Leveraging quantum computing for Healthcare

What is Quantum Computing?

Imagine you have a super powerful computer that can solve really hard problems much faster than the computers we use today. This super computer is called a quantum computer.

In a regular computer, the basic unit of information is called a bit, which can be either a 0 or a 1. Think of it like a light switch that can be either off (0) or on (1). But in a quantum computer, the basic unit of information is called a qubit. A qubit can be both 0 and 1 at the same time, thanks to a special property called superposition. It's like having a light switch that can be both off and on at the same time!

Another cool thing about qubits is that they can be entangled. This means that the state of one qubit can depend on the state of another qubit, no matter how far apart they are. It's like having two magic dice that always show the same number when you roll them, even if one is on Earth and the other is on Mars!

These properties of qubits allow quantum computers to process a huge amount of information simultaneously, making them incredibly powerful for certain tasks, like solving complex mathematical problems, simulating molecules for drug discovery, and even breaking encryption codes.

So, in simple terms, quantum computing is a new way of making computers that use the strange and amazing properties of quantum mechanics to solve problems much faster than regular computers can.

Understanding Quantum algorithms

One cornerstone of this transformation are quantum algorithms, whose design enable a quantum computer to execute calculations more efficiently than classical computers. Quantum algorithms are specialized algorithms designed to run on quantum computers, leveraging the unique properties of quantum mechanics such as superposition, entanglement, and interference. Quantum algorithms are typically described using the quantum circuit model, where a quantum circuit consists of quantum gates acting on qubits. Here are some key points about quantum algorithms:

Quantum algorithms can solve certain problems much faster than classical algorithms. For example, Shor's algorithm can factor large integers exponentially faster than the best-known classical algorithms, which has significant implications for cryptography. Grover's algorithm can search an unsorted database quadratically faster than any classical algorithm. Additionally, quantum algorithms can be used for optimization problems, solving linear equations, and simulating quantum systems.

We need to identify quantum algorithms that are related to their business cases, and to evaluate whether these could be implementable in the Noisy Intermediate- Scale Quantum (NISQ) era. The algorithm selection process of course prioritized those algorithms that are highly recognized within the quantum-computing community, ensuring the inclusion of foundational algorithms that have significantly contributed to the advancement of the field over the years.

A Large-Scale Quantum (LSQ) computer is a theoretical type of quantum computer that can perform a wide range of tasks and applications beyond those that can be performed by current or near-term quantum computers. It is typically characterized by having a large number of qubits , sufficiently many so that effective error correction is possible.

The quantum devices that are available commercially or in research laboratories today are more limited and belong, for now at least, to the so-called Noisy Intermediate-Scale Quantum (NISQ) regime. NISQ devices are a necessary step on the path towards LSQ computers, but in principle they could already enable quantum computational advantages, at least in specialized problems that have been explicitly designed to demonstrate a separation between classical and quantum performances. The original definition of NISQ devices is that they contain 50 to 100 qubits and that they are sensitive to the noise induced by their environment, the so- called quantum decoherence . They suffer from errors, and have insufficiently many qubits to perform effective error correction.

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Quantum Computing in Healthcare

Quantum computing in healthcare has the potential to revolutionize the way we diagnose and treat diseases. By harnessing the power of quantum computers, we can perform calculations that are impossible with classical computers. This could lead to breakthroughs in areas like drug development, personalized medicine, and disease detection. Quantum computing in healthcare is still in its early stages, but there is great potential for it to transform healthcare.

From quantum sensors to quantum computing, this range of cutting-edge technology promises advancements in healthcare domain. Quantum physics and quantum technology utilize various methods to play a role in the healthcare industry, including nanotechnology, artificial intelligence, and quantum computing.

The reason that quantum technology seems to fit so well into healthcare domain is that many biological processes in human body function similar to quantum interactions.? This makes utilizing quantum technologies a good fit.

Quantum Sensors based solutions

MacQSimal, a quantum technology company is working to replace MEG (Magneto Encephalo Graphy) machines which are bulky and take a lot of money and energy to use. Instead, MacQSimal is proposing a helmet of quantum sensors to create more accurate brain scans.

MEG is a non-invasive technique for investigating human brain activity.? It allows the measurement of ongoing brain activity on a millisecond-by-millisecond basis, and it shows where in the brain activity is produced. At the cellular level, individual neurons in the brain have electrochemical properties that result in the flow of electrically charged ions through a cell. Electromagnetic fields are generated by the net effect of this slow ionic current flow. While the magnitude of fields associated with an individual neuron is negligible, the effect of multiple neurons (for example, 50,000 – 100,000) excited together in a specific area generates a measurable magnetic field outside the head. These neuromagnetic signals generated by the brain are extremely small—a billionth of the strength of the earth’s magnetic field. Therefore, MEG scanners require superconducting sensors (SQUID, superconducting quantum interference device). The SQUID sensors are bathed in a large liquid helium cooling unit at approximately -269 degrees C. Due to low impedance at this temperature, the SQUID device can detect and amplify magnetic fields generated by neurons a few centimetres away from the sensors.

