5 papers
Learning Enables Exponential-to-Polynomial Sampling Overhead Scaling in Quantum Divide-and-Conquer for Tree-Structured Circuits
Hiroyuki Harada, Kaito Wada, Naoki Yamamoto +1
Quantum circuit cutting and knitting are divide-and-conquer methods that enable large-scale quantum computations on hardware with limited qubit resources and connectivity by decomp…
Adaptive identification of low-degree polynomials in quantum singular value transformation: application to nonlinear quantum properties estimation
Jumpei Kato, Akira Tanji, Hiroyuki Harada +3
Estimating properties of unknown quantum states via quantum singular value transformation (QSVT) often requires high-degree polynomials to handle small eigenvalues of density matri…
Trade-offs between Quantum and Classical Resources in the Linear Combination of Unitaries
Kaito Wada, Hiroyuki Harada, Yasunari Suzuki +3
The randomized linear combination of unitaries (LCU) method with many applications to early fault-tolerant quantum computing algorithms has been proposed. This quantum algorithm co…
State-to-Hamiltonian conversion with a few copies
Kaito Wada, Jumpei Kato, Hiroyuki Harada +1
Density matrix exponentiation (DME) is a general procedure that converts an unknown quantum state into the Hamiltonian evolution. This enables state-dependent operations and can re…
Density matrix representation of hybrid tensor networks for noisy quantum devices
Hiroyuki Harada, Yasunari Suzuki, Bo Yang +2
The hybrid tensor network (HTN) method is a general framework allowing for the construction of an effective wavefunction with the combination of classical tensors and quantum tenso…