A proof-of-concept published today in Nature promises warmer, cheaper and more robust quantum computing. And it can be manufactured using conventional silicon chip foundries.
Most quantum computers being developed around the world will only work at fractions of a degree above absolute zero. That requires multi-million-dollar refrigeration and as soon as you plug them into conventional electronic circuits they’ll instantly overheat.
But now researchers led by Professor Andrew Dzurak at UNSW Sydney have addressed this problem.
“Our new results open a path from experimental devices to affordable quantum computers for real world business and government applications,” says Professor Dzurak.
The researchers’ proof-of-concept quantum processor unit cell, on a silicon chip, works at 1.5 Kelvin – 15 times warmer than the main competing chip-based technology being developed by Google, IBM, and others, which uses superconducting qubits.
“This is still very cold, but is a temperature that can be achieved using just a few thousand dollars’ worth of refrigeration, rather than the millions of dollars needed to cool chips to 0.1 Kelvin,” explains Dzurak.
“While difficult to appreciate using our everyday concepts of temperature, this increase is extreme in the quantum world.”
Quantum computers are expected to outperform conventional ones for a range of important problems, from precision drug-making to search algorithms. Designing one that can be manufactured and operated in a real-world setting, however, represents a major technical challenge.
The UNSW researchers believe that they have overcome one of the hardest obstacles standing in the way of quantum computers becoming a reality.
In a paper published in the journal Nature today, Dzurak’s team, together with collaborators in Canada, Finland and Japan, report a proof-of-concept quantum processor unit cell that, unlike most designs being explored worldwide, doesn’t need to operate at temperatures below one-tenth of one Kelvin.
Dzurak’s team first announced their experimental results via the academic pre-print archive in February last year. Then, in October 2019, a group in the Netherlands led by a former post-doctoral researcher in Dzurak’s group, Menno Veldhorst, announced a similar result using the same silicon technology developed at UNSW in 2014. The confirmation of this ‘hot qubit’ behaviour by two groups on opposite sides of the world has led to the two papers being published ‘back-to-back’ in the same issue of Nature today.
Qubit pairs are the fundamental units of quantum computing. Like its classical computing analogue – the bit – each qubit characterises two states, a 0 or a 1, to create a binary code. Unlike a bit, however, it can manifest both states simultaneously, in what is known as a “superposition”.
Cheaper and easier to integrate
The unit cell developed by Dzurak’s team comprises two qubits confined in a pair of quantum dots embedded in silicon. The result, scaled up, can be manufactured using existing silicon chip factories, and would operate without the need for multi-million-dollar cooling. It would also be easier to integrate with conventional silicon chips, which will be needed to control the quantum processor.
A quantum computer that is able to perform the complex calculations needed to design new medicines, for example, will require millions of qubit pairs, and is generally accepted to be at least a decade away. This need for millions of qubits presents a big challenge for designers.
“Every qubit pair added to the system increases the total heat generated,” explains Dzurak, “and added heat leads to errors. That’s primarily why current designs need to be kept so close to absolute zero.”
The prospect of maintaining quantum computers with enough qubits to be useful at temperatures much colder than deep space is daunting, expensive and pushes refrigeration technology to the limit.
The UNSW team, however, have created an elegant solution to the problem, by initialising and “reading” the qubit pairs using electrons tunnelling between the two quantum dots.
The proof-of-principle experiments were performed by Dr Henry Yang from the UNSW team, who Dzurak describes as a “brilliant experimentalist”.
The Latest Updates from Bing News & Google News
Go deeper with Bing News on:
- Quantum control for advanced technology: Past and present
Quantum devices are a promising technological advance for the future, but this will hinge on the application of quantum optimal control top real-world devices. A new review looks at the status of the ...
- Why Israel is moving to quantum computing
The Israel Innovation Authority (IIA) has selected Quantum Machines to establish its national Quantum Computing Center.
- Computational power unleashed with quantum digits
In a recent study published in Nature Physics, researchers at the University of Innsbruck, Austria, have unleashed the hidden computational resources that | Technology ...
- Bosch’s new partnership aims to explore quantum digital twins
Bosch's partnership with Multiverse Computing is an example of how many legacy companies are exploring quantum computing today to prepare for more capable hardware.
- NRL Launches Quantum Navy YouTube Series
The Great Power Competition Mark your calendars! Quantum Navy, a new three-part video series, is coming to our YouTube channel Wednesday, Aug. 3. WASHINGTON, Aug. 01, 2022 (GLOBE NEWSWIRE) -- The U.S.
