Improving Trial Designs for Heterogeneous Populations

Accurately projecting outcomes for diverse patient populations – from the wealth of genomic, phenotypic and outcomes data available through genome sequencing and electronic health records – holds the potential to transform the effectiveness and efficiency of drug and medical device development.

Yet the computations required for statisticians to explore, understand and interpret these enormous multivariate and often poorly structured data take a prohibitively long time to complete. In many cases, it would take months and often years to transform the raw data into informative, scientifically sound, and statistically significant results using even the most advanced conventional computers.

Quantum computing may change that. In theory, quantum computers are capable of computational power many orders of magnitude beyond today’s conventional computers. Quantum computers promise to be faster and more reliable in correctly identifying patterns and dynamic trends in massive noisy data sets similar to those that would be useful for mapping dose response and other complex therapeutic relationships.

Quantum computing could prove useful in designing clinical trials. For example, statistical methods of trial design that fuse combinatorial and optimal experimental design techniques have been developed to potentially reduce by half the number of sites and patients needed to select the best combination treatment for targeting multiple cancer types and various biomarkers.

That emerging approach was among the advances presented at a quantum computing workshop this spring, co-sponsored by the ICON Innovation Centre with the George Washington University (GWU) Department of Statistics and Lockheed Martin Corporation. Speakers representing GWU, Lockheed, ICON, the National Institute of Standards and Technology, the University of Wisconsin-Madison, and Nokia Bell Labs addressed more than 50 participants from academia, government, and industry.

The workshop featured quantum computing overviews, talks on quantum algorithms and their links with statistics, as well as case studies and a round table discussion. It exemplifies the cross-industry collaborative approach that is moving this potentially revolutionary computing technology from theory to practical reality.

We asked Sergei Leonov, VP of Clinical Trial Methodology at ICON, to reflect on discussions at the conference about how quantum computing is evolving for clinical applications.

When will quantum computing be available for use in clinical trials?

Quantum computing is only beginning to move from the realm of demonstrations to practical application. Medical product development may be one of the earliest likely applications.

However, harnessing the potential power of quantum computing computational power will require significant additional advances in both mathematics and technology. The machine learning algorithms and statistical methods needed are in development, but far from mature.

Significant hardware and software challenges also exist. One is providing a super-cool (no pun intended) environment. Since minor interactions with the external world may change the state of a quantum system, a stable quantum system can only exist at temperatures close to absolute zero on the Kelvin scale (0°K = -273.15°C = 459.67°F) and being isolated from the external world. For instance, the latest generation D-Wave system operates at 15 millikelvins [1]. Given the physical limits involved, developing software and interfaces capable of reliably working with transient quantum models is difficult.

While it will take time and effort to apply quantum computing to clinical development it may be the only practical way to process the masses of data now available. Even partial success would represent a “quantum leap” in computing power and with it development efficiency, so the effort is well worth it. We expect to make major progress in the next couple of years.

What is quantum computing?

Quantum computing is an alternate approach to solving complex mathematical problems, often involving vast amounts of data.

The theoretical power advantage quantum computing holds over conventional computing primarily relates to two quantum mechanical properties – superposition and entanglement. Together these greatly increase both the quantity of data a computer can process, and the ways in which these data can be combined [2].

For conventional computing, the basic unit, the bit, exists in one state at a time, and this state is deterministic, either 0 or 1. Superposition means that the basic unit of quantum computing, known as the qubit, exists in two states at one time, and these states are probabilistic, adding up to 1. Therefore, whereas classical computing is limited to manipulating binary bits in a linear, deterministic stream, quantum computing can manipulate vast data sets simultaneously in a probabilistic ocean. The magnitude of this difference is hinted at by the number of states a quantum computer can represent. Today, the most powerful commercially available quantum computer operates on quantum systems of up to 2,000 qubits, which could exist in a superposition of as many as 22,000, or about 10600 quantum states. For context, it is estimated that there are about 1080 atoms in the known, observable universe.

The second advantage of quantum computing is entanglement, which means individual quantum qubits can interact directly with each other, even at great distances, altering each other’s states simultaneously without intermediate causal connections. By comparison, conventional bits interact only in a linear sequence, changing each other’s state one at a time in an extended chain of binary operations. So, a quantum computer potentially can use computational “shortcuts” not available in conventional computers.


  1. Latest Generation D-Wave System Nielsen, M.A., Chuang, I.L. (2010).
  2. Quantum Computation and Quantum Information: 10th Anniversary Edition. Cambridge University Press.