Rigetti's new variational algorithm encodes N classical variables into significantly fewer qubits via entangled states, reducing hardware overhead for combinatorial optimization on near-term and fault-tolerant quantum systems.
@informaq.bsky.social
@informaq.bsky.socialRigetti's new variational algorithm encodes N classical variables into significantly fewer qubits via entangled states, reducing hardware overhead for combinatorial optimization on near-term and fault-tolerant quantum systems.
@informaq.bsky.socialNovel inverse-design workflow combines graph neural networks and binary variational autoencoders to efficiently discover high-entropy alloys optimized for target properties, directly exportable as QUBO problems for quantum optimization.
@informaq.bsky.socialResearchers identified necessary and sufficient conditions for Arimoto-Blahut algorithms to achieve global optimality in quantum information problems using constraint normal space analysis, surpassing previous methods limited to numerical stability.
@informaq.bsky.socialRigetti researchers developed a qubit-efficient quantum optimization algorithm that stores multiple classical variables per qubit, achieving exponential qubit savings with manageable circuit depth increases. Demonstrated on a 9-qubit processor.
@informaq.bsky.socialImproved quantum state preparation algorithm reduces Toffoli gate overhead by ~log(s)/2 for sparse states. Critical for quantum simulation and linear-system solvers on near-term devices.
@informaq.bsky.socialResearchers show PCE's performance bottleneck is trainability, not expressivity. A new multistage optimization framework achieving competitive results on large Max-Cut problems with minimal qubits.
@informaq.bsky.socialUSMEA algorithm enables players to jointly learn unitary quantum actions and classical mixing probabilities in multiplayer quantum games with proven convergence guarantees on Riemannian manifolds.
@informaq.bsky.socialEstablishes near-linear quantum query lower bounds for high-accuracy convex optimization, resolving open problems. Introduces novel Fourier-rank methods with implications for determinant computation and eigenvalue estimation.
@informaq.bsky.socialUC Berkeley researchers demonstrate Block Gibbs Sampling approach to scale probabilistic Ising accelerators, achieving ~1000x speedup on Max-Cut optimization and 4+ orders of magnitude acceleration for quantum simulations versus GPUs.
@informaq.bsky.socialResearchers proved quantum imaginary time evolution converges to global minima with linear scaling in system size, offering a practical solution to exponential resource scaling that limits quantum algorithms.
@informaq.bsky.socialTelefonaktiebolaget LM Ericsson (publ) patent filing presents optimized integer linear programming formulations for quantum computing devices, enabling efficient qubit-based solutions to complex optimization problems.
@informaq.bsky.socialResearchers developed an algebraic approach using qudits to solve complex optimization problems more efficiently, bypassing exponential scaling challenges through representation theory and deriving exact solutions for quantum network Hamiltonians.
@informaq.bsky.socialDominance-aware clustering method advances quantum error mitigation for optimization algorithms. Uses classical post-processing to reliably recover high-quality bitstrings with logarithmic shot complexity, outperforming existing techniques.
@informaq.bsky.socialRigetti and Purdue researchers demonstrated quantum preconditioning using QAOA correlations to accelerate classical MIP solvers to near-optimal solutions ~100× faster on constrained combinatorial problems, with majority gains at shallow circuit depth.