On Thursday, Microsoft's Azure Quantum group announced that it has settled on a plan for getting error correction on quantum computers. While the company pursues its own hardware efforts, the Azure team is a platform provider that currently gives access to several distinct types of hardware qubits. So it has chosen a scheme that is suitable for several different quantum computing technologies (notably excluding its own). The company estimates that the system it has settled on can take hardware qubits with an error rate of about 1 in 1,000 and use them to build logical qubits where errors are instead 1 in 1 million.
While it's describing the scheme in terms of mathematical proofs and simulations, it hasn't shown that it works using actual hardware yet. But one of its partners, Atom Computing, is accompanying the announcement with a description of how its machine is capable of performing all the operations that will be needed.
There are similarities and differences between what the company is talking about today and IBM's recent update of its roadmap, which described another path to error-resistant quantum computing. In IBM's case, it makes both the software stack that will perform the error correction and the hardware needed to implement it. It uses chip-based hardware, with the connections among qubits mediated by wiring that's laid out when the chip is fabricated. Since error correction schemes require a very specific layout of connections among qubits, once IBM decides on a quantum error correction scheme, it can design chips with the wiring needed to implement that scheme.
Β© Atom Computing
Cybersecurity practitioners are voicing concerns over a recent executive order issued by the White House that guts requirements for: securing software the government uses, punishing people who compromise sensitive networks, preparing new encryption schemes that will withstand attacks from quantum computers, and other existing controls.
The executive order (EO), issued on June 6, reverses several key cybersecurity orders put in place by President Joe Biden, some as recently as a few days before his term ended in January. A statement that accompanied Donald Trump's EO said the Biden directives "attempted to sneak problematic and distracting issues into cybersecurity policy" and amounted to "political football."
Specific orders Trump dropped or relaxed included ones mandating (1) federal agencies and contractors adopt products with quantum-safe encryption as they become available in the marketplace, (2) a stringent Secure Software Development Framework (SSDF) for software and services used by federal agencies and contractors, (3) the adoption of phishing-resistant regimens such as the WebAuthn standard for logging into networks used by contractors and agencies, (4) the implementation new tools for securing Internet routing through the Border Gateway Protocol, and (5) the encouragement of digital forms of identity.
Β© Getty Images | Erik Pronske Photography
Everyone in quantum computing agrees that error correction will be the key to doing a broad range of useful calculations. But early every company in the field seems to have a different vision of how best to get there. Almost all of their plans share a key feature: some variation on logical qubits built by linking together multiple hardware qubits.
A key exception is Nord Quantique, which aims to dramatically cut the amount of hardware needed to support an error-corrected quantum computer. It does this by putting enough quantum states into a single piece of hardware, allowing each of those pieces to hold an error-corrected qubit. Last week, the company shared results showing that it could make hardware that used photons at two different frequencies to successfully identify every case where a logical qubit lost its state.
That still doesn't provide complete error correction, and they didn't use the logical qubit to perform operations. But it's an important validation of the company's approach.
Β© Nord Quantique
Concerns about AI's energy use have a lot of people looking into ways to cut down on its power requirements. Many of these focus on hardware and software approaches that are pretty straightforward extensions of existing technologies. But a few technologies are much farther out there. One that's definitely in the latter category? Quantum computing.
In some ways, quantum hardware is a better match for some of the math that underlies AI than more traditional hardware. While the current quantum hardware is a bit too error-prone for the more elaborate AI models currently in use, researchers are starting to put the pieces in place to run AI models when the hardware is ready. This week, a couple of commercial interests are releasing a draft of a paper describing how to get classical image data into a quantum processor (actually, two different processors) and perform a basic AI image classification.
All of which gives us a great opportunity to discuss why quantum AI may be more than just hype.
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