Quantum Computing in Healthcare

In 1981, Richard Feynman gave a talk at a conference called "The Physics of Computation" which was latter detailed in the book Feynman Lectures on Computation.  Feynman observed that the world - its physics is quantum and if one wanted to simulate that physics, a quantum computer would be the way to do that. He then went on and outlined many of the main concepts of what is still used in the field of quantum computing.

However, quantum computing was out of reach then for the technology of the 1980's and even now 40+ years later we are still in a nascent stage of quantum computing. The running joke for a long time has been that quantum computing (as well as for fusion energy) is that we are always just 10 years away. Then 10 years later we are again just 10 years away. But recently, there have been some announcements that lead me to believe that we could be arriving at a massive inflection point in turning theory into extremely practical applications. For example, IBM just recently announced that through a partnership with the University of Chicago and the University of Tokyo they plan on building a 100,000 qubit computer within 10 years. Google subsidiary SandboxAQ is looking to do molecular simulations of interactions for drug discovery. And startups looking to use quantum computing in drug discovery include Aqemia doing structure based design of drug candidates and Qubit Pharmaceuticals using quantum computing to do simulations of molecules.

Before I get into what I think will be some of the biggest applications, a brief discussion of what are the characteristics of quantum computing that make it special when compared to classical computing is warranted. And by classical computing, it is meant the everyday binary computing of 0's and 1's that power everything we do. 

These 0's and 1's (bits) of classical computing are processed through logical operations, such as AND, OR, and NOT gates, to perform computations. However, in quantum computing, we utilize quantum bits, or qubits, which can exist in a superposition of states.

A qubit can represent both 0 and 1 simultaneously, thanks to the property of superposition. This means that a qubit can be in a state that is a combination of 0 and 1 at the same time. For example, a qubit can be 0 and 1 with a certain probability assigned to each state. This allows quantum computers to perform multiple computations in parallel, exponentially increasing their computational power compared to classical computers.

Furthermore, qubits can also exhibit a property called entanglement. When qubits become entangled, the state of one qubit becomes correlated with the state of another qubit, regardless of the distance between them. This entanglement enables quantum computers to perform operations on multiple qubits simultaneously, leading to the potential for massive computational speedups.

To manipulate qubits and perform computations, quantum computers rely on quantum gates. These gates are analogous to the logical gates in classical computing and allow for operations such as changing the probability distribution of a qubit's states or entangling qubits together.

If you want to dive more into the details of quantum computing, IBM has a great resource that goes through the details of how it works and what's really cool is they show that by using their python-based Qiskit language you can get some real experience in quantum computing using their cloud quantum computers. So you can install it and gets some hands on experience and even upload your code on one of their quantum computers to run.

https://www.ibm.com/topics/quantum-computing

Also, here is a great series again from IBM that explains quantum computing.

"Understanding Quantum Information and Computation"

Quantum computing holds great promise for solving complex problems that are computationally infeasible for classical computers. Certain algorithms, such as Shor's algorithm, can factor large numbers exponentially faster on a quantum computer, posing a potential threat to modern cryptographic systems. Quantum computers also excel in optimization, simulation of quantum systems, and machine learning tasks. Specifically quantum computing could be applied to financial modeling, materials design, and transportation scheduling. 

And as a side note, quantum computing can be applied to natural language processing. In October 2021, Cambridge Quantum Computing (CQC) announced the release of the first Quantum Natural Language Processing (QNLP) toolkit and library. This toolkit can translate sentences into quantum circuits. As powerful as transformers/attention models have become with GPT with companies like OpenAI, there will need to be another breakthrough or multiple breakthroughs to reach sophisticated AGI and quantum computing may be one of the factors to enable an acceleration towards artificial super intelligence (ASI).

With all of these possible applications of quantum computing, some of the more intriguing ones are in healthcare. In March of this year, IBM and Cleveland Clinic announced their partnership of the first on-site quantum computer. A few of the applications will include prediction models for cardiovascular disease, drug discovery, and genetics.

