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QC Ware Races Ahead With Breakthrough in Quantum Machine Learning Algorithms

Efficient loading of classical data into current quantum hardware increases QML accuracy, advances industry timeline for practical quantum machine learning applications.

Press release from QC Ware
July 21st 2020 | 2777 readers

QC Ware, the leader in enterprise software and services for quantum computing, today announced a significant 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. 

QC Ware’s algorithms researchers have discovered how classical data can be loaded onto quantum hardware efficiently 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.” 

Apart from the Forge Data Loaders, the latest release of Forge includes tools for GPU acceleration, which allows algorithms testing to be completed in seconds versus hours, and 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 come with very significant hardware requirements in qubit count and circuit depth. The Forge Data Loaders provide a powerful and near-term alternative to QRAM. 

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). The Forge Optimized Data Loader can load such data points with just 100 qubits and a circuit depth of 100. 
QC Ware Races Ahead With Breakthrough in Quantum Machine Learning Algorithms

New Forge approach to accelerated GPU simulation of quantum algorithms

The latest release of Forge also offers a new approach to editing and simulating quantum algorithms. While testing larger algorithms can often take several minutes on readily available CPUs, these tests have to be repeated hundreds to thousands of times, adding hours of computation time. With GPU acceleration, algorithm testing can be done in seconds instead of  hours, driving faster development.

Forge enables GPU acceleration with: 

A new library that experts can use to compose circuits

Automatic importing and translation of IBM Qiskit and Google Cirq circuits

Integration with GPUs on the cloud, to which users can submit problems  

Algorithms and hardware access in an integrated platform

Also available in the new release of Forge are various turnkey algorithm implementations to help experts and novices experiment with quantum computing. Each implementation offers unique performance advantages and capabilities:
  • Users can now run quantum classification, regression, and clustering algorithms on larger problems than what was previously possible as the implementations use the Forge Data Loaders and Distance Estimation. Forge contains classification and clustering examples that can run on a simulator with user-specified data sets.
  • Improved quantum annealing performance on D-Wave, by 10 to 100 times for larger problems
  • Optimization of algorithm parameters for optimization algorithms
In addition, the algorithms can be just as easily executed on any of the following backends:
  • Classical CPU simulators
  • Classical GPU simulators (NVIDIA GPUs provisioned on AWS)
  • Hardware on Amazon Braket, which includes access to IonQ and Rigetti hardware
  • D-Wave Systems hardware
QC Ware Races Ahead With Breakthrough in Quantum Machine Learning Algorithms

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