Other companies like? MetaboliQs are using quantum sensors manufactured with diamonds to work on replacing MRI cooling systems. Other quantum sensors are predicted to make disease detection faster and more accurate, being able to diagnose cancer, Alzheimer’s, or dementia faster. All of these new tools would reduce the cost of healthcare and vastly improve the quality of life for thousands of patients.

Quantum in Drug Discovery

Quantum computing could be used to?optimize drug design & drug testing processes. ?Quantum computers can also perform simulations and could compute accurate simulations of a new drug on virtual human subjects, only within a few hours. This would save drug companies money and time, as well as remove the number of test subjects for a study. This process has already been tried by a company InSilicoMedicine?, which was able to develop a new drug candidate in 46 days using a simulated algorithm. Using quantum computers can speed up the drug design and test process.

Precision Genomic Medicine

Quantum computing’s unprecedented speed and accuracy enables the practice of precision genomic medicine, where treatments are tailored to an individual’s unique genetic makeup. Clinicians can better predict disease risks, identify genetic mutations, and personalize treatment plans, leading to more effective therapies and improved patient outcomes. This quicker genetic analysis could lead to, better genetic screenings for genetic diseases and more accurate drug screens.?

Quantum computing have many other uses when it comes to healthcare. For those having radiation therapy, a quantum computer could simulate the least harmful treatment that would best target only the cancerous tissue and not healthy tissues. Quantum computing additionally offers more secure medical data through quantum data encryption. This data encryption could make medical data more secure and protected from hackers or ransomware.?

Advanced Disease Modelling

Quantum computing allows researchers to create sophisticated disease models, offering deeper insights into disease mechanisms and progression. These detailed models can help scientists understand how diseases evolve, identify critical intervention points, and design more targeted treatments. This benefit is particularly crucial in complex diseases like cancer, where understanding the interplay of various factors is essential for developing effective therapies.

Enhanced Medical Imaging

Quantum computing can revolutionize medical imaging by enhancing the quality, resolution, and speed of imaging technologies such as magnetic resonance imaging (MRI) and positron emission tomography (PET). Quantum-enhanced imaging can detect subtle changes in tissues and organs, enabling earlier and more accurate disease detection. This has the potential to save lives by catching diseases like cancer at their earliest and most treatable stages.

Challenges related to Quantum Computing adoption in Healthcare

The nascent nature of quantum computing technology, its high implementation costs, and the profound data security implications present formidable hurdles that must be surmounted for its integration into the healthcare ecosystem.

Quantum Error Correction

Quantum computers are highly susceptible to errors due to factors like qubit decoherence and interference from external factors. Quantum error correction techniques are essential to mitigate these errors. However, implementing such error correction is complex and resource-intensive, often requiring a significant number of additional qubits. This not only increases the computational resources needed but also exacerbates the already high costs associated with quantum computing in healthcare.

Limited Software Ecosystem

Compared to classical computing, quantum computing’s software ecosystem is relatively underdeveloped. Quantum algorithms and software tools are complex and specialized, requiring expertise that is currently in short supply. This limitation makes it challenging for healthcare professionals and researchers to develop and implement quantum solutions effectively. Additionally, quantum programming languages and tools may have a steep learning curve, further impeding adoption.

Ethical and Bias Concerns in Machine Learning

Quantum machine learning, which combines quantum computing and AI, may inadvertently inherit biases from the datasets used for training.

This could result in biased healthcare predictions and decisions, potentially leading to unequal access to healthcare resources and treatments. It is crucial to address ethical concerns and implement stringent fairness and bias mitigation strategies in quantum machine learning models.

Conclusion

As described about the benefits of Quantum Computing in Healthcare and the intricacies encapsulated within the challenges of Quantum Computing in Healthcare, it becomes abundantly clear that the path forward is one of both great promise and profound challenges.

The potential to revolutionize drug discovery, diagnostics, and personalized treatment plans is undeniably enticing, offering the prospect of improved patient outcomes and a brighter future for healthcare.

Nevertheless, the challenges of Quantum Computing in Healthcare, including the intricacies of implementation, substantial financial investments, and data security concerns, must not be underestimated.

The future of quantum computing in healthcare hinges on our ability to surmount these obstacles through collaborative research, stringent regulations, and innovative solutions. It is a journey that necessitates not just technological advancements but also ethical considerations and responsible stewardship of this transformative technology.

The prospects of quantum computing in healthcare remain tantalizingly vast, with the potential to unlock unprecedented advancements in medical science, offering new hope for patients, researchers, and healthcare providers alike.

About the Author

Raghuveeran Sowmyanarayanan is Global Delivery Head for Artificial Intelligence @ Wipro Technologies and has been personally leading very large & complex Enterprise Data Lake & AI/ML implementations and many Gen AI experimentations & PoCs. He can be reached at [email protected]