Kentaro Tamura

Shirin Vafaei, Ryohei Fukuma, Takufumi Yanagisawa et al.

The development of algorithms to accurately decode of neural information is a long-standing effort in the field of neuroscience. Brain decoding is typically employed by training machine learning models to map neural data onto a preestablished vector representation of stimulus features. These vectors are usually derived from image- and/or text-based feature spaces. Nonetheless, the intrinsic characteristics of these vectors might be fundamentally different than those encoded by the brain, limiting the ability of algorithms to accurately learn this mapping. To address this issue, here, we propose a representation learning framework, called brain-aligning of semantic vectors, that fine-tunes pretrained feature vectors to better align with the structure of neural representations of visual stimuli in the human brain. We trained this model with functional magnetic resonance imaging (fMRI) data representing 150 visual stimulus categories; then, we performed zero-shot brain decoding on 1) fMRI, 2) magnetoencephalography (MEG), and 3) electrocorticography (ECoG) data reflecting neural representations of visual stimuli. By using fMRI-based brain-aligned vectors, the zero-shot decoding accuracy all three neuroimaging datasets increased. This finding underscores the potential of leveraging a richer array of brainderived features to increase the performance of brain decoding algorithms.

Yutaka Shikano, Kentaro Tamura, Rudy Raymond

All computing devices, including quantum computers, must exhibit that for a given input, an output is produced in accordance with the program. The outputs generated by quantum computers that fulfill these requirements are not temporally correlated, however. In a quantum-computing device comprising solid-state qubits such as superconducting qubits, any operation to rest the qubits to their initial state faces a practical problem. We applied a statistical analysis to a collection of random numbers output from a 20-qubit superconducting-qubit cloud quantum computer using the simplest random number generation scheme. The analysis indicates temporal correlation in the output of some sequences obtained from the 20 qubits. This temporal correlation is not related to the relaxation time of each qubit. We conclude that the correlation could be a result of a systematic error.

Kentaro Tamura, Yutaka Shikano

A cloud quantum computer is similar to a random number generator in that its physical mechanism is inaccessible to its users. In this respect, a cloud quantum computer is a black box. In both devices, its users decide the device condition from the output. A framework to achieve this exists in the field of random number generation in the form of statistical tests for random number generators. In the present study, we generated random numbers on a 20-qubit cloud quantum computer and evaluated the condition and stability of its qubits using statistical tests for random number generators. As a result, we observed that some qubits were more biased than others. Statistical tests for random number generators may provide a simple indicator of qubit condition and stability, enabling users to decide for themselves which qubits inside a cloud quantum computer to use.