare data sources and when they may
start driving significant value.

Within 5
years

Within 10
years

10+

– Claims– “-omics”– Customer
relationshipmanagement– Internet of
medical things– Clinicalresearch– EHRs– Social
determinants– Imaging– Wearables

years– Lab values

7Action guide
Exploring quantum computing
use cases for healthcare
Healthcare organizations are expected to reap
considerable business and scientific benefits in the era ofquantum computing. In addition to the benefits describedabove, this powerful technology may also create otheradvantages, such as attracting talent interested in working
with next-generation technology.
Quantum advantage is likely to be proprietary, bestowing
benefits on early movers. In order to start the journey andprepare for quantum advantage, healthcare organizations
should take the following steps:– Engage quantum champions. Identify, enable, and hire
quantum champions in your organization—including bothtechnology and healthcare professionals. They can serve
as focal points to connect quantum expertise withhealthcare imperatives. These quantum champions aretasked with staying up-to-date with the latest quantum
developments and driving quantum initiatives in theenterprise.
– Explore and prioritize. Explore potential quantum use
cases and prioritize the ones with the largest impact foryour organization. This includes determining how
quantum computing fits into your corporate andtechnology strategies. Update your priority use cases
over time based on the latest corporate strategy andquantum computing developments.
– Experiment. Implement relevant quantum applications
and experiment with real quantum computers.31 This willallow you to drive toward quantum advantage andprovide your employees with hands-on enablement.
These steps should be iteratively pursued. Accomplishingthem can be accelerated by joining a quantum ecosystem, agroup of industry and technology participants sharing risks
and rewards in pursuing common quantum computing
goals.32
Quantum computing has the potential to become atransformative enabler for healthcare organizations in theircore mission: sustainably saving and improving lives. Thus,
the time to get started with quantum computing is now.

8

Notes and sources
1

Kent, Jessica. “Big Data to See Explosive Growth, Challenging
Healthcare Organizations.” Health IT Analytics. December 3,
2018. https://healthitanalytics.com/news/
big-data-to-see-explosive-growth-challenging-healthcareorganizations

2

Bodenheimer, MD, Thomas and Christine Sinsky, MD. “FromTriple to Quadruple Aim: Care of the Patient Requires Care of
the Provider.” Annals of Family Medicine. November/
December 2014. http://www.annfammed.org/
content/12/6/573.full.pdf

3

Sutor, Bob. “Scientists Prove a Quantum Computing
Advantage over Classical.” IBM. October 18, 2018.
https://www.ibm.com/blogs/research/2018/10/
quantum-advantage-2/

4National Academies of Sciences, Engineering, and Medicine.
2019. Quantum Computing: Progress and Prospects.
Washington, DC: The National Academies Press. https://doi.
org/10.17226/25196

5

Rjaibi, Walid, Sridhar Muppidi and Mary O’Brien. “Wielding adouble-edged sword: Preparing cybersecurity now for aquantum world.” IBM Institute for Business Value. July 2018.
https://www.ibm.com/thought-leadership/institute-businessvalue/report/quantumsecurity

6

Havlíček, Vojtěch, Antonio D. Córcoles, Kristan Temme, Aram
W. Harrow, Abhinav Kandala, Jerry M. Chow and Jay M.
Gambetta. “Supervised learning with quantum-enhanced
feature spaces.” Nature. March 13, 2019. https://arxiv.org/
pdf/1804.11326.pdf; Carleo, Giuseppe, Matthias Troyer.
“Solving the Quantum Many-Body Problem with Artificial
Neural Networks.” Science. February 10, 2017. https://arxiv.
org/pdf/1606.02318.pdf

7Montanaro, Ashley. “Quantum algorithms: an overview.”
Nature. January 12, 2016. https://www.nature.com/articles/
npjqi201523.pdf

8Birtwistle, Mike. “Saving lives and averting costs? The case forearlier diagnosis just got stronger.” Cancer Research UK.
September 22, 2014. https://scienceblog.cancerresearchuk.
org/2014/09/22/saving-lives-and-averting-costs-thecase-for-earlier-diagnosis-just-got-stronger/

9Jack, Andrew. “Affordable diagnostics is the missing link inmedicine.” Financial Times. December 15, 2015. https://www.ft.com/content/46c4e51a-9451-11e5bd82-c1fb87bef7af

