Intel has been one of the main pioneers of the silicon transistor for classical computers for the past fifty years. Now, this is a solid quantum computing strategy.
Key Concepts Behind Intel’s Quantum Computing In 2023
Traditionally, binary computing has been referred to as classical computing. As part of this traditional computing method, information is stored in bits which are represented logically by 0s (off) or 1s (on).
Quantum computers, however, use quantum physics principles such as entanglement and superposition to compute. Quantum bits, also known as qubits, are capable of operating probabilistically in many states, enabling unprecedented levels of parallelism and computational efficiency.
There are only tens or hundreds of entangled qubits in today’s quantum systems, which limits their ability to solve real-world problems. Commercial quantum systems need to scale to more than a million qubits and overcome challenges such as qubit sensitivity and bottlenecks to make effective algorithms practical.
Having worked with industry and academic partners, Intel is one of the companies making significant progress in addressing some of these bottlenecks. Since Intel has been developing silicon transistors for the last 50 years, we’d say Intel has some sort of majority “edge” in the QC race to build its high performance capabilities.
Intel is using its expertise in high-volume transistor manufacturing to develop ‘hot’ silicon spin qubits, much smaller computing devices that operate at higher temperatures. Additionally, the Horse Ridge II cryogenic quantum control chip offers greater integration while the cryoprober enables high-volume testing, contributing to commercialization.
The original Horse Ridge controller chip was originally developed in 2019. However, by manipulating and reading the state of the qubits, the Mark II can control the potential of multiple gates when multiple qubits are used for computation.
As part of its commitment to advancing quantum computing, Intel is building a developer community. The University of Pennsylvania, Technische Hochschule Deggendorf, Keio University, Ohio State University, and Pennsylvania State University have all received grants from Intel to develop a quantum course curriculum, which will be shared with additional universities and widely used throughout academia as a starting point for efforts This .
In Munich, Germany, the Deggendorf Institute of Technology uses the SDK to explore aerodynamics and hydrodynamics problems. Earlier this year, the Deggendorf Institute of Technology hosted Intel’s Quantum Computing Challenge. Many of the submitted submissions examine quantum use cases using the Intel Quantum SDK, such as image denoising, realistic image generation, and unstructured search. As an additional beta user, Leidos explores applications such as quantum machine learning, materials simulation, and wormholes and black holes in astrophysics.
Intel’s Quantum Computing Power
In September 2022, Intel released beta version of Intel® Quantum Software Development Kit (SDK). When released this year, Intel’s spin quantum spin qubit chip and Intel Horse Ridge II control chip will be interoperable with SDK full quantum computers in simulations. Using a low-level virtual machine compiler (LLVM) toolchain, the kit enables developers to program quantum algorithms in simulations, resulting in the Intel SDK integrating seamlessly with C/C++ and Python applications, resulting in greater flexibility and customization.
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Intel’s Most Significant Quantum Computing Project
With SDK 1.0, classical computing developers can collaborate with quantum developers, using programming languages they are familiar with. It also includes a quantum runtime environment for executing hybrid quantum classical algorithms. Qubit can be simulated using a generic or Intel hardware backend, depending on developer preference. IQS (Intel® Quantum Simulator) is an open-source, high-performance generic qubit simulator. A single IQS node can support 32 qubits, and multiple nodes can support more than 40 qubits. Second, the target backend simulates Intel quantum dot qubit hardware and enables simulation of the Intel silicon spin qubit compact model. To build large-scale quantum computers, Intel relies on its expertise in manufacturing silicon transistors.
By using the SDK, users can identify what functions a quantum computer system architecture requires to run algorithms efficiently and accurately. The SDK is also used internally by Intel to collaborate on quantum hardware and software development. As a customizable and extensible platform, the SDK makes developing quantum applications more flexible. Additionally, compilers can also be compared, a standard feature in classical computing development, to determine how well an algorithm is optimized. By viewing the source code, the user gains an understanding of how the system stores data.
The SDK will be enhanced with new features and seamlessly integrated with Intel’s quantum hardware in the future.
Example Project 1
In May 2021 at a collaboration between Delft University of Technology and the Netherlands Organization for Applied Scientific Research, Intel and QuTech published key quantum research findings to resolve the “interconnection bottleneck” between the quantum chip in a cryogenic dilution refrigerator and the complex room temperature electronics controlling it. Nature, a peer-reviewed science journal, covers the innovation and marks a significant milestone in addressing one of the biggest challenges in quantum scalability.
Regarding this collaboration, Stefano Pellerano, Principal Engineer at Intel Labs, said that (the) “The research results, driven by a partnership with QuTech, quantitatively prove that our cryogenic controller, Horse Ridge, can achieve the same high fidelity results as room temperature electronics while controlling multiple silicon qubits. We also successfully demonstrated frequency multiplexing over two qubits using a single wire, which paves the way for simplifying the ‘wiring challenge’ in quantum computing. Together, these innovations pave the way for fully integrating quantum control chips with quantum processors in the future, removing a major bottleneck in quantum scaling.”
Example Project 2
In October 2022, that is announced achievement of mass production of quantum computing chips. A typical research and laboratory process involves manufacturing one quantum chip at a time. Despite the use of extreme ultraviolet (EUV) lithography, Intel managed to achieve a typical 300mm wafer with multiple chips, claiming the highest uniformity and delivering results to date. The company also stated at the time that in order to identify areas where the fabrication process could be further optimized, these test wafers would be investigated.
For this achievement, James ClarkeDirector of Quantum Hardware at Intel, said: “Intel continues to make progress in manufacturing silicon spin qubits using its own transistor manufacturing technology. The high yields and uniformity achieved show that fabricating quantum chips on Intel’s established transistor process nodes is a sound strategy and is a strong indicator of success as the technology ripens for commercialization.”
Where Is Intel Going With Quantum Computing In 2023?
To answer this, we must rely once more on Clarke’s wisdom. Clarke, who has worked at Intel for more than twenty years and has a Ph.D. in Physical Chemistry from Harvard, believes that a number of technological advances have been made, such as open source libraries and new qubit processors, but that there is still a need for better qubit devices and better quality qubits. He thinks we can get there by developing cleaner materials with sharper interfaces than we currently have for our transistor processes while also noting that we lack more sophisticated interconnect technologies.
He also realized that by incorporating control chips near the qubit chips, we could simplify wiring and achieve faster control. Finally, he realized that to achieve quantum practicality, we would need to demonstrate error correction in order to have more stable qubits that could perform the kinds of calculations that would ultimately be possible.
To get there, he said in a company blog posts starting November 2022, “Intel predicts a hybrid future for quantum along with classical supercomputers. Currently, we are limited to working with the relatively small number of qubits that we can simulate or run so that quantum algorithms can co-optimize between the classical component and the quantum component. Part of the algorithm might run on a classical system, with other data from a quantum system. A very large-scale quantum computer will probably have a small supercomputer next to it. And the bill of materials for a quantum computer probably has a lot more from the classical computing space than from a true quantum chip.