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M. Murao

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Preprint Sep 2026

An exponential query advantage from access to Stinespring dilation unitaries in quantum channel learning

We present a learning task for quantum channels that exhibits an exponential separation in query complexity (in terms of the number of qubits) between two access models: i) black-box channel access and ii) access to a Stinespring dilation unitary of the channel along with its inverse. We consider the task of learning h...

Manaki Arihara, Satoshi Yoshida, Philip Taranto et al. · 0 citations
Preprint Sep 2026

Reducing the Entanglement Cost of Distributed Bipartite Quantum Computation with Constant Qubit Overhead

Distributed quantum computation connects multiple quantum processing units (QPUs) through quantum communication to jointly perform large-scale quantum computations. Since the number of qubits available at each QPU is limited, it is important to reduce quantum communication while keeping the qubit overhead small. To thi...

Kosuke Matsui, Jun-Yi Wu, Min-Hsiu Hsieh et al. · 0 citations
#machine learning Preprint Sep 2026

Advantage of Sample Complexity in Quantum PAC Learning Requires Inverse Access to State-Preparation Unitaries

The results show that forward-only access cannot provide an asymptotic query-complexity advantage over learning from classical data or quantum data copies in this worst-case setting, and establish the essential role of inverse access in the known realizable-setting improvement.

Natsuto Isogai, Satoshi Yoshida, M. Murao · 0 citations
Preprint Aug 2026

Asymptotically optimal purification of noisy unitary channels in any dimension

The optimal fidelity for the dual problem of noisy unitary conjugation, where the goal is to obtain the best approximation of the complex conjugate of the original unknown unitary from access to noisy queries, coincides with that of noisy unitary purification to the leading-order in the low-noise and large-query limit.

Ryotaro Niwa, Satoshi Yoshida, M. Murao · 0 citations
#machine learning Preprint Sep 2026

A Quantum-Inspired Dequantization Method for Diagonally Weighted Matrix Functions: Application to Learning with Optimized Random Features

The results show that the factorization underlying a quantum block encoding can itself provide sufficient classical structure even when sampling-and-query access to the composite matrix is unavailable, suggesting a classical sampler with prescribed accuracy and polynomially related runtime.

Natsuto Isogai, M. Murao, Hayata Yamasaki · 0 citations
Preprint Aug 2026

Sample-Query Interconversion of Block Encoding of Unknown Quantum States

The results identify inherent limitations of block encoding as a representation of unknown quantum states and reveal a separation between learning properties of a quantum state and generating the state itself, revealing an unavoidable dependence on the dimension of the state.

Manaki Arihara, M. Murao · 1 citation
Preprint Jul 2026

Optimal complex conjugation of unknown isometry channels

A closed-form expression for the optimal fidelity is derived and it is proved that a parallel protocol is optimal even among general quantum superchannels, including adaptive and indefinite-causal-order strategies.

Satoshi Yoshida, M. Murao · 1 citation

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