Richard Feynman’s 1982 talk is widely regarded by experts as a cornerstone in the field of quantum computing. Feynman envisioned a quantum machine that could simulate quantum physics by utilizing the principles of quantum mechanics. He argued that a computer based on quantum mechanics might be necessary to accurately mimic natural phenomena, as Nature itself is fundamentally quantum mechanical.
The emergence of quantum computers has validated this concept, as they can leverage quantum mechanical features like superposition, interference, and entanglement to handle the immense processing power required to model complex quantum systems.
Quantum computing today
Early attempts to develop quantum computing hardware progressed slowly due to technical challenges in shielding and coherently controlling the dynamics of quantum mechanical properties at the smallest scales, such as electron spin or photon polarization. However, as of 2024, quantum computing is one of the most talked-about fields and has experienced rapid growth in recent years.
There is significant enthusiasm among academics and businesses to build initial quantum computers because they promise processing powers beyond those of today’s most powerful supercomputers for specific tasks.
Major efforts are underway to develop large-scale quantum computers, involving established corporations like ZTE, QUDOOR, Honeywell, Intel, Google, Microsoft, and IBM, as well as growing SMEs such as D-Wave and startups such as Rigetti, Xanadu, Infleqtion, Origin Quantum, and IonQ.
Substantial advancements in quantum algorithms and software have paralleled the development of quantum hardware.
The power of qubits
Traditional digital computing relies on bits that can be either ‘0’ or ‘1’ to store and process data. In contrast, quantum computing uses quantum bits (qubits), which, according to quantum physics, can be ‘0’, ‘1’, or exist in a superposition of both states simultaneously.
This allows quantum computers to operate in a computational field of enormous dimension, known as Hilbertspace, where n qubits can exist in a superposition of 2^n possible states at once.
This exponential growth of the parameter space suggests that large-scale problems could be more easily solvable with quantum computers.
Quantum Computing challenges
Nonetheless, developing large-scale quantum computerspresents specific challenges. The most critical challenge is mitigating the decoherence of quantum states, which occurs when qubits interact with their environment and lose their coherent properties.
Decoherence is a significant obstacle to building large-scale quantum devices. Current research focuses on reducing decoherence and developing effective error correction procedures to address defects in Noisy Intermediate Scale Quantum (NISQ) devices, which attempt to manage imperfections and losses caused by decoherence.
Another significant challenge is effectively engineering and interconnectingqubits. Presently, quantum devices can only manage sparsely connected qubits, making it difficult to implement deep quantum circuits with multiple two-qubit gates requiring strong couplings between qubits.
The future of Quantum Computing
Despite technological hurdles, NISQ (Noisy Intermediate-Scale Quantum) computers have shown promising computational capabilities in their early stages. A significant milestone was Google’s demonstration of quantumsupremacy, which marked a major advancement in the field of quantum computing.
Currently, there is a global race to achieve Quantum Advantage—the point at which quantum computers can solve practical problems that conventional computers cannot solve within a reasonable time frame. To achieve this level of quantum computing, substantial improvements in quantum hardware, algorithms, and error correction are essential.
Efforts are ongoing to develop and benchmark quantum algorithms using NISQ devices. While Grover’s and Shor’s algorithms were among the first notable quantum algorithms from the early 1990s, hundreds of others have been developed since then.
Variational Quantum Eigensolver (VQE) and other variational quantum algorithms are popular hybrid quantum-classical algorithms that leverage the strengths of both technologies.
On NISQ devices, VQE algorithms have performed well in solving quantum mechanical problems and Quantum Artificial Intelligence (QAI) tasks.
Although a large, resilient quantum computer capable of fully realizing practical applications is not yet available and will require significant advancements, quantum computing is already yielding encouraging results in research and prototype scenarios using current NISQ-era equipment.
As the field of quantum computing continues to evolve, ongoing research and development remain crucial to overcoming technical challenges and unlocking its full potential. Many of these innovative efforts qualify for valuable tax credits, providing financial support for pioneering work.
At Leyton, we specialize in helping businesses navigate R&D tax incentives, ensuring they receive the full benefits of their innovation.
