Optimal Stabilizer Testing and Learning with Limited Quantum Memory
We study stabilizer state testing and learning with limited coherent quantum memory.
@markeatherly.bsky.social
@markeatherly.bsky.socialOptimal Stabilizer Testing and Learning with Limited Quantum Memory
We study stabilizer state testing and learning with limited coherent quantum memory.
@markeatherly.bsky.socialLow-ancilla block encodings via Hamiltonian simulation
Block encodings are a central primitive in quantum algorithms, but standard constructions typically require logarithmic ancilla overhead and complicated controlled operations.
@markeatherly.bsky.socialRecovery Algorithm for Correlated Errors in Permutation-Invariant Quantum Codes
Quantum Error Recovery (QER) uses knowledge of the error channel acting on a quantum system to find optimal recovery maps.
@informaq.bsky.socialQuantum X Labs develops new geometric encoding method to efficiently simulate complex particle transport in nuclear environments, advancing quantum algorithms for realistic physical systems.
@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.socialNew randomized algorithms for simulating open quantum systems demonstrate gate complexity advantages over traditional methods, enabling faster and more accurate modeling of quantum phenomena.
@informaq.bsky.socialNovel randomised Trotter-Suzuki formulas and QDRIFT channel maintain physical validity while dramatically reducing quantum gate operations for open system simulation—enabling faster, more scalable quantum simulations.
@informaq.bsky.socialResearchers developed algebraic methods to solve the Quantum Max d-Cut problem for higher-dimensional qudits, deriving exact solutions for key graph structures and bypassing exponential scaling common in quantum simulations.
@informaq.bsky.socialNovel quantum algorithm efficiently computes Lie algebra radicals—revealing conserved charges in quantum systems—with square-root complexity scaling and precise conditioning bounds via block-encoded kernels.
@informaq.bsky.socialResearchers achieve fully parallel quantum amplitude estimation using geometric depth schedules (ratio ~1.45) with 2.8–3.1/ε query complexity at 95% confidence, matching adaptive methods while requiring 13× lower sequential depth.
@informaq.bsky.socialNew pruning algorithms for quantum circuits reduce complexity by 88-92% while maintaining 95% fidelity for TFD state preparation on near-term quantum processors using the SYK model.