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Daisuke Sakamoto

Publications and source records attributed to Daisuke Sakamoto.

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Efficient equivalence checking of Clifford-U circuits with shared single-qubit unitaries

Quantum circuit equivalence checking asks whether two circuits implement the same unitary. It guarantees compiler correctness and safe optimization, yet most existing approaches scale exponentially with the number of qubits or the circuit depth, or are restricted to specific circuit structures. In this work, we present an equivalence-checking method for circuits formed by arbitrary single-qubit layers interleaved with Clifford layers. This pattern is common in variational quantum algorithms and Hamiltonian simulation via Trotter decomposition. It can also represent any unitary with sufficient depth. We prove the existence of an efficient classical algorithm that determines whether a pair of circuits with shared single-qubit layers are equivalent for every possible choice of the shared single-qubit unitaries. The same algorithm can also certify their non-equivalence for fixed assignments of single-qubit unitaries. Our framework supports the validation of emerging quantum compilers and facilitate the discovery of novel circuit optimization passes.

quant-ph

Exploring Gestural Interaction with a Cushion Interface for Smart Home Control

In this research, we aim to realize cushion interface for operating smart home. We designed user-defined gestures using cushion and developed gesture recognition system. We asked some users to make gestures using cushions for operating home appliances and determined user-defined gesture sets. We developed two methods for gesture identification. The First, We inserted sensor modules consisting of photo reflective sensors and acceleration sensor inside a cushion. The second, we embedded the acceleration sensor arrays in the cushion cover. Gesture recognizer was implemented using Convolutional Neural Networks (CNN). To evaluate our method, We conducted an experiment to measure recognition accuracy. Results showed that an average accuracy was 94.8% when training for each user, and an average accuracy of 91.3% when testing with a user that did not exist in the training data set.

cs.HC