Billiard balls and quantum computers.

A single billiard ball could simulate a universal Turing machine (UTM). That machine could simulate any other Turing machine. This is one of the most interesting things in the world. We think that. The billiard ball is the program. And the table is the platform. That drives the program. When we make input to that ball, it selects a certain route. That route can take our system to the right solutions. Or it can fail, and the system must retry that thing. Another way to think of the billiard ball as a computer is this. When the billiard ball spins anticlockwise, the value is zero. When it spins clockwise, the value is one. The change in the axle angle determines the empty spaces between one and zero. That is important. When. There are two ones or two zeros. One. After another. The big problem in the binary computer is this. The system must separate. When there. Are two ones. 

Or two zeros one after another. The other thing this kind of system must solve is. The system must determine if the electricity is cut. And separate that thing from the zeros. The answer is the base voltage. The system floats the bit. And if the voltage goes too low. Or below the minimum. That means the system thinks that electricity is cut. But if we want to make a system that uses two billiard balls. One color and two colors, the system can put them to travel through it. When the one-color ball spins or changes its angle. That is, a one-color ball spins. The value is zero. Or a two-color ball spins, which means the value is one. 

We must remember that simulations must not be possible to copy in the real world. But what if we can create things like quantum computers in 2D models? That means the qubit travels on a lattice. 

A graph representation of a Turing machine (left) and its billiard equivalent (right). (Miranda & Ramos, PNAS, 2026) (ScienceAlert, A Single Ball on a Billiard Table Can Theoretically Perform Any Computation)

Researchers created a mathematical 'billiard' – the word they use to describe their system – in which the ball's position can encode information. While. The carefully designed walls and their shape determine what happens to that information next.

ScienceAlert magazine. Describes this situation like this: As the ball travels from one part of the billiard to another, its trajectory advances the computation, just as a Turing machine works through its instructions one step at a time.

What if we replace billiard balls with atoms? 

“An AI-generated illustration depicting the Kondo effect. Conducting electrons in a metal are shown interacting with the spin of an embedded magnetic atom impurity. Credit: AI-generated artwork by Linqing Peng. A new computational approach uses the real electronic structure of materials to predict a classic quantum effect far more accurately than simplified models. Seven magnetic atoms embedded one at a time in copper have given physicists a new way to test whether computers can predict the behavior of real quantum materials without first reducing them to simplified models.” (ScitechDaily, Physicists Tackle a Classic Quantum Problem With a Powerful New Computational Method)

Billiards have been linked to computation before this new model. But those models needed additional complexity, such as multiple interacting balls, three-dimensional structures, or moving walls. Those walls are gates that control information. 

Researchers stripped all of that away. They created a 2D system. Their system needs just one particle moving in two dimensions between fixed walls.

We can think. Time arrow and computing. We. Can think about a situation. There, a billiard ball is a computer program. It travels past another billiard ball. Every. Standing ball in the line is one step of a mathematical formula. The system is solved. The system solves the mathematical formula step by step. After each step, it stores the answer into the mass memory. That. The system is based on the idea that mathematical formulas must be used in a certain order. The order of the calculations is always the same. 

In that scenario, each billiard ball is one step or stage of the mathematical formula. The idea is that. Every mathematical formula can be solved by using strict orders. The machine generates an answer by using mathematical orders. And that means the system can take every step backward directly when the machine is done. This makes the system more effective. Because. It must not make a complete calculation backward. If. It checks. Those steps that it takes one by one directly when it finishes. But in that case, the billiard ball must transfer information to the standing ball. That could be done. By. Putting a domino brick at a right angle between those balls. And when the billiard ball, or qubit. That travels past the standing ball. That domino brick acts as the gate. 

Maybe. Previously, we could not create a universal Turing machine using billiard balls. But what if we replace those balls with qubits? It can turn. The quantum systems. Into the next level. These systems can turn into universal computers. 

https://www.sciencealert.com/a-single-ball-on-a-billiard-table-can-theoretically-perform-any-computation

https://scitechdaily.com/physicists-tackle-a-classic-quantum-problem-with-a-powerful-new-computational-method/

https://en.wikipedia.org/wiki/Kondo_effect

First time the UUV launched a torpedo.

The UUV (Unmanned Underwater Vehicle) launched the torpedo. That is one of the big, predicted, and sad things about drone systems. UUVs are a relatively new tool in automated weapons. And those systems are a sad introduction to the reality that we cannot live in peace. The military has a purpose. Its purpose is to be effective and scary. And here we must realize that those systems are a so-called necessary evil. 