10 Singh, Hardeep, Ashley N. D. Meyer and Eric J. Thomas. “Thefrequency of diagnostic errors in outpatient care: estimations
from three large observational studies involving US adult
populations.” BMJ Quality and Safety. April 17, 2014. https://
qualitysafety.bmj.com/content/qhc/23/9/727.full.pdf; Graber,Mark, L. “The incidence of diagnostic error in medicine.” BMJ
Quality and Safety. June 15, 2013. https://qualitysafety.bmj.
com/content/qhc/22/Suppl_2/ii21.full.pdf

11 Wang, Daojing and Steven Bodovitz. “Single cell analysis: thenew frontier in ‘Omics’.” US Department of Energy Office of
Scientific and Technical Information. January 14, 2010.
https://www.osti.gov/servlets/purl/983315

21 Fogel, Alexander L. and Joseph C. Kvedar. “Artificial
intelligence powers digital medicine.” npj Digital Medicine.
March 14, 2018. https://www.nature.com/articles/s41746017-0012-2.pdf

12 Andreyev, Dmitry S. and Boris L. Zybailov. “Integration of
Flow Cytometry and Single Cell Sequencing.” Trends in
Biotechnology. February 1, 2020. https://doi.org/10.1016/j.
tibtech.2019.09.002

22 Coleman, Jr, Charles A., Angus McCann and Heather Fraser.
“Precision health and wellness: The next step for population
health management.” IBM Institute for Business Value.
December 2016. https://www.ibm.com/thought-leadership/
institute-business-value/report/phm

13 McDermott, Jason E, Jing Wang, Hugh Mitchell, Bobbie-Jo
Webb-Robertson, Ryan Hafen, John Ramey and Karin D
Rodland. “Challenges in biomarker discovery: combining
expert insights with statistical analysis of complex omics
data.” Expert Opinion on Medical Diagnostics. August 27,
2012. https://www.tandfonline.com/doi/abs/10.1517/17530
059.2012.718329
14 Shahrjooihaghighi, Aliasghar, Hichem Frigui, Xiang Zhang,
Xiaoli Wei, Biyun Shi and Ameni Trabelsi. “An Ensemble
Feature Selection Method for Biomarker Discovery.”IEEE International Symposium on Signal Processing andInformation Technology. 2017. https://www.ncbi.nlm.nih.gov/
pmc/articles/PMC6420823/pdf/nihms-1016736.pdf
15 Nilesh, Jain. “How precision medicine will change the futureof healthcare.” World Economic Forum. January 1, 2019.
https://www.weforum.org/agenda/2019/01/
why-precision-medicine-is-the-future-of-healthcare/
16 Hood, Carlyn M., Keith P. Gennuso, Geoffrey R. Swain, andBridget B. Catlin. “County Health Rankings: Relationships
Between Determinant Factors and Health Outcomes.”
American Journal of Preventive Medicine. February 1, 2016.
https://www.ajpmonline.org/article/S07493797(15)00514-0/abstract
17 Spilker, Isabell. “A crash test dummy for medicine.” Best
Practice. March 2018. https://www.t-systems.com/
blob/840450/9bc15bd337e9c5597ad2f85a012be216/
DL_Best-Practice_03-2018_FT_Gesundheitssektor.pdf
18 Ravizza, Stefan, Tony Huschto, Anja Adamov, Lars Böhm,
Alexander Büsser, Frederik F. Flöther, Rolf Hinzmann, Helena
König, Scott M. McAhren, Daniel H. Robertson, Titus Schleyer,
Bernd Schneidinger and Wolfgang Petrich. “Predicting theearly risk of chronic kidney disease in patients with diabetes
using real-world data.” Nature Medicine. January 7, 2019.
https://rdcu.be/bfKPU
19 Biamonte, Jacob, Peter Wittek, Nicola Pancotti, Patrick
Rebentrost, Nathan Wiebe and Seth Lloyd. “Quantum Machine
Learning.” May 14, 2018. https://arxiv.org/pdf/1611.09347.
pdf
20 Menden, Michael P., Francesco Iorio, Mathew Garnett, Ultan
McDermott, Cyril H. Benes, Pedro J. Ballester, and Julio SaezRodriguez. “Machine Learning Prediction of Cancer Cell
Sensitivity to Drugs Based on Genomic and Chemical
Properties.” PLOS One. April 2013. https://journals.plos.org/
plosone/article/file?id=10.1371/journal.pone.0061318&type=printable