Go deeper with Google Headlines on:
Go deeper with Bing News on:
Practical quantum computers
- NetworkNewsAudio – DPCM Capital Inc. (NYSE: XPOA) Gaining Recognition for Quantum Computing Products, Services
To view the full editorial, visit To hear the NetworkNewsAudio version, visit About DPCM Capital Inc. DPCM Capital, a special purpose acquisition company, on February 9, 2022, announced its entry ...
- How Can Governments Use Quantum Computing for Public Sector?
With a global focus on quantum technology, can quantum computing technologies address public sector problems today? Guest blog by D-Wave Systems ...
- Priming your business for the new age of quantum computing
Last year also saw the establishment of the National Quantum Computing Centre (NQCC). Commenting on EY’s research when it was published, Dr Simon Plant, deputy director for Innovation at the NQCC, ...
- This Startup Raised $9 Million To Make Better Quality Quantum Computers
While there are quantum computers out there in operation, developed by companies like D-Wave, Rigetti, IBM and Google, none have the capability to solve practical problems any faster than a ...
- Researchers create order from quantum chaos
Pair Spin Signatures From Macroscopically Aligned Heteroacenes in an Oriented Single Crystal," National Renewable Energy Laboratory (NREL) researchers Brandon Rugg, Brian Fluegel, Christopher Chang, ...


Rachel Goldman’s lab is working to produce “designer alloys” with carefully tailored electrical and light-absorbing properties. These materials could one day be used to build solar cells with double the efficiency of the flat-panel silicon cells that dot rooftops today. The new cells, called concentrator photovoltaics, use gallium arsenide semiconductors instead of the silicon-based semiconductors used in today’s cells. Gallium arsenide could move us toward the utility-scale solar arrays we’ll need to make solar energy a large part of our electrical infrastructure.
In her most recent paper, Goldman and her collaborators moved forward the science by figuring out how incorporating small fractions of nitrogen and bismuth in gallium arsenide semiconductors affects their structure and light-absorbing properties, creating a new map for bandgap engineering of designer semiconductor alloys. The advance could accelerate the development of concentrator photovoltaics, and could also lead to advances in semiconductor lasers and quantum computing.
Goldman is a professor of materials science and engineering. We sat down with her recently to learn more about her work.
How is your “magic ratio” useful in solar cells?
Concentrator photovoltaics will depend on the development of alloys that are safer and less expensive than those currently used in gallium arsenide semiconductors. In our earlier research, we developed alloys that use a combination of nitrogen and bismuth. Since then, we’ve been working to develop a more complete understanding of exactly how the nitrogen-bismuth combination functions, and how changing the proportion of those two elements affects the alloy’s overall properties.
That research led us to the “magic ratio”—the precise proportion of bismuth to nitrogen that works best with a gallium arsenide substrate. We’ve found that by slightly tweaking that ratio within a certain range, we can control what bandwidth of light that the alloy absorbs.
What’s the main hurdle standing in the way of concentrator photovoltaics?
Turning “near-infrared” light into electricity is one big challenge—this is light that’s just outside the visible spectrum. A gallium arsenide solar cell consists of several thin layers of metal alloy sprayed onto a gallium arsenide substrate. It’s these thin layers that turn light into electrical charge. Each layer absorbs only a specific wavelength of light. A wavelength that slips through one layer can be caught by the next.
The “magic ratio” should help researchers dial in the exact mix of an alloy to absorb whatever bandwidth of light they choose.
How were you able to do what others couldn’t?
We had to start by acknowledging that the conventional way of thinking about alloy composition doesn’t work for bismuth-nitrogen alloys.
Making an alloy out of individual atoms is a little like filling a box with a mix of differently-sized marbles. If you know the sizes of the marbles and the size of the box, you can calculate the combination of marbles that will fill the box exactly. Researchers can calculate the composition of most alloys by using x-ray diffraction to measure the “box” and then calculating the combination of atoms that fits.
That doesn’t work with bismuth and nitrogen. Bismuth is very large and nitrogen is very small, so it’s more like mixing sand and marbles. It’s hard to measure the size of a single grain of sand and even harder to predict how it will flow around all those marbles.
So we worked with labs in New Mexico, Poland and Romania, as well as here at U-M, to develop a series of measurements that would each solve part of the puzzle. Then we brought them all together to precisely determine the ratio of nitrogen to bismuth in a wide range of sample alloys, and how that ratio affects light absorption properties.
Where else might these kinds of alloys be useful?
A better understanding of nitrogen-bismuth alloys could help us build more efficient infra-red lasers, which are widely used in fiber-optic communications and in the military. They could also be used in quantum computing, to build transistors that use the spin of electrons as a way to store information.
