The convergence of Quantum with AI
Specialised hardware that finds low-energy states of an optimisation problem rather than running arbitrary circuits. Commercially available at scale, with an ongoing debate about where it beats classical methods.
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D-Wave has been awarded up to $100 million through the U.S. CHIPS Act to expand its quantum computing hardware efforts. The funding supports its dual-platform strategy, covering both quantum annealing and gate-model systems.
D-Wave could use the federal funding to bring a gate-model processor to its Leap cloud service within two years, giving existing annealing customers an on-ramp to circuit-model workloads.
NEC Corporation has ended its research and development of superconducting quantum computers. The company will shift toward quantum-inspired annealing and classical emulation, concentrating on software and optimization services. Fujitsu remains the main Japanese corporate developer of superconducting quantum hardware.
NEC could package its annealing and classical emulation capabilities into commercial optimization services for Japanese enterprises within the next two years.
A new arXiv preprint applies time-dependent variational Monte Carlo to simulate the D-Wave quantum advantage experiment reported by King et al. The abstract notes that King et al. had argued classical simulation would require exponential resources for tensor networks and neural quantum states, and frames this work as a test of that claim. The available abstract ends before revealing the simulation's outcome.
If the t-VMC results hold up, this method could become a standard classical benchmark for future quantum annealing advantage claims, allowing rapid independent checks before such claims are widely accepted.
An arXiv preprint proposes a machine learning-based method for tuning penalty parameters when solving the Minimum Bisection Problem on quantum annealers. The approach targets the QUBO formulation of constrained optimization, aiming to automate penalty weight selection rather than relying on manual tuning. The paper evaluates the method on quantum annealing instances.
Learned penalty-tuning models could be integrated into quantum annealing software toolchains within two years, automatically setting penalty weights for new constrained optimization problems.
D-Wave has been awarded funding by the National Research Council of Canada to advance its commercial quantum annealing technology. The funding is intended to support development of annealing quantum computing for commercial applications.
This funding could enable D-Wave to expand pilot deployments of its Advantage2 annealers with Canadian logistics and manufacturing firms, making quantum annealing a tested option for real-world scheduling and optimization within two years.
Canada has awarded funding to D-Wave Systems to advance its quantum computing software tools. The funding is expected to support the company's software stack for programming and hybrid quantum-classical workloads.
Within the next two years, this could help D-Wave extend its Ocean SDK and Leap hybrid solvers enough that logistics and manufacturing firms begin running recurring optimization workloads on quantum annealing, rather than one-off experiments.
D-Wave published a paper in Nature detailing an advance in dual-rail quantum computing. The technique encodes a logical qubit in two physical superconducting qubits to reduce errors and extend coherence times.
D-Wave could integrate dual-rail encoded qubits into its next-generation quantum processors within two years, potentially offering gate-model operations with lower error rates.