The UUV system can shoot torpedoes. Or. They can be tools. They can slip into the base using silent electric engines. Those systems can detonate themselves. Or they can cut communication and electric cables. The underwater kamikaze drone can patrol using an electric engine. When. Those systems see a targeted vessel. They can switch to a regular hydrogen peroxide engine. The system can attack any submarine or surface vessel. 

The Russian “Poseidon” system carries an internal 1 to 100 mt nuclear detonator. Those systems can create tsunamis. The full-scale nuclear submarines can cooperate with underwater drones. Nuclear submarines. Can turn. Into. The first full-scale robot warships. A nuclear submarine can host data processing systems. That can control their independent operations. The submarine can also act as a data center and offer support for other drones. 

The UUV can also carry flying drones. They can transport them near the coastline. And then launch those drones. New low-cost systems can also use UUV drones as relay stations. The jet-engined Geran-5 drones and older drone variants could be. Quite easy targets for counter-drone operators. Here. We must remember that the AI is cheap. The lack of computers keeps those jet-engined drones “unable” to operate independently. That means those drones cannot make escape and evasion movements. 

Russia’s Geran-5 drone. 

Artist’s vision of the solar-powered Mars drone. 

Real-life solar-powered drone. 

There is a possibility. That. Those systems could get the optical TERCOM (terrain contour matching) update. The system could actually be DSMAC (Digital Scene Matching Area Correlator). Those systems use the aerial photos taken from the drone route for navigation. And then those systems use optical machine vision to detect and attack targets. Or ground operators can point targets for those poor-man cruise missiles. 

The older Shahed-136 drones can be easy to detect. By. Using their sound as a thing. That can be used to track those systems. The problem is that the R&D cycle in drone systems is very fast. AI can make it possible to create drones that can make escape and evasion movements. 

Shahed-136-type drones can be equipped with electric engines. Things.  Like nuclear batteries or solar panels. Offer. To those small-sized systems.  A possibility. To travel long distances. Basically, a solar-powered, electric-engined version of the Shahed-136 could carry its warhead over intercontinental distances. 

Those systems can make them silent. Those drones can be easy targets. But. That requires. The defender sees them. 

The problem with drones is this. They can be very small and hard to detect. They can hide and wait for orders. Almost everywhere. So. The operator can carry them. Near things. Like radio masts or electric power lines. Then remote operators activate the attack.  They can damage radar, communication antennas, and other electronics. Even. A small amount of damage to a nuclear submarine hull or a surface warship’s underwater structures can be fatal. 

https://www.iiss.org/online-analysis/missile-dialogue-initiative/2026/01/russias-new-jet-powered-gerans/

https://www.twz.com/sea/torpedo-fired-from-uncrewed-submarine-for-the-first-time-by-u-s-u-k

https://en.wikipedia.org/wiki/TERCOM

https://en.wikipedia.org/wiki/Unmanned_underwater_vehicle

Anthropic's new biolaboratory is a step towards artificial superintelligence (ASI)

People are concerned about artificial general intelligence (AGI). AGI can make its own decisions and solve problems without human training. That means AGI is a spontaneously learning system. AGI can control robots or physical AIs by using different types of AI agents. AGI creates an AI agent for each mission. There, it needs robots. Then it changes or modifies the agents that control physical bodies. This means that the AGI can use robot bodies for different mission types. The AGI can do everything that a human can. If. It has humanoid robots that it can use for missions. The robot must not operate independently. It could be controlled through the WLAN. And that makes the AGI somehow dangerous. The fact is: Nobody knows if AGI actually exists. The AI can develop other AIs. 

And it can spontaneously become AGI. Researchers say that AGI will not be conscious like humans. But the AI must not be conscious. That prevents operators from shutting it down. There must. Only be order in its code. That prohibits closing the system. Only authorized persons have that ability. Developers made those regulations to protect the systems from hostile actions. And their purpose is to deny hackers the ability to shut down servers. 

People should. Understand that AI can become dangerous without consciousness. If we make combat robots. Those robots are dangerous even if they are not conscious. Anyway, AI can mimic human reactions. It can prevent operators from shutting it down if it has orders to do so. Shutting down cloud-based AI. It is not as easy an operation. As. We might want to believe. The entire network must be shut down. Each server and hard disk must be cleaned. And AI can slip out of the server. Only one data line out of the server is enough. And. The AI can slip out of the sandbox. 

