Canadian quantum computer company, Xanadu, has used its photonic quantum computer chip, Borealis, to solve a problem in 36 microseconds versus classical supercomputers taking 9000 years. This is 7884 trillion times faster. This runtime advantage is more than 50 million times larger than that of earlier photonic demonstrations.
An earlier quantum photonic computer used a static chip. The Borealis optical elements can all be readily programmed.
Borealis is accessible to anyone with an internet connection over Xanadu Cloud, and will also be available via Amazon Braket, the fully managed quantum computing service from AWS.
“With Borealis on Amazon Braket, for the first time, any researcher or developer will be able to validate a claim of quantum advantage and evaluate how photonic quantum computing may eventually expand their choice of compute technologies, enabling them to innovate more quickly,” said Richard Moulds, General Manager of Amazon Braket at AWS.
Xanadu is working with NVIDIA to provide native GPU support and high-performance computing (HPC) capabilities to the quantum computing researchers and developers working with Xanadu’s open-source software framework for quantum computing, PennyLane. The quantum computing community can now leverage cuQuantum through PennyLane. This collaboration provides PennyLane users with HPC-grade performance enabling them to tackle a wide range of problem scales on cloud platforms and supercomputers in a way that is simple, effective, and incredibly fast.
PennyLane offers a powerful and innovative differentiable programming approach to quantum computing. It seamlessly integrates classical machine learning libraries with quantum hardware and simulators, giving users the power to train quantum computers the same way as neural networks.
NVIDIA cuQuantum is a software development kit that consists of optimized libraries and tools designed to accelerate quantum computing workflows. NVIDIA GPU devices, coupled with cuQuantum, offer best-in-class performance for simulating quantum systems. Users can apply cuQuantum to accelerate standard quantum circuit simulations, as well as develop and test algorithms that may otherwise be intractable with standard tooling.
Xanadu solved a problem called Gaussian boson sampling. This is a benchmark for evaluating quantum computing prowess. The test, while extraordinarily difficult computationally, doesn’t have much impact on real-world problems. However, like chess or Go for measuring AI performance, it acts as an unbiased judge to examine quantum computing performance.
They used a photonic quantum device with 216 qubits. Xanadu’s architecture is modular and capable of scaling to one million qubits through optical networking. They are developing a manufacturable, fault-tolerant module consisting of four components that work together to generate, entangle, and process thousands of qubits.
They have a published blueprint for fault tolerant quantum computers.
Key Features of Xanadu Borealis
Cloud accessible with quantum-computational-advantage-level performance.
A quantum light source, with adjustable brightness, emits trains of up to 288 squeezed-state qubits.
Contains a fully programmable loop-based interferometer, synthesizing a large entangled state suitable for Gaussian Boson Sampling.
Reprogrammable — dynamically program the gate parameters according to your own task.
High-speed processing and readout by true photon-number-resolving detectors.
Access for everyone. Sign up for your Xanadu Cloud Free Tier account and get access to Xanadu’s hardware, software, educational resources — including US$1000 of computing credits for starter workloads.
5 million shots on Borealis quantum hardware for free.
10 million shots on X-Series quantum hardware for free.
Unlimited access to educational resources and tutorials.
Community forum support.
Nature – Quantum computational advantage with a programmable photonic processor
A quantum computer attains computational advantage when outperforming the best classical computers running the best-known algorithms on well-defined tasks. No photonic machine offering programmability over all its quantum gates has demonstrated quantum computational advantage: previous machines were largely restricted to static gate sequences. Earlier photonic demonstrations were also vulnerable to spoofing, in which classical heuristics produce samples, without direct simulation, lying closer to the ideal distribution than do samples from the quantum hardware. Here we report quantum computational advantage using Borealis, a photonic processor offering dynamic programmability on all gates implemented. We carry out Gaussian boson sampling (GBS) on 216 squeezed modes entangled with three-dimensional connectivity, using a time-multiplexed and photon-number-resolving architecture. On average, it would take more than 9,000 years for the best available algorithms and supercomputers to produce, using exact methods, a single sample from the programmed distribution, whereas Borealis requires only 36 μs. This runtime advantage is over 50 million times as extreme as that reported from earlier photonic machines. Ours constitutes a very large GBS experiment, registering events with up to 219 photons and a mean photon number of 125. This work is a critical milestone on the path to a practical quantum computer, validating key technological features of photonics as a platform for this goal.
SOURCES – Xanadu, Nature, Arxiv
Written by Brian Wang, Nextbigfuture.com
Brian Wang is a Futurist Thought Leader and a popular Science blogger with 1 million readers per month. His blog Nextbigfuture.com is ranked #1 Science News Blog. It covers many disruptive technology and trends including Space, Robotics, Artificial Intelligence, Medicine, Anti-aging Biotechnology, and Nanotechnology.
Known for identifying cutting edge technologies, he is currently a Co-Founder of a startup and fundraiser for high potential early-stage companies. He is the Head of Research for Allocations for deep technology investments and an Angel Investor at Space Angels.
A frequent speaker at corporations, he has been a TEDx speaker, a Singularity University speaker and guest at numerous interviews for radio and podcasts. He is open to public speaking and advising engagements.
6 thoughts on “Xanadu Photonic Quantum Chip Solves Trillions of Times Faster”
Scott Baker – would like to +1 you, but new comment system does not seem to allow that.
Quantum entanglement is real… No real?
Crazy… Not crazy
What can I say?
I cannot understand anything of the gobbledygook.
If this is as useful as you say, why doesn’t Xanadu and Nvidia do the world a huge carbon savings favor while making billions, by simply coining all the remaining Bitcoins left to be coined, currently estimated to be 2,163,000 – https://www.blockchain-council.org/cryptocurrency/how-many-bitcoins-are-left/.
If they can’t do this, I question how much real world application this development has, though I personally could benefit from sub-half hour rendering times for my 375MB Sketch-Up model: http://bit.ly/Riverarch
Indigo Renderer is the only software out of 6 I tested that doesn’t choke completely when rendering my largest-in-the-world building model (even with most of the inside items turned off). It would be great if I could do a render in seconds instead, but I think it’ll take longer to get the Xanadu Photonic Quantum Chip longer to be able to do that then 100 renders in my classical iMac with a 4 GHz Quad-Core Intel Core i7.
Any collision-resistant cryptographic hash function that has an output size >=256 is quantum-safe. You can see this from the optimality of the Grovers algorithm. This is the reason that there is no hash competition from NIST post-quantum. A Quantum computer is not magic. It can solve some problems easily like the period finding algorithm of Shor’s that can be used to factor RSA moduli or solve discrete logarithm problems. What they solved is actually a physical problem (Gaussian boson sampling https://en.wikipedia.org/wiki/Boson_sampling) that has no shortcut in a classical computer. This is a true demonstration of what is envisioned for this problem.
Not all problems can be accelerated by quantum computing techniques. Rendering is likely one of those “classical” problems that isn’t. As the EPA is fond to say, “your milage may vary”.
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