When will the results of this research go into widespread use?
There’s still a lot of progress to be made. But this research opens the door to a better understanding of exactly how these alloys work and how to make them do what we want, in solar power and elsewhere.
Goldman’s most recent paper is titled “Mapping the composition-dependence of the energy bandgap of GaAsNBi alloys.” It is published in the August 23, 2019 issue of Applied Physics Letters. U-M graduate researcher Jordan Occena, T. Jen and J.W. Mitchell are also authors on the paper.
An earlier, related paper is titled “Bi-enhanced N incorporation in GaAsNBi alloys.” It published in the June 15, 2017 issue of Applied Physics Letters.


Table of contents:
- What is a phenomenon under study?
- Why do we study phenomena?
- What is a phenomenon in biology?
- What is anchoring effect give example?
- What are the 3 dimensions of Ngss?
- What is the anchoring effect in psychology?
- Does anchoring really work?
- How do you stop the anchoring effect?
- What are the five keys to anchoring?
- When should you avoid anchoring?
- What is the anchoring rule in negotiation?
- Who should make the first offer?
- Why you should never split the difference?
- Who should make the first move in a negotiation?
- Why you should never accept the first offer?
- How do you negotiate?
- What are the 5 stages of negotiation?
- How do you ask for a lower price?
- How do you ask for a lower rent price?
What is a phenomenon under study?
A phenomenon (plural, phenomena) is a general result that has been observed reliably in systematic empirical research. In essence, it is an established answer to a research question. ... Phenomena are often given names by their discoverers or other researchers, and these names can catch on and become widely known.
Why do we study phenomena?
Often simple events, when looking at them through a scientific eye, can elicit curiosity and questions in students and adults. ... By having students observe and explain smaller related phenomena first, they can then be challenged to explain the larger and more complicated phenomenon.
What is a phenomenon in biology?
Important Biological Phenomena. Biology is the study of living organisms, including their structure, functioning, evolution, distribution and interrelationships whereas a Biological phenomenon is the series of chemical reactions or other events that result in a transformation.
What is anchoring effect give example?
Anchoring bias occurs when people rely too much on pre-existing information or the first information they find when making decisions. For example, if you first see a T-shirt that costs $1,200 – then see a second one that costs $100 – you're prone to see the second shirt as cheap.
What are the 3 dimensions of Ngss?
The term "three-dimensional learning" refers to the three pillars that support each standard, now called "performance expectations." These three dimensions are: Science and Engineering Practices, Crosscutting Concepts, and Disciplinary Core Ideas. You can use this rubric to evaluate your own curriculum for NGSS.
What is the anchoring effect in psychology?
The anchoring effect is a cognitive bias that describes the common human tendency to rely too heavily on the first piece of information offered. ... Once an anchor is set, other judgments are made by adjusting away from that anchor, and there is a bias toward interpreting other information around the anchor.
Does anchoring really work?
Anchoring is a powerful force, an unconscious response to information. It's not a guarantee of a win, but it is a factor to be aware of when you enter into any negotiations – or retail sales. Using it effectively, and knowing when it's being used on you, is critical in arriving at a satisfactory result.
How do you stop the anchoring effect?
Outsmart the biasAcknowledge the bias. Being aware of your bias is the first step. Know the weaknesses of your mind and anticipate prejudiced judgement. ... Delay your decision. The second step involves slowing your decision-making process and seeking additional information. ... Drop your own anchor.
What are the five keys to anchoring?
The Five Keys to Anchoring:The Intensity of the Experience.The Timing of the Anchor.The Uniqueness of the Anchor.The Replication of the Stimulus.Number of Times.
When should you avoid anchoring?
Important. You should never anchor in, or otherwise obstruct passage through, channels or areas such as launching ramps or any other high-traffic areas.
What is the anchoring rule in negotiation?
Answer: A well-known cognitive bias in negotiation, anchoring is the tendency to give too much weight to the first number put on the table and then inadequately adjust from that starting point.
Who should make the first offer?
Whoever makes the first offer, whether seller or buyer, is usually more effective in the negotiation. The power of first offers is strong thanks to the science of the anchor effect. Anchoring is an irrational part of human decision making—what's called a cognitive bias.
Why you should never split the difference?
The idea that we should approach social interactions as negotiations will feel distasteful to many. According to Voss, that is because we misunderstand what a negotiation is. ... Never Split the Difference provides the reader with a series of straightforward and actionable negotiating strategies.
Who should make the first move in a negotiation?
Common wisdom for negotiations says it's better to wait for your opponent to make the first offer. In fact, you may win by making the first offer yourself.
Why you should never accept the first offer?