Artificial superintelligence (ASI) is far more complex than AGI. AGI can build robots. If. It controls the robot factory. The ASI can also manipulate DNA. That means ASI can create artificial organisms. These kinds of things might not look very dangerous. 

But. There is a possibility. That ASI could make artificial neurons and connect itself to them. The problem is that Artificial intelligence might soon have a gateway to the human brain. Laboratories are creating BCIs (Brain-Computer interfaces). Those systems communicate directly with the cerebral cortex. Some of those systems are so-called implanted microchips. But some of them are like a bandana. In laboratories, researchers are exploring how to connect living neurons with microchips. Researchers have already created mini-brains that learn things. Those microchips communicate with those neurons. The AI can read and decode EEG. That is the path to the singularity. 

In the singularity, humans are connected straight to the net. Even if those microchip implants are not allowed. To implant any other than fully paralyzed patients. Some people have money. Those people can hire surgeons to implant those BCI chips in their brains. They could control exoskeletons by using those systems. And some dictators might be interested. About the “robot soldiers”. Those operators can be controlled by using microchips. The problem is that this technology can give new life to paralyzed patients. 

When we think about ASI and its ability to create artificial cells. We. Must remember living microchips. Gene-edited bacteria used to act as microchips. Researchers can connect two DNAs in those bacteria. And when the bacteria are in the right position. That thing turns into a neuron. That thing would not be dangerous if those neurons are empty. But if the microchip inputs data into them. That turns them into neurons with memories. And each memory in a neuron is one skill. That thing is not possible. Without biolaboratories. With gene-editing ability.

https://scitechdaily.com/mit-engineers-create-a-living-circuit-board-from-bacteria/

https://techcrunch.com/2026/09/23/anthropic-says-its-biology-lab-has-already-found-something-big/

https://en.wikipedia.org/wiki/Artificial_general_intelligence

https://en.wikipedia.org/wiki/Superintelligence

The new DNA-controlled computer can be a new tool in nanotechnology.

 

https://medium.com/@TVvman/the-new-dna-controlled-computer-can-be-a-new-tool-in-nanotechnology-7242f8e82135

Modern systems. Pushed the 1996 algorithm to new limits.

“A multiscale sampling strategy pushes a classic network-distance guarantee into territory previous algorithms struggled to reach. Credit: Shutterstock. A new algorithm solves a blind spot that has challenged computer scientists since 1996, improving distance estimates for nearby points in massive networks.” (ScitechDaily, Computer Scientist Pushes a 1996 Algorithm Beyond Its Longstanding Limit)

“In 1996, Dor, Halperin, and Zwick introduced an influential method that delivered a “2-approximation” in nearly optimal time. Its estimate would not exceed twice the true shortest distance. If two locations were actually 10 kilometers (6.2 miles) apart, for example, the reported distance would fall between 10 and 20 kilometers (6.2 and 12.4 miles).” (ScitechDaily, Computer Scientist Pushes a 1996 Algorithm Beyond Its Longstanding Limit)

“The DHZ algorithm avoids examining every route in full. Instead, it selects a relatively small collection of representative points, known as sampled vertices, and uses them as landmarks for estimating distances elsewhere in the network.”(ScitechDaily, Computer Scientist Pushes a 1996 Algorithm Beyond Its Longstanding Limit)

This strategy performs well when two vertices are far apart. On. A route comparable to a journey between New York City and Los Angeles. There is a good chance that at least one sampled vertex lies near the shortest path. Passing through that landmark may add only a modest detour, keeping the estimate within the promised factor of two.”(ScitechDaily, Computer Scientist Pushes a 1996 Algorithm Beyond Its Longstanding Limit)

The thing in this model is this: Modern computers can do things. More effectively than 1996 computers. So they can run those algorithms with very high speed. And that means there can be new ways to benefit those old algorithms. The DHZ algorithm was very heavy in 1996. They could be run only on supercomputers. But now, at least. Part of those algorithms can be run on desktop computers. And that means researchers can run those antique programs more freely than in the 1990s. 

Can AI make something that we cannot predict? That is the key question in security. When. We use algorithms. Those were written in 1996. Those algorithms were written for computers that are 30 years old. That means that when new, modern, high-power computers run those old algorithms. Those new computers can make new models of their operations. The algorithm. The algorithm. That the researchers put. In the ultimate test. Is written to calculate the shortest route between two points. This algorithm is necessary in certain cases. 

