Scientists demonstrate brain-quantum computer interface for the first time

Brain-computer interfaces are one of the most talked-about cutting-edge technologies, using which the brain can be connected to devices, which are currently mainly classical computers. So, can the brain be connected to a quantum computer?
 
In an ArXiv paper published on January 5, an international research team presented the first proof-of-concept system for brain-quantum computer interface, demonstrating how a quantum bit can be controlled by mental activity. Enrique Solano, Director of the Quantum Artificial Intelligence Science and Technology Center (QuArtist) and Distinguished Professor at Shanghai University participated in this research.
 
The researchers developed a method to encode neural correlation data of mental activity into instructions for a quantum computer. Brain signals are detected by electrodes placed on the human scalp, and the subjects learn how to generate the mental activity needed to issue instructions to spin and measure quantum bits. They ran this proof-of-concept system on the IBM Quantum Simulator.
 
But the authors also acknowledge that currently, available quantum computing hardware and brain activity sensing technologies are not sufficient to develop real-time control of quantum states with the brain. But we are one step closer to connecting the brain to a real quantum machine, and as hardware technology improves, a brain/quantum computer interface will become available in the future. The paper concludes with a discussion of some of the challenging problems that need to be solved before we can connect the brain to quantum hardware.

The first control of quantum bits through the brain

In a recent prospective paper, the team proposed the concept of Quantum Brain Networks (QBraiNs) as an emerging interdisciplinary effort that combines knowledge and methods from neurotechnology, artificial intelligence (AI), and quantum computing (QC).
 
The goal of QBraiNs is to establish direct communication between the human brain and quantum computers. They expect to drive the development of highly interconnected networks consisting of wetware and hardware devices, processing classical and quantum computing systems through Brain-Computer Interface (BCI) and AI technologies. Such networks will include non-traditional computing systems and new ways of human-computer interaction.
 
The idea of connecting the brain to a quantum computer was proposed by Kanas et al. in 2014. in 2020, Miranda [4] reported the first BCI demonstration using quantum computing, where he aimed to use quantum computing to analyze brain signals in order to control other devices, such as robots, vehicles, and musical instruments. In this paper, however, the team envisions the possibility of creating a deeper connection between the brain and quantum computers. The ultimate goal is to be able to use the brain to influence the quantum state of a quantum computer.
 
The present work is the first attempt by scientists to control quantum bits with brain signals.

Detection, coding and classification of brain systems

In this paper, the researchers used the scalp EEG (scalp EEG) technique to detect brain signals, purchasing an off-the-shelf device from the Austrian company g.tec, consisting of a cap with electrodes and a transmitter that transmits the EEG wirelessly to a computer. The standard protocol is shown in Figure 1.

Figure 1 Scalp EEG (scalp EEG)

The researchers developed a simple method to encode the EEG into instructions for rotating quantum bits. The method considers two mental states: low arousal (also known as relaxation) and high arousal (also known as arousal). However, to control the quantum bits, we need at least four different instructions. Since the number of instructions is greater than the number of mental states, the authors pass the instructions to the system sequentially through a unique "brain code". These are binary codes similar to Morse code.
 
Each instruction has a unique brain code, where 0 and 1 correspond to the relaxed and excited mental states, respectively.
 
{0, 1}: This is the instruction that starts the program and is used to initialize the connection to the quantum system. Without this initialization, none of the other instructions can work.
{1, 1}: This instruction increases the rotation angle by a predefined amount.
{0, 0}: This instruction subtracts the rotation angle by a predefined amount.
{1, 0}: This instruction has two functions. When it appears for the first time, it changes the axis of rotation of the Bloch ball (Figure 2) from z (vertical axis) to y (horizontal axis), and vice versa. Then, when it appears a second time, the system measures the quantum bits.

Figure 2 Rotating quantum bits using the brain code {1, 1}

Since the environment in which the brain signals were detected was very noisy, the authors used machine learning techniques to help identify the two states of the brain. In order to teach the system to classify between the two mental states, a training set needs to be compiled using labeled data generated by the subject.
 
First, the system had to be calibrated for a specific subject. And that person needs to train themselves how to generate EEGs corresponding to the relaxed and excited mental states. For example, closing the eyes is one of the easiest and most practical ways to induce the brain to produce a "relaxed" alpha rhythm. Mentally solving a puzzle or mathematical problem can induce the brain to produce an (exciting) beta rhythm.
 
Once the subject has been trained to switch between the two mental states, samples of EEG signals corresponding to the corresponding states can be recorded to form the training data set for the classifier. Next, the authors perform a fast Fourier transform (FFT) analysis on each sample to calculate their average power in the alpha and beta bands. These values were used as features to teach the samples' profiles to the machine learning algorithm.
 
For machine learning, the K nearest neighbors (KNN) algorithm was used.KNN is a supervised machine learning method that is widely used for classification and regression. In the case of classification, it is based on assigning a class (or label) to a given sample whose k nearest neighbors (in a given metric space) mostly belong to that class.
 
The similarity between samples is calculated using Euclidean distance. The algorithm calculates all possible pairwise Euclidean distances between them. Samples that are close to each other are assigned the same label. The assumption in this paper is that similar brain activities have EEG features that are close to each other. Thus, KNN enables the system to determine the labels or categories of new input EEG data using distance criteria.

Proof-of-Concept System

As mentioned earlier, the subjects generated brain codes or instructions to spin a quantum bit by changing their mental state. There is a metronome that synchronizes the brain with the system. It emits one "click" per second. The system builds the brain code within a window of time that lasts four clicks (i.e., four seconds). The flow chart in Figure 3 illustrates how the system works.

Figure 3 System flow

Initially, the system emits four kata to prompt the subject to prepare to start working. Subsequently, the brain activity detected in the next four kata will correspond to the first number in the code. Similarly, the second number was determined by the next four kata. Then, a break of four sound kata is provided to allow the subject to monitor the output, i.e., to observe whether the desired quantum bit rotation has been achieved. Then, the cycle starts again, and so on. Figure 2 shows the rotation of quantum bits using the code {1, 1}. In this case, the system detects two consecutive excited mental states in the EEG. This instructs the system to rotate the quantum bit to the right by a given angle. "Increasing the rotation angle" means shifting the state vector to the right; "decreasing the rotation angle" means shifting the state vector to the left.
 

The program code used in this article is available through GitHub at
https://github.com/iccmr-plymouth/Quantum-BCI 

Paper Link: https://mp.weixin.qq.com/s/bOp3KHoGzdrZioH_VOLRCg