introduction
in this exclusive interview, we engage with two distinguished research associates of the Imperial College London, Dr. Simon Williams, a leading expert in the application of quantum computing in theoretical particle physics, and Dr. Ioannis Xiotidis, renowned for his expertise in experimental particle physics. together, they delve into the intricate world of quantum computing, sharing insights on its current status and future potential.
Dr Williams on Quantum Computing Fundamentals
Dr. Williams initiates the discussion by elucidating the core principles of quantum computing. he explains that quantum computing represents a paradigm shift grounded in the principles of quantum mechanics. quantum bits, or “qubits,” unlike classical bits, can exist in a state of superposition, allowing them to simultaneously embody both 0 and 1.
Dr. Williams: “one of the key advantages of quantum computing is the concept of superposition. in classical computing, a bit can either be 0 or 1, but in quantum computing, a qubit can exist in a mixture of both states simultaneously. this enables quantum computers to explore a vast solution space with non-trivial correlations between qubits, which is a significant advantage for certain types of problems.”
however, he also underscores the challenges posed by noise in quantum computing. noise arising from factors such as temperature fluctuations, electromagnetic interference and defects in the device can introduce errors into quantum computations. these errors are problematic when developing quantum algorithms, as they can disrupt the delicate quantum states that qubits rely on.
Dr. Williams: “while computing using quantum mechanical objects can lead to very powerful algorithms, it comes with its own set of challenges.. noise, or unwanted interactions with the environment, can introduce errors into our quantum computations. this noise can cause qubits to lose their coherence and ultimately lead to unreliable results.”
Dr Xiotidis on practical applications of quantum computing
Dr. Xiotidis delves into the realm of practical applications in the area of experimental high energy physics, emphasising the burgeoning possibilities in this innovative field. quantum computing, he asserts, is not confined to abstract theoretical discussions but has already begun making substantial contributions in various domains.
Dr. Xiotidis: “quantum computing is not just a theoretical concept; it’s very much a practical endeavour with tangible applications. one of the exciting frontiers is quantum machine learning, where we combine quantum computing algorithms and machine learning techniques. the difference mostly with classical machine learning is that you can introduce much more information into an neural network that is being designed for quantum computers as the qubit states are entangled in contrast to classical bit streams.“
Dr williams: “quantum machine learning utilises the unique behaviour of quantum systems to solve certain computational problems more efficiently than classical computers, for example complex optimisation problems. in some exceptional cases, such as quantum reservoir computing, noise can even become an asset, making quantum machine learning in reach of current hardware.”
he highlights that quantum machine learning leverages the inherent quantum properties to perform computations more efficiently than classical counterparts. the success of quantum machine learning, and its robustness, allows for practical and proof-of-principle algorithms to be developed and run on real quantum computers today.
Dr. Xiotidis: “in quantum machine learning, we see a unique synergy between quantum computing and noisy environments. classical computers don’t struggle from noise, they are pretty deterministic and resilient to their environments. i’d say that quantum computers can excel in scenarios where the computational strength needed to solve a problem is excessive. this opens up exciting opportunities for industries dealing with real-world, noisy data, such as healthcare, finance, and logistics.”
moreover, Dr. Xiotidis points out that while quantum computing is still evolving, practical applications are already in progress. quantum cloud services, offered by companies like ibm, provide access to quantum devices, allowing researchers, businesses, and developers to experiment with quantum algorithms and explore their potential advantages.
Dr. Xiotidis: “quantum cloud services are a significant step in making quantum computing more easily available to the public. they enable users to access quantum hardware remotely, removing the need for expensive infrastructure investments.the collaboration between research and larger technology companies, such as ibm, is of great benefit to the development and progress of quantum computing applications, both in particle physics and real world applications. ibm is one of the leading companies in qubit based quantum computers, and grants public access to some of their devices and simulators. these can be used to perform proof-of-principle studies that can then be used as seeds into further developments. for instance, in our research we are solving a pattern matching problem applied to tracks of sub-atomic particles traversing a particle detector. we do this by identifying a set of simulated track patterns in real data to allow for efficient measurements of key particle physics quantities. such measurements require enormous computational power from classical devices due to the exponential increase in the pattern complexity. we hope that quantum computers will be able to deal with this exponential increase more efficiently and provide a quantum advantage.”
in conclusion, dr. xiotidis underscores that quantum computing is not a mere theoretical curiosity but a transformative technology with real-world applications. quantum machine learning and quantum cloud services exemplify how quantum computing is poised to address practical challenges and drive innovation across various sectors.
Challenge of fault tolerance
Dr. Williams underscores the critical importance of fault tolerance in quantum computing and highlights the primary challenges associated with achieving it.
Dr. Williams: “the development of error-resilient algorithms is paramount in the absence of foolproof error correction. while hardware improvements are anticipated, our focus must be on designing algorithms and architectures that mitigate noise and errors.”
he stresses the ongoing research into quantum error correction techniques intended to mitigate errors in quantum devices. simultaneously, he emphasises the need for developing algorithms
capable of withstanding the current levels of noise and errors inherent in quantum devices. addressing these challenges represents a pivotal step in realising practical quantum algorithms.
The role of organizations like d-wave systems
The discussion extends to the role of organizations such as D-Wave Systems and IBM in advancing the practical development and commercialization of quantum computing.
Dr. Xiotidis: “Organizations like D-Wave play a crucial role in selecting and collaborating on feasible quantum computing approaches. D-Wave specializes in quantum annealing and offers quantum annealers equipped with numerous qubits.”
Dr. Williams: “Collaboration between academia and industry is pivotal. IBM’s ‘100 by 100 challenge’ invites researchers to formulate quantum algorithms that can effectively operate on their 100 gate operations on 100 qubits. Such collaborations accelerate the realisation of practical implementations of quantum algorithms.”
The future of quantum computing:
both research associates conclude the interview by offering insights into the future of quantum computing.
Dr. Williams: “quantum computing is still in its infancy, despite the current peak of hype surrounding it. with sustained research and the pressing need for simulating complex quantum field theories, it is poised to become an integral component of scientific computing in the future.”
Dr. Xiotidis: “quantum computing’s trajectory parallels that of other groundbreaking technologies. with sustained research, collaboration, and advancements in both hardware and algorithms, it is poised to become an integral component of computing in the future.”
