Quantum Computing: Solutions and Challenges

Quantum computing is emerging as a technological revolution capable of transforming diverse fields, from medicine to artificial intelligence. In our previous article, “What is Quantum Computing?”, we explored the basic concepts and transformative potential of this technology. We defined quantum computing as a branch of computer science that uses the principles of quantum physics to process information in a radically different way than classical computing.

Since its conceptualization by Paul Benioff in 1981, quantum computing has advanced significantly. Scientists such as Richard Feynman and David Deutsch have made crucial contributions to the field. Despite being in its early stages of development, the potential of this technology to revolutionize various sectors is immense.

The aim of this article is to delve into the specific problems that quantum computing can solve and the challenges facing its implementation. We will address the solutions that this technology promises, such as industrial process optimization and materials simulation. We will also discuss the technical, scientific and societal challenges that still need to be overcome. We will explore how these challenges can be addressed through innovations in quantum error correction, the development of more stable qubits, and the design of optimized algorithms. In addition, we will discuss the social and economic impact that the adoption of quantum computing could have, underscoring the importance of continued collaborative research in this emerging field.

Quantum algorithms take advantage of the unique properties of qubits, such as superposition and entanglement, to perform computations that would be impossible or extremely inefficient on traditional computers.

Shor’s algorithm:
Shor’s algorithm, proposed by Peter Shor in 1994 (Shor, P. W. (1994). Algorithms for Quantum Computation: Discrete Logarithms and Factoring. Proceedings of the 35th Annual Symposium on Foundations of Computer Science (FOCS), 124-134. Available at arXiv), this algorithm is one of the most outstanding achievements in quantum computing. It allows factoring integers in polynomial time, a task that is exponentially difficult for classical computers. Efficient factorization has crucial implications for cryptography, especially for encryption systems such as RSA(public key cryptographic system), which rely on the difficulty of factoring large prime numbers, Martin Ekerå explained how such factorization would be done (Gidney, C., & Ekerå, M. (2019). How to factor 2048 bit RSA integers in 8 hours using 20 million noisy qubits . arXiv preprint arXiv:1905.09749. Available at arXiv).
One of the major impacts and issues is that if implemented on a large scale, Shor’s algorithm could break current cryptographic systems, exposing sensitive data and requiring the adoption of post-quantum cryptography. This has spurred research into quantum attack-resistant cryptography.
One of the biggest impacts and issues is that if implemented on a large scale, Shor’s algorithm could break current cryptographic systems, exposing sensitive data and requiring the adoption of post-quantum cryptography. This has spurred research into quantum attack-resistant cryptography.

Grover’s algorithm:
Developed by Lov Grover in 1996, this algorithm provides a way to search an unordered database in a quadratically faster time than classical algorithms. Whereas a classical algorithm would require O(N) operations to search an item in a database of N entries, Grover’s algorithm can do so in approximately O(√N) operations. In addition to speeding up database searches, Grover’s algorithm offers significant improvements in NP-complete problem solving and function optimization, with applications in areas such as artificial intelligence and operations research.

The ability of quantum computers to simulate complex molecular systems with high accuracy promises to revolutionize medicine and pharmacology. These advances can have a direct impact on medical science, facilitating the investigation of complex molecular structures, enabling the accurate simulation of these structures, and developing new drugs and accelerating the creation of personalized drugs and treatments. Below, we will explore three key areas in the healthcare sector where quantum computing will have a significant impact.

1. Pharmacological Treatments

In biotechnology, quantum computing is driving significant advances. One notable example is the collaboration between Accenture, quantum software company 1QBit and biotech Biogen. Together, these companies are developing the first quantum application of medical solutions for diseases such as multiple sclerosis, Alzheimer’s and Parkinson’s disease.
In addition, custom drug design is being transformed. Currently, the creation of treatments is a lengthy and expensive process that can take many years to complete due to the need for numerous laboratory experiments. Quantum computing makes it possible to simulate the effect of different chemical compounds on organisms at the molecular level, significantly reducing the costs and time associated with the development of new pharmacs.

2. Diagnosis

Quantum computing is also revolutionizing disease diagnosis and monitoring. Cancer patients, for example, can wait months to find out if chemotherapy is working. Thanks to advances in quantum computing, however, this is changing. Researchers at Case Western Reserve University partner with Microsoft in new quantum computing health project (Home of Healthcare News), have developed a technique that can detect the effectiveness of chemotherapy after a single dose.
The technique known as MRF (Magnetic Resonance Fingerprinting) compares tissues from an MRI scan with many others already stored, thus making it possible to estimate diagnoses automatically and accurately. This speeds up and refines the diagnosis of diseases without the need for invasive procedures, and makes it possible to know the effectiveness of a treatment in a matter of minutes or days.

