QC Ware Quantum Loader Breakthrough for up 100X Faster Training of Quantum Machine Learning

QC Ware, a leader in enterprise software and services for quantum computing, announced a breakthrough in quantum machine learning (QML) that increases QML accuracy and speeds up the industry timeline for practical QML applications on near-term quantum computers. They have achieved a speedup of about 100 times.

Nextbigfuture interviewed Yianni Gamvros, Head of Product and Business Development at QC Ware. Quantum Computers are still much slower than classical computers. However, the new loading system, algorithm improvements and hardware improvements are rapidly enabling quantum systems to catch up to classical computers.

Classical AI systems have improved 44 times over the last eight years using algorithmic improvement. ImageNet classification has been decreasing by a factor of 2 every 16 months. Compared to 2012, it now takes 44 times less compute to train a neural network to the level of AlexNet. Moore’s Law would yield an 11x cost improvement over this period with hardware speedup. there are custom AI chips that have about 100X speedup.

QC Ware’s algorithms researchers can efficiently load classical data onto quantum hardware and how distance estimations can be performed quantumly. These new capabilities enabled by Data Loaders are now available in the latest release of QC Ware’s Forge cloud services platform, an integrated environment to build, edit, and implement quantum algorithms on quantum hardware and simulators.

“QC Ware estimates that with Forge Data Loaders, the industry’s 10-to-15-year timeline for practical applications of QML will be reduced significantly,” said Yianni Gamvros, Head of Product and Business Development at QC Ware. “What our algorithms team has achieved for the quantum computing industry is equivalent to a quantum hardware manufacturer introducing a chip that is 10 to 100 times faster than their previous offering. This exciting development will require business analysts to update their quad charts and innovation scouts to adjust their technology timelines.”

The latest release of Forge also includes tools for GPU acceleration. This allows algorithms testing to be completed in seconds versus hours. There are turnkey algorithms implementations on a choice of simulators and quantum hardware. Simulations are executed on CPUs and Nvidia GPU on AWS. Quantum hardware integrations include D-Wave Systems, and IonQ and Rigetti architectures through Amazon Braket.

“To gain performance speedups on near-term quantum computers, it’s important to keep pushing the boundaries of what is possible with current hardware and current algorithms,” said Iordanis Kerenidis, Head of Quantum Algorithms International at QC Ware. “We are constantly striving to make fewer qubits and shallower circuits do more through innovative algorithms.”

Industry impact of Forge Data Loaders

Forge offers two types of data loaders: the Forge Parallel Data Loader and the Forge Optimized Data Loader, which optimally transform classical data to quantum states to be readily used in machine learning applications. Additionally QC Ware is introducing optimized Distance Estimation algorithms that allow for powerful quantum classification and clustering applications.

These capabilities were considered major challenges for QML algorithms. Most research papers from academia, government, and industry assume the availability of Quantum Random Access Memory (QRAM), the quantum equivalent of classical RAM, to load data on quantum computers. However, very few researchers and vendors have worked on QRAM, and the few proposals around it came with very significant hardware requirements in qubit count and circuit depth.

The table below illustrates what is required to load data points with a thousand features each. Compared with the Forge Data Loaders, the traditional approaches are impractical because they require hardware technology that does not yet exist (QRAM hardware) or an impossible number of qubits and/or deep circuits (Multiplexer and QRAM-inspired circuit).

SOURCES – QC Ware, Interview with Yianni Gamvros
Written By Brian Wang

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