13 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…
Provably Efficient Learning of Fermionic Correlations under Particle-Number Symmetry
Yuki Koizumi, Kaito Wada, Toshinori P. Takama +1
Predicting local fermionic correlations is a central task in quantum many-body physics, as these correlations encode many physically relevant local observables. The ubiquitous part…
Near-Heisenberg-limited parallel amplitude estimation with logarithmic depth circuit
Kohei Oshio, Kaito Wada, Naoki Yamamoto
Quantum amplitude estimation is one of the core subroutines in quantum algorithms. This paper gives a parallelized amplitude estimation (PAE) algorithm that simultaneously achieves…
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…
Exponentially accurate open quantum simulation via randomized dissipation with minimal ancilla
Jumpei Kato, Kaito Wada, Kosuke Ito +1
Simulating open quantum systems is an essential technique for understanding complex physical phenomena and advancing quantum technologies. Some quantum algorithms simulate Lindblad…
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…