68 citations · 97 across the 5 of their papers we have counts for
7 papers
Quantum--classical break-even in electronic dynamics of macrocyclic molecules
Shu Kanno, Kenji Sugisaki, Takashi Imamichi +5
Here we demonstrate quantum--classical break-even for chemical dynamics applications, reaching useful accuracy with a quantum-hardware wall-clock time comparable to that estimated…
Qiskit Machine Learning: an open-source library for quantum machine learning tasks at scale on quantum hardware and classical simulators
M. Emre Sahin, Edoardo Altamura, Oscar Wallis +6
We present Qiskit Machine Learning (ML), a high-level Python library that combines elements of quantum computing with traditional machine learning. The API abstracts Qiskit's primi…
Approximate Solutions of Combinatorial Problems via Quantum Relaxations
Bryce Fuller, Charles Hadfield, Jennifer R. Glick +8
Combinatorial problems are formulated to find optimal designs within a fixed set of constraints. They are commonly found across diverse engineering and scientific domains. Understa…
Scheduling of Operations in Quantum Compiler
Toshinari Itoko, Takashi Imamichi
When scheduling quantum operations, a shorter overall execution time of the resulting schedule yields a better throughput and higher fidelity output. In this paper, we demonstrate…
Efficient evaluation of quantum observables using entangled measurements
Ikko Hamamura, Takashi Imamichi
The advent of cloud quantum computing has led to the rapid development of quantum algorithms. In particular, it is necessary to study variational quantum-classical hybrid algorithm…
Optimization of Quantum Circuit Mapping using Gate Transformation and Commutation
Toshinari Itoko, Rudy Raymond, Takashi Imamichi +1
This paper addresses quantum circuit mapping for Noisy Intermediate-Scale Quantum (NISQ) computers. Since NISQ computers constraint two-qubit operations on limited couplings, an in…
Profile-guided memory optimization for deep neural networks
Taro Sekiyama, Takashi Imamichi, Haruki Imai +1
Recent years have seen deep neural networks (DNNs) becoming wider and deeper to achieve better performance in many applications of AI. Such DNNs however require huge amounts of mem…