Power Negotiators know that you should never say Yes to the first offer (or counter-offer) because it automatically triggers two thoughts in the other person's mind.
How do you negotiate?
5 Tips for Negotiating BetterMake the first offer. One of the best negotiating strategies is to seize control of the bargaining table. ... When discussing money, use concrete numbers instead of a range. ... Only talk as much as you need to. ... Ask open-ended questions and listen carefully. ... Remember, the best-negotiated agreement lets both sides win.
What are the 5 stages of negotiation?
Negotiation Stages IntroductionThere are five collaborative stages of the negotiation process: Prepare, Information Exchange, Bargain, Conclude, Execute.There is no shortcut to negotiation preparation.Building trust in negotiations is key.Communication skills are critical during bargaining.
How do you ask for a lower price?
5 Tips On How To Negotiate Fair Prices Without Offending The SellerBe Reasonable When Negotiating. ... If You Don't Have the Money, Don't Offer It. ... Ask For a Lower Price. ... Be Friendly. ... Don't Be Afraid to Move On.2
How do you ask for a lower rent price?
How to Negotiate Your RentAsk the landlord if rent price is open to discussion. ... Highlight your strengths as a tenant. ... Inquire about extending the lease. ... Offer to end the lease in the summer. ... Research the property's value. ... Be open to compromise. ... Negotiate directly, follow up in writing. ... Have a backup plan.
- Which of the following phenomena take place inside an optical Fibre?
- What are some weather phenomena?
- How does bronchitis differ from pneumonia?
- What do we mean by embargo?
- What is a wave phenomenon?
- What is the phenomenon known as aurora borealis?
- What is earthquake and its effects?
- What type of lung disease is pneumonia?
- What exactly is quantum computing?
- How do you call a girl beautiful without saying it?
You will be interested
- What is subordinate evaluation?
- What is sentence give me 5 examples?
- What does such mean in English?
- What are the benefits of philosophy?
- How do you appraise subordinates?
- How does pneumonia appear on a chest X ray?
- What does Psychogenesis mean?
- What is phenomenon in science?
- What is the opposite of phenomena?
- What is Zone phenomenon?


via UNSW Sydney
Quantum engineers from UNSW Sydney have removed a major obstacle that has stood in the way of quantum computers becoming a reality: they discovered a new technique they say will be capable of controlling millions of spin qubits – the basic units of information in a silicon quantum processor.
Until now, quantum computer engineers and scientists have worked with a proof-of-concept model of quantum processors by demonstrating the control of only a handful of qubits.
But with their latest research, published today in Science Advances, the team have found what they consider ‘the missing jigsaw piece’ in the quantum computer architecture that should enable the control of the millions of qubits needed for extraordinarily complex calculations.
Dr Jarryd Pla, a faculty member in UNSW’s School of Electrical Engineering and Telecommunications says his research team wanted to crack the problem that had stumped quantum computer scientists for decades: how to control not just a few, but millions of qubits without taking up valuable space with more wiring, using more electricity, and generating more heat.
“Up until this point, controlling electron spin qubits relied on us delivering microwave magnetic fields by putting a current through a wire right beside the qubit,” Dr Pla says.
“This poses some real challenges if we want to scale up to the millions of qubits that a quantum computer will need to solve globally significant problems, such as the design of new vaccines.
“First off, the magnetic fields drop off really quickly with distance, so we can only control those qubits closest to the wire. That means we would need to add more and more wires as we brought in more and more qubits, which would take up a lot of real estate on the chip.”
And since the chip must operate at freezing cold temperatures, below -270°C, Dr Pla says introducing more wires would generate way too much heat in the chip, interfering with the reliability of the qubits.
“So we come back to only being able to control a few qubits with this wire technique,” Dr Pla says.
The solution to this problem involved a complete reimagining of the silicon chip structure.
Rather than having thousands of control wires on the same thumbnail-sized silicon chip that also needs to contain millions of qubits, the team looked at the feasibility of generating a magnetic field from above the chip that could manipulate all of the qubits simultaneously.
This idea of controlling all qubits simultaneously was first posited by quantum computing scientists back in the 1990s, but so far, nobody had worked out a practical way to do this – until now.
“First we removed the wire next to the qubits and then came up with a novel way to deliver microwave-frequency magnetic control fields across the entire system. So in principle, we could deliver control fields to up to four million qubits,” says Dr Pla.
Dr Pla and the team introduced a new component directly above the silicon chip – a crystal prism called a dielectric resonator. When microwaves are directed into the resonator, it focuses the wavelength of the microwaves down to a much smaller size.
