Quantum Computing’s Transformational Potential. Is This The Next Big Thing?

Current state and potential of quantum computing technology, including underlying principles, recent advances, and applications.

Quantum computing is an emergent computational paradigm that harnesses inherent quantum mechanical phenomena to manipulate and process information. In contrast to classical binary bits employed in conventional digital computers, quantum computing uses quantum bits or ‘qubits’ as the basic units of information. Qubits differ fundamentally from classical bits in that they can exist in a superposition of 0 and 1 states before observation, enabling massive parallelism during computation [1].

Additionally, quantum entanglement permits correlating the states of distantly separated qubits that are not classically connected, allowing more complex computation through instantaneous state communication [2]. By judiciously exploiting these quantum effects, quantum algorithms have demonstrated superiority over classical counterparts for important problems including integer factorization [3], unstructured database search [4], and quantum simulation [5].

Hence, quantum computing promises disruptive advances in areas ranging from cryptography to quantum chemistry. Realizing this immense potential poses formidable challenges including fragile quantum coherence, exponential state space growth, and error correction [1,6].

Nevertheless, steady experimental progress in controllably manipulating quantum systems has enabled small demonstrations of quantum speedups known as ‘quantum supremacy’, and commercial interest is burgeoning worldwide [7–9].

This ScienceShot reviews the fundamentals of how quantum computers function, surveys cutting-edge advances in qubit technologies, summarizes leading quantum algorithms, examines prospective applications, and contrasts strengths versus limitations against conventional classical computing.


Superposition and entanglement are pivotal phenomena enabling quantum speedups. Unlike classical bits occupying discrete 0 or 1 states, the quantum uncertainty principle allows qubits to exist simultaneously in ‘superposition’ linear combinations of basis states prior to measurement [1]. For example, qubits may occupy uncertain states described as:

|ψ⟩ = α|0⟩ + β|1⟩

Here, α and β are probability amplitudes determining likelihoods of measuring 0 or 1 respectively from the superposition. Appropriately manipulating groups of qubits leverages massive parallelism to evaluate computations over many basis input states concurrently. However, upon final readout, the quantum state irreversibly collapses to a single discrete result according to the probability distribution [1].

Entanglement is another critical quantum effect that enables instantaneous communication between qubits without direct physical interaction [2]. Specifically, initializing a group of qubits in an entangled state produces strong correlations between them regardless of physical separation. Measuring one qubit then definitively determines the states of all others in the entangled group.

Thereby, entangled states permit long-range qubit communication crucial for elaborate quantum computation. Carefully choreographing cycles of entanglement, manipulation, and measurement implemented through quantum circuits consisting of elemental quantum logic gates empowers quantum algorithms to outperform their classical counterparts for specialized problems [10].


Integer factorization via Shor’s landmark quantum algorithm represents one provocative demonstration of quantum computational supremacy with valuable real-world implications in cryptography [3]. Efficiently factoring large numbers into primes is believed classically difficult, yet Shor discovered a quantum approach using specially constructed interference effects that can find factors exponentially faster than classical methods. Recently, researchers reported successfully factoring the number 21 on an ion trap quantum computer, portending more dramatic cryptographic demonstrations as qubits scale-up [11].

Quantum simulation is another promising domain for quantum computational advantage [5]. Quantum systems are ordinarily so complex that even supercomputers struggle to simulate them. However, tailored quantum hardware may efficiently replicate other quantum systems’ dynamics through natural analogue processes.

Recently, superconducting and trapped ion quantum platforms have simulated small molecular and lattice spin systems unfeasible classically [12,13], heralding prospective applications in materials science, quantum chemistry, and fundamental physics.


Besides demonstrating quantum supremacy for specialized problems, researchers worldwide are actively developing the fundamental hardware components and architecture to scale up the number of reliable qubits.

Currently, superconducting, ion trap, and photonics platforms are among the most advanced approaches to constructing quantum processors [6]. Superconducting quantum computers from firms including IBM, Google, and Rigetti couple commercial semiconductor manufacturing techniques with cryogenically cooled superconducting materials exhibiting quantum behaviour at macroscopic scales amenable to qubit registers [7].