3. Data Management

Quantum computing also significantly improves artificial intelligence and big data, applied to the field of healthcare. This technology makes it possible to record, sort and analyze massive amounts of complex data, identifying patterns that can be crucial for the diagnosis and treatment of diseases.
In addition, quantum computing can optimize the management of information and data in global systems, identifying and selecting the most efficient processes. This facilitates and streamlines many healthcare processes, from logistics organization to healthcare data management, providing more efficient and secure management.

In conclusion, quantum computing promises to radically transform medicine and pharmacology, offering innovative solutions for treatment design, diagnosis and health data management. This technology not only speeds up and makes the development of new drugs cheaper, but also improves accuracy and efficiency in the diagnosis and management of diseases.

Materials Science

Quantum computing promises to revolutionize materials science thanks to its ability to simulate the behavior of materials at the quantum level with unprecedented accuracy. This technology enables the creation and study of new materials with specific properties for advanced technological applications, something that is extremely difficult to achieve with classical computers due to the complexity of interactions at the atomic level.

Simulation of New Materials at the Atomic Level

Quantum computers can simulate complex quantum systems efficiently, which is essential for research and development of new materials. These quantum systems include molecules and solids where electronic interactions play a crucial role in determining material properties.
For example, quantum simulation can help design new superconducting materials at room temperature, which would have a huge impact on energy transmission and electronics.
The potential impact includes the development of materials with novel properties for various technological and construction applications. Below are some real-world examples and case studies that illustrate this impact:

1. Superconducting Materials:
   Recent research is using quantum algorithms to study high-temperature superconductors, which could enable the transmission of electricity without energy loss. This could revolutionize the transport of electrical energy, significantly reducing costs and improving energy efficiency globally.
   
2. Solar Cells
   Quantum simulation is being used to investigate new materials for next-generation solar cells. These materials could significantly increase the efficiency of converting solar energy into electricity, contributing to more efficient and sustainable energy solutions.
   
3. Materials Chemistry:
   Companies such as IBM Research y Google’s Quantum Processor Sycamore and Material Properties, are working on quantum algorithms that can model complex chemical reactions more accurately than classical methods. This is crucial for the development of new catalysts and materials that can be used in more efficient and less polluting industrial processes.
   
4. Nanomaterials:
   Quantum computing is also being explored to design nanomaterials with specific properties, such as increased strength, flexibility or conductivity. These nanomaterials have potential applications in the fabrication of electronic devices, sensors and advanced building materials.

Examples and Case Studies

1. IBM and the Simulation of Chemical Reactions:
   IBM has demonstrated the use of its quantum processor to simulate the hydrogen molecule, a crucial step toward simulating more complex chemical reactions. This breakthrough could accelerate the discovery of new materials and pharmacs.
   
2. Google and Quantum Materials:
   Google has used its Sycamore quantum computer to explore properties of quantum materials that are difficult to simulate with classical computers. This type of research is essential for the development of innovative materials with applications in quantum computing and communication.

Optimization

This breakthrough has significant applications in a variety of fields, including logistics and finance.

Solving complex optimization problems in logistics, finance and more, along with improved efficiency in route planning, portfolio management, etc.

Logistics

In logistics, quantum optimization can significantly improve route planning and supply chain management. Quantum computers can handle the enormous number of variables and constraints that affect logistics more efficiently. A prime example is the Traveling Salesman Problem, where a quantum computer can explore multiple routes simultaneously to find the most optimal one, reducing transportation time and costs.
A real case of application is the use of quantum algorithms by companies such as Volkswagen, which has been working on optimizing bus routes in Lisbon using quantum computing to minimize travel times and improve operational efficiency. (Volkswagen Group)

Finances

In the financial sector, quantum optimization can transform portfolio management, risk analysis and fraud detection. Quantum algorithms enable more accurate and faster evaluation of asset mixes, helping to maximize returns and minimize risks. In addition, they can analyze large volumes of financial data to identify patterns and trends more quickly.
For example, financial institutions such as JP Morgan have begun to explore how quantum algorithms can improve the efficiency and accuracy of risk management and portfolio optimization models.

Other Fields

Beyond logistics and finance, quantum optimization has potential applications in areas such as energy and manufacturing. In the energy industry, it can help optimize energy distribution and consumption, while in manufacturing it can improve production scheduling and inventory management, reducing costs and increasing efficiency.
D-Wave has developed quantum optimization solutions applied to real-world problems, including the optimization of telecommunications networks and the improvement of industrial processes.