“The dielectric resonator shrinks the wavelength down below one millimetre, so we now have a very efficient conversion of microwave power into the magnetic field that controls the spins of all the qubits.
“There are two key innovations here. The first is that we don’t have to put in a lot of power to get a strong driving field for the qubits, which crucially means we don’t generate much heat. The second is that the field is very uniform across the chip, so that millions of qubits all experience the same level of control.”
Although Dr Pla and his team had developed the prototype resonator technology, they didn’t have the silicon qubits to test it on. So he spoke with his engineering colleague at UNSW, Scientia Professor Andrew Dzurak, whose team had over the past decade demonstrated the first and the most accurate quantum logic using the same silicon manufacturing technology used to make conventional computer chips.
“I was completely blown away when Jarryd came to me with his new idea,” Prof. Dzurak says, “and we immediately got down to work to see how we could integrate it with the qubit chips that my team has developed.
“We put two of our best PhD students on the project, Ensar Vahapoglu from my team, and James Slack-Smith from Jarryd’s.
“We were overjoyed when the experiment proved successful. This problem of how to control millions of qubits had been worrying me for a long time, since it was a major roadblock to building a full-scale quantum computer.”
Once only dreamt about in the 1980s, quantum computers using thousands of qubits to solve problems of commercial significance may now be less than a decade away. Beyond that, they are expected to bring new firepower to solving global challenges and developing new technologies because of their ability to model extraordinarily complex systems.
Climate change, drug and vaccine design, code decryption and artificial intelligence all stand to benefit from quantum computing technology.
Next up, the team plans to use this new technology to simplify the design of near-term silicon quantum processors.
“Removing the on-chip control wire frees up space for additional qubits and all of the other electronics required to build a quantum processor. It makes the task of going to the next step of producing devices with some tens of qubits much simpler,” says Prof. Dzurak.
“While there are engineering challenges to resolve before processors with a million qubits can be made, we are excited by the fact that we now have a way to control them,” says Dr Pla.
More from: University of New South Wales
The Latest Updates from Bing News & Google News
Go deeper with Bing News on:
Silicon quantum processor
- MIT’s New Analog Synapse Is 1 Million Times Faster Than the Synapses in the Human Brain
New Hardware Delivers Faster Computation for Artificial Intelligence, With Much Less Energy MIT engineers working on “analog deep learning” have found a way to propel protons through solids at ...
- New hardware offers faster computation for artificial intelligence, with much less energy
As scientists push the boundaries of machine learning, the amount of time, energy, and money required to train increasingly complex neural network models is skyrocketing. A new area of artificial ...
- New quantum encryption method could lead to truly secure communication
Researchers said their method of quantum encryption could lead to secure communication that is 'fundamentally beyond' an adversary's control.
- US-Irish partnership gets €3m to lay foundations of the quantum internet
Researchers in Ireland, Northern Ireland and the US aim to link quantum computers together over a quantum internet to boost their power.
- Best of Last Week—New phase of matter, a replacement for silicon, a better vaccine for omicron subvariants
It was a good week for physics as a team with members affiliated with several institutions in the U.S. created a strange new phase of matter in a quantum computer that acted like it had two dimensions ...
Go deeper with Google Headlines on:
Silicon quantum processor
Go deeper with Bing News on:
- Why Israel is moving to quantum computing
The Israel Innovation Authority (IIA) has selected Quantum Machines to establish its national Quantum Computing Center.
- Bosch’s new partnership aims to explore quantum digital twins
Bosch's partnership with Multiverse Computing is an example of how many legacy companies are exploring quantum computing today to prepare for more capable hardware.
- HCL Technologies to support Sydney-based universities on quantum computing research and development
HCL Technologies has signed a memorandum of understanding with four Sydney universities on how the Indian multinational IT services and consulting company will support quantum computing research and ...
- Inca Knots Inspire Quantum Computer
We think of data storage as a modern problem, but even ancient civilizations kept records. While much of the world used stone tablets or other media that didn’t survive the centuries, the ...
- New method of controlling qubits could advance quantum computers
Quantum computing, a field that relies on the principles of quantum mechanics to calculate outcomes, has the potential to perform tasks too complex for traditional computers and to do so at high ...


At SAND14 in San Jose, California, Quantum Physicist John Hagelin spoke about the theory that entangled particles are connected through wormholes – the rabbit hole of physics, providing a physical link to enable instantaneous tunneling through space. Let’s take a closer look at that perspective.