Trapped atomic ion approaches leverage electromagnetic fields to isolate, control, and entangle chains of atomic ions acting as qubits [8]. Photonic methods employ nanophotonic chips to manipulate quantum information encoded in single photons [9]. Each qubit modality offers characteristic strengths and weaknesses regarding qubit connectivity, control, and coherence necessitating customized architectures and tradeoffs.


Ongoing challenges pervading quantum information hardware include fragile qubit coherence times, engineering scalability of qubit arrays, and operational errors [6]. Qubits remain viable for computation only while quantum coherence persists before decoherence to classical states. Lengthening baseline qubit coherence times then permits more complex algorithmic sequences. Therefore materials enhancements and finely tuned control electronics to reduce environmental noise promote greater quantum information storage.

Translating a few qubit demonstrations into expansive qubit grids matching classical computer memories requires advances in precision manufacturing, chip architecture, and microelectronics coordination. Finally, actively detecting and correcting operational errors via redundancy, entanglement, and software protocols enhances computation accuracy.

Development efforts are also advancing higher-level quantum computing software stacks and applications [14]. Leading full-stack commercial efforts provide cloud-based access to early quantum hardware accompanied by structured programming frameworks easing algorithm design. Code packages tailored for mathematics, machine learning, chemistry, and optimization are emerging.

Fledgling quantum machine learning algorithms are being tested against classical versions, probing potential future advantages. Specialized quantum software development kits also enable programmers to start integrating quantum subroutines into traditional programs to exploit possible speedups. Compatibility representations translating conventional algorithms into quantum circuits aid in bridging classical and quantum programming models during this early hybrid period. Ultimately seamless integration of robust quantum hardware matched with practical software and specialized applications will precipitate mainstream quantum computing.

Quantum Computing at CERN

At CERN, scientists are leveraging quantum computing to tackle a wide range of challenges. One significant area of focus is the simulation of particle interactions. Quantum simulations offer a unique advantage in modelling complex quantum systems, enabling researchers to gain deeper insights into the behaviour of particles and their interactions. This aids in the understanding of fundamental properties of matter, such as the behaviour of subatomic particles and the dynamics of the early universe.

With the exponential growth of data in particle physics experiments, traditional computing approaches face limitations in handling and extracting meaningful insights from massive datasets. Quantum computing offers the promise of enhanced data processing capabilities, enabling researchers to uncover hidden patterns, identify correlations, and extract valuable information from complex datasets more efficiently.

Collaborations between CERN and leading quantum computing researchers and institutions are crucial for driving progress in the field. CERN’s Quantum Technology Initiative fosters partnerships and collaborations with academia and industry to exchange knowledge, share expertise, and accelerate the development and application of quantum technologies.

These collaborations provide a platform for exploring innovative quantum computing architectures, developing quantum algorithms tailored for particle physics research, and addressing the unique challenges faced in the domain.

Moreover, CERN’s expertise in particle detection and instrumentation plays a vital role in the development of quantum computing hardware. The organization’s deep understanding of precision measurement and control systems contributes to advancements in quantum hardware technologies, such as improving qubit stability, reducing decoherence, and enhancing the overall reliability and scalability of quantum computing platforms.


Benchmarking quantum computing performance to precisely map current capabilities against classical supercomputers remains an open challenge [15].

While strict quantum supremacy is theoretically proven for contrived algorithmic examples, demonstrating unambiguous practical speedup over classical algorithms on useful problems is more nuanced without formal complexity-proof methodologies [15]. Runtime depended greatly on supercomputer optimizations versus uncontrolled implementation aspects of emerging quantum platforms. Finally, limited qubit counts restrict demonstration problems to trivial toy problem complexities unrepresentative of production scales.

If scaling challenges are surmounted, quantum computing could tremendously accelerate progress in diverse scientific and industrial fields over the coming decades. Chemistry simulation promises exact modelling of molecular interactions supporting drug discovery and materials innovation [5]. Optimization and machine learning could assimilate patterns within massive datasets more quickly to amplify decision support [14]. Secure communications may be enabled by quantum key distribution protocols [16]. Financial risk analysis could precisely weigh portfolios through quantum algorithms [17]. Particle physics may reconstruct elusive cosmic dynamics with sharper quantum simulations [18]. Across these applications, correctly mapping problem structures onto quantum frameworks will be essential to harness theoretically predicted speedups.