23 “How insurance companies set health premiums.”
HealthCare.gov. Accessed May 8, 2020. https://www.
healthcare.gov/how-plans-set-your-premiums/
24 Woerner, Stefan and Daniel J. Egger. “Quantum risk analysis.”npj Quantum Information. February 8, 2019. https://www.
nature.com/articles/s41534-019-0130-6.pdf
25 “The Challenge of Health Care Fraud.” National Health Care
Anti-Fraud Association. Accessed May 8, 2020. https://www.
nhcaa.org/resources/health-care-anti-fraud-resources/
the-challenge-of-health-care-fraud/
26 Obodoekwe, Nnaemeka and Dustin Terence van der Haar.
“A Critical Analysis of the Application of Data Mining Methods
to Detect Healthcare Claim Fraud in the Medical Billing
Process.” Ubiquitous Networking. November 3, 2018. https://link.springer.com/chapter/10.1007/978-3-030-02849-7_29
27 Yndurain, Elena, Stefan Woerner and Daniel J. Egger.
“Exploring quantum computing use cases for financial
services.” IBM Institute for Business Value. September 2019.
https://www.ibm.com/thought-leadership/institute-businessvalue/report/exploring-quantum-financial
28 “Trump Administration Announces Historic Price
Transparency Requirements to Increase Competition andLower Healthcare Costs for All Americans.” US Department of
Health and Human Services. November 15, 2019. https://www.hhs.gov/about/news/2019/11/15/trumpadministration-announces-historic-price-transparency-andlower-healthcare-costs-for-all-americans.html
29 Kerenidis, Iordanis and Anupam Prakash. “Quantum gradientdescent for linear systems and least squares.” PhysicalReview A. May 28, 2019. https://arxiv.org/pdf/1704.04992.
pdf
30 “Health Care Company Anthem Joins NC State’s IBM Q Hub.”
NC State University News. February 10, 2020. https://news.
ncsu.edu/2020/02/health-care-anthem-joins-q-hub/
31 “Real quantum computers. Right at your fingertips.” IBM.
https://quantum-computing.ibm.com/
32 Sutor, Robert, Scott Crowder and Frederik Flöther. “Building
your quantum capability: The case for joining an “ecosystem.”
IBM Institute for Business Value. January 2019. https://www.
ibm.com/thought-leadership/institute-business-value/report/quantumeco

9About Expert Insights
Expert Insights represent the opinions of thought
leaders on newsworthy business and related technology
topics. They are based upon conversations with leading
subject matter experts from around the globe. For more
information, contact the IBM Institute for Business Valueat iibv@us.ibm.com.

© Copyright IBM Corporation 2020
IBM Corporation
New Orchard Road
Armonk, NY 10504
Produced in the United States of America
June 2020
IBM, the IBM logo, and ibm.com are trademarks ofInternational Business Machines Corp., registered in many
jurisdictions worldwide. Other product and service names
might be trademarks of IBM or other companies. A current
list of IBM trademarks is available on the web at “Copyrightand trademark information” at www.ibm.com/legal/
copytrade.shtml.
This document is current as of the initial date of publicationand may be changed by IBM at any time. Not all offerings
are available in every country in which IBM operates.
THE INFORMATION IN THIS DOCUMENT IS PROVIDED“AS IS” WITHOUT ANY WARRANTY, EXPRESS OR IMPLIED,INCLUDING WITHOUT ANY WARRANTIES OF
MERCHANTABILITY, FITNESS FOR A PARTICULARPURPOSE AND ANY WARRANTY OR CONDITION OF NONINFRINGEMENT. IBM products are warranted according to
the terms and conditions of the agreements under which
they are provided.
This report is intended for general guidance only. It is notintended to be a substitute for detailed research or the
exercise of professional judgment. IBM shall not be
responsible for any loss whatsoever sustained by anyorganization or person who relies on this publication.
The data used in this report may be derived from third-party
sources and IBM does not independently verify, validate
or audit such data. The results from the use of such data
are provided on an “as is” basis and IBM makes norepresentations or warranties, express or implied.

91032591USEN-00