Coherent quantum inference achieves O(1/ε) sample complexity for d-dimensional quantum purity amplification, exponentially better than the Ω(d/ε) required by any incoherent measurement-mediated protocol.
Dalzell, András Gilyén, Connor T
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Solves quantum purity amplification for arbitrary n, m, eigenstates, and dimension d, with asymptotic input scaling O(m/(ε D_min²)) independent of d and non-asymptotic bounds from generalized Young diagrams.
A polylog-sized quantum computer achieves exponential advantage over classical machines in classification and dimension reduction of massive classical data using quantum oracle sketching combined with classical shadows.
Quantum circuits for coherent multilayer neural network inference achieve quadratic to polylogarithmic speedups over classical methods depending on quantum data access models for inputs and weights.
New scalable QRAM simulator reveals post-selection constraints on error filtration and produces refined near-deterministic performance criteria.
Precomputes rotation angles classically and adds a magnitude-then-phase procedure to enable complex-valued state preparation on BBQRAM at unchanged O(log²(MN)) query cost with no reversible arithmetic on the QPU.
citing papers explorer
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An Exponential Sample-Complexity Advantage for Coherent Quantum Inference
Coherent quantum inference achieves O(1/ε) sample complexity for d-dimensional quantum purity amplification, exponentially better than the Ω(d/ε) required by any incoherent measurement-mediated protocol.
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Quantum Purity Amplification for Arbitrary Eigenstates and Multiple Outputs
Solves quantum purity amplification for arbitrary n, m, eigenstates, and dimension d, with asymptotic input scaling O(m/(ε D_min²)) independent of d and non-asymptotic bounds from generalized Young diagrams.
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Exponential quantum advantage in processing massive classical data
A polylog-sized quantum computer achieves exponential advantage over classical machines in classification and dimension reduction of massive classical data using quantum oracle sketching combined with classical shadows.
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Accelerating Inference for Multilayer Neural Networks with Quantum Computers
Quantum circuits for coherent multilayer neural network inference achieve quadratic to polylogarithmic speedups over classical methods depending on quantum data access models for inputs and weights.
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Refined Criteria for QRAM Error Suppression via Efficient Large-Scale QRAM Simulator
New scalable QRAM simulator reveals post-selection constraints on error filtration and produces refined near-deterministic performance criteria.
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Efficient Complex-Valued State Preparation on Bucket Brigade QRAM
Precomputes rotation angles classically and adds a magnitude-then-phase procedure to enable complex-valued state preparation on BBQRAM at unchanged O(log²(MN)) query cost with no reversible arithmetic on the QPU.