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Where we are currently, and where we are headed

Quantum computing is a technology that exploits the laws of quantum mechanics to solve problems too complex for classical computers. The first significant contribution to the development of quantum computing occurred in 1982, when Richard Feynman postulated that to simulate the evolution of quantum systems in an efficient way, we would need to build quantum computers (computational machines that use quantum effects). Nevertheless, it was not until 1994 that the view on quantum computing changed. Peter Shor developed a polynomial time quantum algorithm allowing quantum computers to efficiently factorize large integers exponentially quicker than the best classical algorithm on traditional machines, turning a problem which is computationally intractable into one that can be solved in just a few hours by a large enough quantum computer. So, once practical quantum computers are a reality, it will be possible to crack cryptographic algorithms based on integer factorization, such as RSA, which are fundamental for the operation of internet protocols.

But what do we mean by a large enough quantum computer? How far are we from building it?

Large technology companies have been working for years with the objective of building a large-scale quantum device. As published by the Quantum Insider, the leading players in this field are Google, IBM, Microsoft and AWS (Amazon), although IBM has the longest computing history.

Apart from them, there are other promising companies which are also invested in fabricating quantum hardware and developing software. Some examples are D-Wave, Rigetti Computing, IonQ, PsiQuantum, Quantiuum or Oxford Ionics. It is worth noting that not all of them are working on the same type of quantum computers. Differences among these computers depend on the nature of qubits and how they can be controlled and manipulated. The main types of quantum computers are superconducting, photonic, neutral atoms-based, trapped ions, quantum dots and gate-based quantum computers, the first being the most mature and popular type.

In 2016, IBM put the first quantum computer on the cloud for anyone to run experiments (the IBM Quantum Experience). One year later, they introduced Qiskit, the open-source python-based toolkit for programming these quantum computers (the version 1.0 will be released this year). Then, in subsequent years, the company developed Falcon, a 27-qubit quantum computer (2018) and the 65-qubit Hummingbird (2020). Also, in 2020, IBM released their development roadmap, which had a major update in 2022 and provides a detailed plan to build an error-corrected quantum computer before the end of the decade. According to this roadmap, IBM was planning to build in 2021 the first quantum processor with more than 100 qubits, the 127 qubit Eagle; in 2022, the 433-qubit Osprey; and finally, in 2023, the 1121-qubit Condor processor. All objectives were successfully achieved. Nevertheless, as Jay Gambetta, VP of IBM Quantum, mentioned in his article, we must figure out how to scale up quantum processors since a quantum computer capable of reaching its full potential could require hundreds of thousands, maybe millions of high-quality qubits. For this reason, in the following years and with the ambition of solving the scaling problem, the company is proposing three different approaches for developing ways to link processors together into a modular system capable of scaling without physics limitations.

Scalability refers to the ability to increase the number of qubits in a quantum system, allowing to solve more complex problems.

Another tech giant working on quantum computing is Google, which has the Quantum AI Campus. This company announced in 2018 a 72-qubit quantum processor called Bristlecone and in 2019 presented a 53-qubit quantum computer, Sycamore, and claimed quantum supremacy for the first time, which generated a lot of debate in the community. Lastly, the Quantum AI researchers announced significant advances in quantum error correction by achieving for the first time the experimental milestone of scaling a logical qubit. Quantum error correction is essential for scaling up quantum computers and achieving error rates low enough for useful calculations.

Quantum supremacy describes the ability of a quantum computer for solving a problem that the most powerful conventional computer cannot process in a practical amount of time.

Microsoft decided to focus on quantum computing in the late 1990s and currently is offering Azure Quantum, a cloud quantum computing service which provides an environment to develop quantum algorithms which can be run in simulators of quantum computers. Due to the companys approach of working with partners and academic institutions, Azure Quantum allows us to choose from different quantum hardware solutions created by industry leaders such as Quantinuum, Ionq, Quantum Circuits, Inc., Rigetti or Pasqal.

Microsoft is taking a different approach on the design of quantum computers they are relying on a new type of qubit, a topological qubit. As they explicitly say, Our approach to building a scaled quantum machine is the more challenging path in the near term, but its the most promising one long term. In this regard, in 2022, Microsoft reported an important achievement on the development topological qubit hardware, and later that year they share more data from their experiments.

Although Amazon has not announced that it is developing quantum hardware and/or software, they launched in 2019 Amazon Braket, a quantum computing service which makes it possible to build quantum algorithms, test them in a simulator, run them on different quantum computers and analyze the results. Customers can access hardware from leaders such as Rigetti, Ion-Q and D-Wave Systems, which means that they can experiment with systems based on three different qubit technologies.

In addition, Amazon also launched the Amazon Quantum Solutions Lab which helps companies to be ready for quantum computing by offering them the possibility to work with leading experts in quantum computing, machine learning, optimization, and high-performance computing.

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The State of the Art in Quantum Computing - Medium