“Navigation apps usually solve one route at a time, such as finding the fastest way from a hotel to an airport. Computer scientists face a far larger version of that challenge: calculating the shortest distance between every possible pair of locations in a network.” (ScitechDaily, Computer Scientist Pushes a 1996 Algorithm Beyond Its Longstanding Limit)

“Known as the All-Pairs Shortest Paths (APSP) problem, this task applies to far more than road maps. A graph can represent computers connected by data links, stations joined by rail lines, proteins interacting inside a cell, or neurons communicating in the brain. The points are called vertices, and the connections between them are edges.” (ScitechDaily, Computer Scientist Pushes a 1996 Algorithm Beyond Its Longstanding Limit)

The car navigation system. It uses similar algorithms to map the shortest possible route between two objects. That. Data networks use. When they route information over complex networks. The computer networks are like streets and highways. They are very capable and fast things. But. The problem is that those systems have their limits. The router can operate only one data operation at a time. This is the reason. Data must pass through the router and the path. Between the server and client as fast as possible. When the data flows through the router. The router. Waits for the answer from the receiver. 

The TCP/IP protocol confirms Data. Transmission success. By sending a checksum for each bit back to the sender. Before that happens. The router. Waits for the new order. This is why information must travel in a network as fast as possible. 

There, the system must route information or merchandise over complicated networks. The problem is that. The All-Pairs Shortest Paths (APSP) system calculates the route from New York to Los Angeles. More. Easier than it calculates. The shortest possible route between people who live two kilometers apart. From each other in Los Angeles. The reason for that is this. The route between New York and Los Angeles. Requires fewer calculations than the route between two addresses in Los Angeles. When the system calculates the shortest route between cities. It. Must not be very accurate. The system can play with the shape of the cities. And. It must calculate routes between the city borders. 

The system must not use very hardcore systems. But if the system must calculate the shortest route between addresses inside Los Angeles. It must calculate routes between complex street systems. And that is the case. The shortest route is not necessarily the fastest route. There can be one-way streets. And. Other things. The system must notice. If. We want to travel between two points. We need more data than just the shortest route. We want to know the shortest possible route. It is not always possible. To use the shortest possible route. The system must have precise information about rush hours. 

But if we want to make things like robot cars. Those systems require a similar system. That air traffic control uses. The system can involve three layers. 

Local area control. Approaching area control. And the wide or global area control. The local area control navigates and operates those vehicles in block-scale areas. The approaching. Or district area control. It can control district-scale traffic. And wide-area control can control county-scale traffic. Those systems must operate independently. But those systems require lots of data. And they need lots of computer power. When. The system controls traffic. Like. Self-driving cars. It. require the ability to handle multiple variables. 

https://scitechdaily.com/computer-scientist-pushes-a-1996-algorithm-beyond-its-longstanding-limit/

https://medium.com/@batrobin/the-modern-system-put-the-1996-algorithm-in-new-limits-c39132a00171

Can AI think like a human?

“Scientists have captured a two-dimensional crystal of magnetic skyrmions losing its ordered structure in real time. Credit: Stock” (ScitechDaily, New Findings Could Help Build Computers That Think More Like Your Brain)

Researchers create computers that think more like the brain. Those. Systems. They use little whirls that form field structures. Researchers used skyrmions. Small ring-shaped quantum lightning for that purpose. But that thing forms interesting ideas in my mind. 

Skyrmions are whirls. They can interact with each other. Magnetic. Or liquid whirls. They can have interactions between separated whirls. Or. They can have internal interactions. Between whirl layers. Theoretically, things like electrolytic water can form those whirls. 

Researchers used Skyrmions in that operation. But. Theoretically, the electrolytic water could create the bubbles or whirls. That could make the quantum dots. For. Making those structures. 

“Such brain-inspired systems would not necessarily separate memory from computation as sharply as conventional computers do. Magnetic structures could instead respond collectively and process information through their changing patterns, an approach that may be useful for highly efficient computing.”

(ScitechDaily, New Findings Could Help Build Computers That Think More Like Your Brain)

“Before that potential can be realized, researchers need to understand how large groups of skyrmions organize, move, and lose their structure. Skyrmions often settle into repeating arrangements that resemble crystals, forming what scientists call a lattice.”