A wormhole, officially known as an Einstein–Rosen bridge, is any structure connecting two regions or areas otherwise distant or unrelated. It is a hypothetical topological feature of spacetime, fundamentally a shortcut through spacetime. A wormhole is much like a tunnel with two ends, each with separate points in spacetime. In principle, two widely separated black holes can be connected to each other and look like trumpet horns making shortcut through spacetime.
Try to visualize space as a two-dimensional (2D) surface. In this way, a wormhole can be pictured as a surface that leads into a 3D tube (the inside surface of a cylinder). The tube then re-emerges at another location on the 2D surface with a similar entrance hole. The actual wormhole would be equivalent to this, but with the spatial dimensions plus one. For example, instead of circular holes in a 2D plane, the two wormhole`s mouths could actually be spheres in 3D space.
Wormholes have long been discussed as a possible mode of interstellar travel and even of time travel. The recently released movie Interstellar is greatly inspired by this phenomenon. They are also fairly well-popularized by science fiction, especially Star Trek: Deep Space Nine, which depicts a large traversible wormhole that allows the characters to travel from familiar regions of space to a distant and unrelated area on the other side of the galaxy.
Wormholes and quantum entanglement
Two quantum-entangled particles always instantly adopt correlated values, no matter how much distance separates them. If a quantum-entangled pair is depicted as a pair of twins, as one twin is raising the right hand, the other invariably and simultaneously raises the left hand. Being able to explain this phenomenon through the wormhole connection, reduces the spookiness Einstein referred to when he talked about entanglement.
Few theoretical physicists have imagined a connection between the concept of entanglement and that of a wormhole, and a hypothetical connection between black holes that serves as a shortcut through space.
Juan Maldacena, theorist at the Institute for Advanced Study in Princeton, New Jersey, and Leonard Susskind, theorist at Stanford University in Palo Alto, California, have observed the entanglement of quantum states of two black holes - and pulling the black holes apart. When that happens, they argued, a bona fide wormhole forms between the two black holes. According to Maldacena and Susskind, it could also be possible to create a wormhole connection between two ordinary quantum particles such as quarks that make up protons and neutrons.
Kristan Jensen of the University of Victoria in Canada and Andreas Karch of the University of Washington, Seattle assume that the 3D space where the quarks reside is a hypothetical boundary of 4D world. In this 3D space, the entangled pair is connected with a kind of conceptual string. But in the 4D space, the string becomes a wormhole.
Julian Sonner of the Massachusetts Institute of Technology in Cambridge builds upon Karch’s and Jensen’s work. He observed that a quark-antiquark pair popping up produces a strong electric field which then sends it to the oppositely charged particles accelerating in opposite directions. Sonner also found that the entangled particles in the 3D world are connected with wormholes in the 4D world.
To arrive at this result, Jensen, Karch and Sonner use the so-called holographic principle, a concept invented by Maldacena stating that a quantum theory with gravity in a given space is equivalent to a quantum theory without gravity in a space with one less dimension that makes up the original space’s boundary. In other words, black holes inside 4D space and a wormhole between them are mathematically equivalent to their holographic projections existing on the boundary in 3D. These projections are essentially elementary particles that function according to the laws of quantum mechanics, without gravity and a string connecting them. The wormhole and the entangled pair don’t live in the same space, but mathematically they are equivalent.
Susskind and Maldacena argued that the original quantum particles reside in a space without gravity. In a simplified gravity-free 3D model of our world, there can’t be any black holes or wormholes. Susskind adds that the connection between a wormhole and entanglement in a higher dimensional space is a mere mathematical analogy. The wormhole and entanglement equivalence only makes sense in a theory with gravity.
However, Karch and his colleagues said that their calculations are an important first step toward verifying Maldacena and Susskind’s theory. Their toy model without gravity gives a concrete realization of the idea that wormhole geometry and entanglement can be different manifestations of the same physical reality.
Promoting his new book 'The Myth of Normal' on the Tim Ferris Show
Please enter your email and we’ll send you instructions to reset your password


A Bose-Einstein condensate is a state of matter in which atoms lose their individual identities and instead behave as a single entity. In this state, all of the atoms have the same wavelength, meaning they vibrate in unison. Bose-Einstein condensates are incredibly difficult to create and study, but they could potentially revolutionize computing.
Everywhere we look around ourselves, we see matter. The device you’re reading this article on, the air we breathe, along with all life on Earth is made up of matter. We can safely say that matter is everything composed of atoms. The reason we see matter taking so many different forms is because it exists in many different states. Generally, matter exists in a certain amount of states at classical conditions, but when subjected to extreme conditions, matter is found to behave in different states altogether.
