D-Wave Quantum Shares Edge Higher as $350M Trading Volume Ranks 338th in Market Activity

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The company launched a new open-source quantum AI toolkit integrated with PyTorch, enabling developers to combine D-Wave’s quantum processors with workflows. The toolkit includes a module for training restricted Boltzmann machines (RBMs), a type of neural network used in tasks like image recognition and drug discovery. A demo showcases quantum processors generating simple images, highlighting the potential for to enhance AI model training efficiency.

Collaborations with Japan Tobacco and TRIUMF demonstrated quantum methods outperforming classical algorithms in protein-DNA binding prediction and high-energy particle simulations. These projects underscore D-Wave’s focus on optimization challenges in drug discovery and materials science, differentiating it from competitors through technology. The toolkit’s PyTorch compatibility aims to lower technical barriers for developers, aligning with growing industry interest in solutions.

The backtest result segment requires confirmation on data scope, transaction assumptions, and practical workarounds for simulating a 500-stock volume-weighted basket. Key considerations include universe scope (e.g., S&P 1500 vs. Russell 3000), cost assumptions (e.g., , , and whether to use a synthetic portfolio or narrower index. Finalizing these parameters will enable the backtest engine to generate return/risk metrics for the proposed strategy.