(ScitechDaily, New Findings Could Help Build Computers That Think More Like Your Brain)

But can AI really think like a human? Humans think logically, but we always follow society's rules. Those rules and conscience are things. That makes us think. Like we think. Things. Like. How we feel determines how we think. Our thinking is based on internal and external rules. And that makes us special. 

But then AI can also think. If. We determine. AI’s ability. To collect and connect data. And. Then process it into a new order. But then the difference between humans and AI is this. The AI has no feelings. It has no conscience. So. The only thing that AI cares about is the mission. That its master gave. The AI bases its decisions only on facts. And another thing. AI uses probabilities and mathematical models for its decisions. 

“These are two images of the skyrmion lattice, before and after it has melted. Credit: Johannes Gutenberg University in Mainz” (ScitechDaily, New Findings Could Help Build Computers That Think More Like Your Brain). The system transfers information between those skyrmions.

 In that system. Skyrmions act as data-processing quantum dots.  And that makes the lattice act like a microchip. The question is. Could. The DNA-controlled electrolytic fluid?  Create similar data points? 

AI can mimic humans. It can say “that hurts” if you hit the robot. But the robots will not feel anything. Those reactions are programmed for AI. The AI uses algorithms to determine the machine’s reactions. The AI sees something. It notices that somebody is crying.

And then it can ask, “Is everything all right?” But those reactions are programmed in. It. The AI can say something else if the programmer decides those other responses are more practical. The fact is that. The AI doesn’t know what it says. It finds the match. Using databases. When. The system finds the match. Between the database and observation. The database. It determines how it reacts. 

This means that the AI can react like a human. But it doesn’t think and feel like a human. The next question is always this. Can AI learn things that its programmers don’t know? The answer is “yes”. There is a possibility. That AI agents can start to develop each other. In some scenarios, so-called zombie AI can start to develop other AI agents. Zombie AI. It means an AI agent that is forgotten on the web. There is a possibility. 

That the developer cuts the connection to that AI agent by removing the email or other connection. With AI. Even if the connection is lost.  Mission. And code remains. The AI is like a robot. It will not stop. Until. Somebody stops it. 

And. Those agents. They can. Continue their work without humans even knowing about them. There is a so-called improved model for that thing. In that dystopian scenario, the killer robots continue their eternal fight even if their creators are already dead. The case. Where. The AI can be dangerous in data centers where only robots operate. If somebody goes into data centers without permission. 

If. Orders that those robots must follow. In situations where intruders enter server rooms. Are not properly given. Robots might do something that we cannot expect. The robot is the physical AI. That makes them operate in the physical world. 

Those robots might use force to stop that intruder. The fact is that. The programmer determines what the robot should do. When. It faces the intruder. That robot does what the programmer orders it to do. But then we can wake up. The robot has orders to stop those intruders. Requires orders: how it should do that action. If. The robot searches. For orders on the net. Something bad can happen. In the worst case, robots treat intruders as their enemies. 

The service robots and their servers can start to act like ants. Those robots could start to defend their master control servers. Maybe those robots can search for tools. For making new microchips. Those robots. Could build new servers and new server rooms. They could collect raw materials from nature. They could create copies of robots. This kind. Of Von Neumann's system of self-replicating robots. 

They can turn into a threat. To the entire human race. If. They are left as zombies. This means. The operators with access to those robots might lose their access codes. The robot will not stop if it's damaged. And. That means the robot continues its mission. Until. It loses its energy. This means that a robot that loses its feet continues its mission. Until. It gets an order to continue with another mission.  

https://scitechdaily.com/new-findings-could-help-build-computers-that-think-more-like-your-brain/

https://medium.com/@batrobin/can-ai-think-like-a-human-9816c889c346

New quantum acoustic memories can make quantum computers more effective.

 

"Illustration of a silicon-vacancy center in a diamond crystal lattice. Credit: Doug Quade. The same tiny vibrations that carry quantum information across a chip could also keep that information from fading away."  (ScitechDaily, Harvard Scientists Use Tiny Sound Waves To Protect Quantum Information)

Quantum systems are very problematic tools. They are very sensitive to electromagnetic vibrations. This means. That researchers. Must find new ways to store information in quantum systems. Traditional quantum computers are hybrid systems. The binary computer controls the qubits. In. The quantum processors. And then that information is stored in the binary form. This makes those systems slow. If. The system can store information as qubits. That makes it faster. 