One such state of matter, found at extremely critical conditions, was discovered by two legendary scientists, Satyendra Nath Bose and Albert Einstein. This state of matter was therefore given the name Bose-Einstein Condensate. First, however, to understand Bose-Einstein Condensate, we must look at the classical states of matter, refreshing how how atoms behave in them and how matter flows from one state to another.
Recommended Video for you:
The Change Of States Of Matter
Matter has many states in which it can exist. The state of matter depends on the interaction of atoms between one another, as well as the energy levels of every atom as a whole. Matter can change from one state to another when subjected to different temperatures and pressures. Under classical physical conditions, matter can exist in four states:
The best example for depicting changes in states of matter is water. Below 0°C, water exists in its solid state—ice. Upon heating ice above 0°C at standard pressure, it gets converted into liquid water. Upon heating liquid water above 100°C at standard pressure, we obtain steam, which is the gaseous form of water. Steam, when it undergoes the process of ionization, which adds or removes an electron to create ions, generates the plasma state of water.
The energy of atoms is the governing body to determine in which state of matter a substance is found. When we impart heat to atoms, we basically give them energy. That energy is absorbed by the atoms as they begin convert this energy into motion. This is essentially what we see during such change of states of matter. Atoms in solids have very little energy and vibrate with low amplitudes, which is why solids stay in one place. When we heat solids, we impart them with energy. The atoms then begin vibrating with more energy and higher amplitudes. This is when we obtain liquids and gases, both of which have a tendency to flow, rather than remain stagnant.
However, when we talk about Bose-Einstein Condensate, we are not talking about standard terms of physical conditions. Bose-Einstein Condensates are generally made in temperatures that are millions of times colder than space itself. Thus, to get a better understanding of the Bose-Einstein Condensate, we must go into the quantum physics of an atom.
A Dive Into The Quantum Realm
Quantum Physics is the branch of physics dealing with subatomic particles and all matter and energy at the smallest scales. Quantum Physics also describes the laws governing an atom.
In 1924, Louis-Victor de Broglie claimed that all matter had a wave-like nature. This actually laid the basis for Quantum Physics. What this meant was that all matter could exist like both a particle and a wave at the same time! The reason why we don’t see this wave particle duality very often is because the mass of all objects around us has millions of millions of million more mass than the subatomic particles quantum physics deals with. In short, the objects around us have so much mass that their wave nature is almost invisible, but in small objects like electrons, we see this phenomenon more plainly.
Quantum physics also states that each atom has its own identity. Each atom has its own unique wavelength (since it behaves like a wave) and has its own individuality as a particle. We’re able to distinguish one atom from another due to certain qualities, similar to how we can distinguish between two human beings. We must keep these laws in mind when talking about Bose-Einstein Condensate.
Turning The Microscope On The Bose-Einstein Condensate
Most of us know that there is no temperature lower than Absolute Zero, which is -273 °C or 0 K. Absolute Zero is that temperature at which atoms have no energy and cease motion entirely. So, what happens when you cool a gas with low density to temperatures only a fraction above Absolute Zero? Well, the answer to this question is… the Bose-Einstein Condensate!
It was found that upon cooling matter at temperatures just a whisker above 0 K, the material enters another state of matter, suitably named Bose-Einstein Condensate. We already know that when atoms are cooled to lower temperatures, they have lower energy levels. Thus, in the Bose-Einstein Condensate state, atoms have near-zero energy levels.
Remember the wave-particle duality of atoms covered in Quantum Physics? In a Bose-Einstein Condensate, all the atoms of a substance begin to exhibit a similar wavelength. These wavelengths then begin to overlap. At this point, the atoms undergo an identity crisis. Instead of having multiple different atoms exhibiting different wavelengths, we observe a single atom exhibiting a single wavelength. One atom cannot distinguish itself from another, so we consider the aforementioned single atom to be a “super atom”.
To put this very simply, the Bose-Einstein Condensate (BEC) is that state of matter where all the atoms of a particle begin to act as a single atom called a Super Atom. Unlike all the other states of matter, in the BEC, all the atoms vibrate in unison, that is, they all vibrate with the same wavelength with the same time period. This phenomenon could allow the BEC to revolutionize computation, making the realization of quantum computing possible. This concept is immensely tough to grasp and there is still a great deal of research going on related to it, but the BEC could open new and incredible doors of achievement in the world of physics.


Artificial Intelligence (AI) helps machines to learn from experience, adapt to new inputs, and perform human-like tasks. Machine Learning (ML) is a subset of AI that enables software programs to grow increasingly effectively, predicting outcomes without explicitly programming them. The ML algorithm anticipates new output values with preliminary data as input and the future of Software Development.