Ultimately general-purpose quantum computing power promises to expand the computational complexity class solvable in practical times. Using conventional transistor-based hardware governed purely by classical physics, humanity has steadily progressed computing from basic calculators to today’s ubiquitous mobile internet devices over decades of exponential growth according to Moore’s law [19].

However, current silicon chip fabrication technology is approaching hard limits on miniaturization and sequential processing gains. By shifting to direct quantum-level information manipulation, quantum computers could sustain computational development beyond foreseeable classical limits.

Thereby quantum computing may unlock otherwise practically unsolvable advanced science questions and simulation complexity [20]. The future potential partially captured in this review intrigues significant investment from governments and corporations worldwide as quantum technologies transition advanced research into early commercial products over the next decade.

If historic computing growth rates persist, quantum computers available on the global cloud may become high-performance coprocessors for specialized modelling, problem-solving, security and optimization tasks by around 2030 [21].


In conclusion, quantum computing is an extremely promising computational model exploiting quantum physics phenomena to accelerate information processing for difficult problems.

Substantial further technological progress bridging academic research and commercial products is vital to ultimately unlock quantum computing’s prospective societal benefits. But encouraging experimental results and intense global efforts inspire optimism that humanity may be poised to cross a quantum computational threshold, entering an era of new computing capabilities transcending classical limits much as transistors once supplanted vacuum tubes.


  1. Nielsen, M.A. & Chuang, I. Quantum computation and quantum information. (Cambridge University Press, 2010).
  2. Bell, J. S. On the Einstein Podolsky Rosen paradox. Physics Physique Fizika 1, 195–200 (1964).
  3. Shor, P. W. Algorithms for quantum computation: discrete logarithms and factoring. Proceedings. 35th Annual Symposium on Foundations of Computer Science, SFCS ’94 doi:10.1109/sfcs.1994.365700 (1994).
  4. Grover, L. K. A fast quantum mechanical algorithm for database search. Proceedings of the Twenty-Eighth Annual ACM Symposium on Theory of Computing, STOC ’96. doi:10.1145/237814.237866 (1996).
  5. Feynman, R. P. Simulating physics with computers. Int. J. Theor. Phys. 21, 467–488 (1982).
  6. Preskill, J. Quantum Computing in the NISQ era and beyond. Quantum 2, 79 (2018).
  7. Arute, F. et al. Quantum supremacy using a programmable superconducting processor. Nature 574, 505–510 (2019).
  8. Bruzewicz, C. D., Chiaverini, J., McConnell, R. & Sage, J. M. Trapped-ion quantum computing: Progress and challenges. Applied Physics Reviews 6, 021314 (2019).
  9. Qiang, X. et al. Large-scale silicon quantum photonics implementing arbitrary two-qubit processing. Nat Photonics 12, 534–539 (2018).
  10. Deutsch, D. Quantum computational networks. Proceedings of the Royal Society of London. A. Mathematical and Physical Sciences 425, 73–90 (1989).
  11. Monz, T. et al. Realization of a scalable Shor algorithm. Science 351, 1068–1070 (2016).
  12. Arute, F. et al. Quantum approximate optimization of non-planar graph problems on a planar superconducting processor. arXiv:2004.04197 [quant-ph] (2020).
  13. Nam, Y. et al. Ground-state energy estimation of the water molecule on a trapped ion quantum computer. npj Quantum Information 6, 33 (2020).
  14. Ciliberto, C. et al. Quantum machine learning: a classical perspective. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 474, 20170551 (2018).
  15. Harrow, A. W. & Montanaro, A. Quantum computational supremacy. Nature 549, 203–209 (2017).
  16. Pirandola, S. et al. Advances in quantum cryptography. Adv. Opt. Photon. 12, 1012–1236 (2020).
  17. Orús, R. et al. Quantum computing for finance: overview and prospects. Reviews in Physics 4, 100028 (2019).
  18. Jordan, S. P. et al. Quantum algorithm implementations for beginners. Contemporary Physics 59, 366-404 (2018).
  19. Moore, G.E. Cramming more components onto integrated circuits. Proceedings of the IEEE 86, 82-85 (1998).
  20. Aaronson, S. Guest column: NP-complete problems and physical reality. ACM Sigact News 36, 30–52 (2005).
  21. Mohseni, M. et al. Commercialize quantum technologies in five years. Nature 543, 171–174 (2017).

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