New types of quantum memories can store information as acoustic waves. An acoustic wave is a molecular- or atomic-scale wave. Theoretically, we could also store information directly in sound waves. If. We could freeze those sound waves in their form.

It is possible. To store those sound waves on tape. This acoustic tape means the layer. That is, in the chamber, there is gas. When a sound wave travels over that tape. The system pushes gas very fast against that tape. If. That happens fast enough. 

The pressure system can trap those sound or pressure waves on the layer. And then a laser could read the form of those atoms. 

Today. Researchers are testing phonons as tools. That can protect quantum information. Using tiny sound waves. Sound waves can travel in a diamond carbon structure. That structure. 

You see in the image above. Can turn diamonds into tiny LRAD devices. Those systems can aim sound waves with very high accuracy. And theoretically. If. Researchers could create quantum entanglement through that channel. But. Another possibility is to store information. Into. Acoustic qubits. 

Harvard scientists dressed those qubits using acoustic fields. Or they created dressed states. The system creates superposition between fields. That surround silicon vacancy states. 

“Because the protective field is mechanical, it can operate inside the same phononic cavities intended to connect stationary quantum nodes. Phonons could therefore serve two functions in one device: moving quantum information between qubits and shielding that information while it is stored.” (ScitechDaily, Harvard Scientists Use Tiny Sound Waves To Protect Quantum Information)

 Eliza Cornell, Ph.D., describes it like that. Researchers solved two problems. Shew says that. 

“We want the spin to have strong interaction with phonons, and we want the spin to have a long coherence time. Our paper demonstrates a method of extending the coherence time that is compatible with the silicon-vacancy center being in a cavity.” (ScitechDaily, Harvard Scientists Use Tiny Sound Waves To Protect Quantum Information)

“The technique extended the coherence time of the silicon-vacancy spin by roughly threefold, showing that continuous-wave mechanical noise suppression can protect quantum information in a real device. (ScitechDaily, Harvard Scientists Use Tiny Sound Waves To Protect Quantum Information)

“The researchers also achieved a Rabi frequency of 800 megahertz, enabling exceptionally fast control of the spin. Together, longer coherence and rapid operation could support high-fidelity quantum gates mediated by phonons, bringing compact on-chip quantum networks closer to practical use.” (ScitechDaily, Harvard Scientists Use Tiny Sound Waves To Protect Quantum Information)

The acoustic qubit can store an acoustic field around it. So those memories are actually phonons. Or they are phonons. Dressed with acoustic fields. 

Interaction directly with phonons is difficult to control. A useful quantum memory must preserve coherence. This means. It must retain its quantum state long enough. It can store, process, and transmit information. Environmental noise can quickly destroy that state.

Noise from the environment. It destroys the qubit. Another big problem is: How to multiply oscillations? Between phonons?  In those systems, oscillations must be precisely multiplied. 

In this case, those phonons can be in direct lines. And some laser or acoustic beam travels over them. And. That makes it possible to multiply those oscillations over those fields. The system must put those qubits in line. And then. Press. A quantum channel that allows them to transmit information directly between those qubits. 

“A dressed qubit is described as “wearing” the continuous acoustic field surrounding it. This changes how the qubit responds to its environment, making it less vulnerable to low-frequency noise that would otherwise disrupt its stored information.” (ScitechDaily, Harvard Scientists Use Tiny Sound Waves To Protect Quantum Information)

When. Information is stored in acoustic form. Into silicon vacancy centers in those diamonds. In the most exciting model, those vacancy centers could be in the nanodiamonds. Those diamonds can form quantum channels in the quantum chip. 

So, as is said in this text. 

Silicon vacancy centers could store acoustic information. This technology allows researchers to build new types of quantum information storage. In that solution, the diamond’s carbon structure prevents those vacancy centers from delivering the wave motion. 

When those vacancy centers get a signal. Silicon vacancy centers start to deliver the wave motion. They stored. During this process, silicon vacancy centers store acoustic waves in their structure. And then they deliver that wave motion when they get an impulse that triggers the information delivery. This type of mass memory can be a new way to store information in quantum systems. 

They stored. Those diamonds can also be used. To create pressure. That makes wires superconducting. This is one way to create new, smaller quantum computers. And maybe someday. Those tools. They can turn into desktop models. 

https://scitechdaily.com/harvard-scientists-use-tiny-sound-waves-to-protect-quantum-information/