It is in these areas of drug discovery and genetics that I believe hold the greatest promise in healthcare.

Drug Discovery

• Simulation of molecular behavior: Quantum computers can simulate the behavior of molecules at the quantum level with high precision. This capability is crucial for understanding the interactions between drugs and their target molecules, as well as predicting their effectiveness and potential side effects. By providing more accurate and detailed simulations, quantum computers can accelerate the drug discovery process and improve the success rate of drug candidates.
• Drug optimization and molecular design: Quantum computing can aid in optimizing drug molecules and designing new ones. Quantum algorithms can explore vast chemical space to identify compounds with specific properties, such as high potency, selectivity, and bioavailability. This can potentially lead to the discovery of novel drugs or optimization of existing ones, making the drug development process more efficient and cost-effective.
• Quantum machine learning for drug discovery: Quantum machine learning algorithms running on quantum computers can analyze large datasets related to drug targets, molecular structures, and biological interactions. These algorithms can extract valuable insights, identify patterns, and make predictions to guide drug discovery efforts. Quantum machine learning has the potential to improve target identification, lead optimization, and personalized medicine.
• Quantum chemistry calculations: Quantum computers can perform complex quantum chemistry calculations, such as calculating molecular energies, reaction rates, and properties of chemical systems. These calculations are computationally intensive and often intractable for classical computers. By harnessing quantum algorithms, quantum computers can provide more accurate and efficient solutions, enabling researchers to better understand chemical processes and accelerate drug discovery.

Genetics

Considering the large number of base pairs in the human genome, as explained in this paper, an entire genome of a person could be stored in ~34 qubits. And amazingly, doubling the size of the quantum compute could theoretically store the genome of every person on the planet - illustrating the potential of quantum computing is not just its potential speed but its ability to store and process vast amounts of data. This power could revolutionize medicine in genetics in two ways:

• Genomic data analysis: Quantum computing can help analyze vast amounts of genomic data, such as DNA sequences, gene expression profiles, and genetic variations. Quantum algorithms can enhance the efficiency of processing and analyzing this data, enabling researchers to identify genetic patterns, disease markers, and potential drug targets more accurately and quickly.
• Precision medicine and personalized therapies: Quantum computing can aid in the development of personalized medicine approaches by analyzing individual genetic data and matching it with specific drug responses. Quantum algorithms can process and analyze large-scale genomic datasets to identify genetic markers that influence drug efficacy, toxicity, and treatment outcomes. This information can guide the development of tailored therapies based on an individual's genetic profile.

For both drug discovery and genetics, combining quantum computing with artificial intelligence would be completely transformative in finding therapies and cures in healthcare.

So what should you do?

What should you do as an organization or as a technical person looking at the possible quantum landscape? Every company should periodically be evaluating their preparedness and long range opportunities, because even though practical applications may not be here yet, there will be a moment when that will change - and the opportunities could be enormous for companies in the right industries who have positioned themselves. For individuals, there is currently a knowledge shortage and I believe there will be a knowledge shortage for a long time regarding quantum computing as it accelerates. So it is never too early to start learning as much as you can about it.

Summary

Quantum computing has great potential, however it is important not to think of it as a replacement for classical computing. Quantum computing has very specific situations where it should be applied just like the GPU in machine learning has its specific uses. And just like GPUs, the ideal machine will be a marriage between quantum computing and classical computing.

However as great as the promise of quantum computing, it is still in its early stages. And although the announcements from IBM to build a 100,000 qubit computer by the end of the decade and announcements from Google and others are encouraging, many technical challenges remain. Building stable and error-resistant qubits, minimizing environmental interference, and developing error-correcting codes are among the significant hurdles researchers are working to overcome. Despite these challenges, quantum computing has the potential to revolutionize many different industries such as healthcare.