NVIDIA is replicating its AI successes to quantum computing
- An American company made 0.7nm chips: EUV lithography machines can’t do it
- CVE-2007-4559 Python vulnerability ignored for 15 years puts 350,000 projects at risk of code execution
- RISC-V only takes 12 years to achieve the milestone of 10 billion cores
- 14000 cores + 450W: RTX 4080 graphics card perfectly replaces the RTX 3080
- Big upgrade: The difference between Bluetooth 5.0 and 5.2
- Geeks Disappointed that RTX 4080/4090 doesn’t come with PCIe 5.0
- What are advantages and disadvantages of different load balancing?
NVIDIA is replicating its AI successes to quantum computing.
To some, quantum computing may sound like science fiction, decades from now.
In fact, many people around the world have already invested in this cutting-edge computing research, with more than 2,100 quantum computing research papers published, more than 250 quantum computing startups, and 22 national-level quantum computing-related policies.
Quantum computing is a new computing mode that follows the laws of quantum mechanics to control quantum information units for computation, which is usually compared with classical computing.
In principle, quantum computing can have a faster computing speed than classical computing, and this gap may be as high as a trillion times.
Quantum computing promises to overcome many of today’s challenges, advancing everything from drug discovery to weather forecasting, and could play a huge role in the future of HPC.
Because of this, a large number of companies and researchers are investing resources into quantum computing.
At present, there are various schemes for the physical platform to realize quantum computing, such as superconductivity, ion trap, neutral atom, silicon quantum, optical quantum, etc., but they all face different challenges.
To accelerate the development of quantum computing, hybrid quantum computing is expected to realize the first practical applications of quantum computing.
The so-called hybrid quantum computing is that quantum computers and classical computers work together to give full play to the advantages of classical computing (such as CPU and GPU) in traditional operations, such as circuit optimization, correction and error correction, and system-level quantum processors (ie QPU) Advantages of being a new type of accelerator.
Compared to CPUs, GPUs are a good choice for hybrid quantum computing because GPUs can shorten the execution time of traditional jobs and drastically reduce the communication latency between classical and quantum computers, which is the main bottleneck for hybrid quantum jobs today .
Meanwhile, another big challenge is software tools. Quantum processors, as emerging hardware, want to realize their programming value, researchers can only use quantum equivalent to low-level assembly code, which means that only quantum computing experts can program quantum accelerators, which makes it difficult to promote The rapid development of quantum computing.
Therefore, the field of quantum computing needs a unified programming model and compiler toolchain.
Compilers allow scientists to easily port parts of their HPC applications to an analog QPU first and then to a real QPU, efficiently finding ways to accelerate quantum computing work.
With GPU-accelerated simulation tools, programming models, and compiler toolchains all brought together, HPC researchers can begin building the hybrid quantum data center of the future.
With industry-leading high-performance GPUs and rich experience in HPC and AI, NVIDIA can help it quickly establish unique products and advantages in the field of quantum computing.
NVIDIA has indeed begun to replicate its AI successes in quantum computing.
Starting from the software closest to the developer, lowering the threshold for using developers, helping developers in the field of quantum computing to solve problems and create value.
Once quantum computing researchers and users choose NVIDIA tools, they will naturally be able to help NVIDIA has a head start in quantum computing.
At GTC 2021, NVIDIA announced the launch of the cuQuantum SDK, which aims to accelerate quantum circuit simulations running on GPUs.
Today, dozens of quantum organizations are already using the cuQuantum software development kit to accelerate their quantum circuit simulations on GPUs.
Recently, AWS offered cuQuantum in the Braket service and demonstrated cuQuantum achieved 900x speedup on quantum machine learning workloads while reducing cost by 3.5x.
Another important value of cuQuantum for advancing quantum computing lies in its ability to implement accelerated computing on major quantum software frameworks, including Google’s qsim, IBM’s Qiskit Aer, Xanadu’s PennyLane, and Classiq’s Quantum Algorithm Design platform.
For scientists and developers, users of these frameworks can access GPU acceleration without any further coding.
For NVIDIA, it will mean its important value in quantum computing software frameworks, as well as giving full play to the role of its GPUs in hybrid quantum computing.
On July 12, 2022, NVIDIA continued to move forward in the field of quantum computing, releasing QODA, a unified computing platform.
The goal of the Quantum Optimized Device Architecture (QODA) is to make quantum computing easier to use by creating a coherent hybrid quantum-classical programming model.
QODA also enables HPC and AI experts to easily port their applications to the public cloud, NVIDIA DGX systems, or supercomputing centers equipped with large numbers of NVIDIA GPUs.
For quantum organizations that have already simulated quantum circuits on GPUs using the cuQuantum software development kit, QODA enables quantum researchers to also develop quantum circuits in the same cuQuantum simulation environment.
Like AI and high-performance computing, ecology is the key to success, so hardware and software partners are critical to NVIDIA’s success in quantum computing.
At the Q2B 22 Tokyo Quantum Computing Conference, NVIDIA announced a partnership with quantum hardware suppliers IQM quantum Computers, Pasqal, Quantum, Quantum Brilliance and Xanadu, software suppliers QC Ware and Zapata Computing, and supercomputing centers Germany’s Urich Research Center, Lawrence Berkeley The National Laboratory and Oak Ridge National Laboratory collaborate on QODA.
NVIDIA CEO Jensen Huang has always emphasized that what NVIDIA has to do is to create new products and markets, not to seize existing markets.
Quantum computing is such a brand-new market. Whether NVIDIA chooses a technical route in the field of quantum computing or the entry point it chooses, it will help it seize the opportunity of quantum computing.
But we must also see that quantum computing still has a long way to go, and it is difficult to judge who can have quantum supremacy.
- DIY a PBX (Phone System) on Raspberry Pi
- How to host multiple websites on Raspberry Pi 3/4?
- A Free Intercom/Paging system with Raspberry pi and old Android phones
- DIY project: How to use Raspberry Pi to build DNS server?
- Raspberry Pi project : How to use Raspberry Pi to build git server?