Meaning of AI and Machine Learning
The capacity of a digital computer or computer-controlled robot to accomplish activities often associated with intelligent individuals is called Artificial Intelligence (AI). The phrase endeavors to produce systems with human-like cognitive processes, such as the ability to reason, discover meaning, generalize, or learn from prior experience. AI refers to machine intelligence instead of human intelligence. Although no AI can accomplish the full range of jobs that an ordinary person can, specific AIs can match humans in specialized skills.
Machine Learning is a branch of artificial intelligence, which is the capability of a machine to replicate intelligent human behavior. AI systems simplify complicated tasks comparable to how people solve issues. ML is a modern breakthrough that has improved a wide range of industrial and professional procedures and our daily lives. AI is a subfield focusing on developing intelligent computer systems that can learn from accessible databases using statistical approaches.
Software development predictions for the future:
The future of software development is already here. And Software Development may be seen in the current patterns software development teams use.
- Innovation Will Spread
- Applications will become smaller, and hardware will become obsolete
- Quantum Computing Will Change Everything
- Software Will Be Proactive
- User Experience Will (Still) Be Number One
7 Stages of Machine Learning
Machine learning is used in software development to increase software accuracy and dependability by employing algorithms that recognize patterns, categorize data, and generate predictions. It aids in finding code mistakes that might lead to bugs and other issues.
- Collecting Data
Machines, as you may know, first learn from the data you provide them. So, at this point, we are gathering data to train the model.
- Preparing the Data
You must arrange your data after you receive it. The most fundamental component is the cleaning and changing it so we may use it.
- Choosing a Model
The first step in every machine learning project is to decide which model to utilize. Simple linear regression models to more complicated deep learning models are available.
- Training the Model
In this stage, we will train our model using labeled data and test it with new unlabeled data. We can also do feature engineering like discretization or dimensionality reduction here for accurate predictions.
- Evaluating the Model
In this stage, we compare our predictions to the actual data to see whether or not our model is correct.
- Parameter Tuning
Parameter Tuning is one of the essential tasks in machine learning because if your parameters are appropriately tuned, your model will be valuable, if not worse!
- Making Predictions
Forecasting about the future at this time is done. We employ a learning system trained on data with predetermined outputs.
Positive Changes that Machine Learning can bring to Software Development
The following are a few Positive Changes that Machine Learning can bring to Software Development:
- Detect Deviation from Coding Guidelines
ML in real-world applications helps to speed the anomaly detection process and save resources. It can occur not just after the fact but also in actual time. Real-time anomaly detection uses to increase security and resilience in fraud and cyber security areas.
- Obtain Code-Based Insights
- ML may give various essential insights, such as:
- How much legacy code do you have in your IT portfolio?
- Do you have any unmaintained code?
- How many apps do you have that need to be cloud-ready?
- Uncontainerized app percentage
- What is slowing down your development?
- How frequently do you repurpose code in your organization?
- Who are your top-performing programmers?
- How well does your team work together?
- What vital talents does your team lack?
- Machine Learning can help you with coding, code review, and testing
As a senior executive in a corporate IT division, you know that application development, code review, and testing are all manual, repetitive chores. On the other hand, ML provides a new generation of automation that goes well beyond the rule-based automation you have previously seen.
- Enhance Data Management
ML Models Function Successfully on Huge Data Machine learning models work effectively on big data, where they can learn a fantastic range of patterns and trends. Assuring quicker reaction time and reduced memory usage becomes more difficult for data science specialists. Data integration from numerous sources is easier with ML than with classical data indexing. Furthermore, machine learning aids in data infrastructure administration, allowing data engineers to manage data pipelines more effectively.
The Future of Software Development
ML is used in software development to increase software accuracy and dependability by employing algorithms that recognize patterns, categorize data, and generate predictions. It aids in finding code mistakes that might lead to bugs and other issues. ML is also used to forecast the future of software development by the occurrences based on past user behavior or data. The process of employing ML algorithms to improve software quality is known as machine learning development.
In other words, it is a method of automatically identifying and repairing mistakes in your code, allowing it to function more smoothly and satisfy better standards. AI may increase human creativity, liberate humans from complex or pointless duties, and even replace humans in risky positions. Even with this, the future of software development is still possible because AI will not replace developers or programmers anytime soon. However, it may undertake code and creating activities in the future.
The advancement of AI technology will work for hand in hand with the digitalization and intelligent upgrading of the sector, resulting in a bright future of software development with limitless potential. Artificial intelligence is the most significant achievement in the realm of software development. Because of its superior neural algorithms, AI-assisted automation minimizes manual participation, reduces complexity, and can handle real-world processes.


Steven Galbraith once told me that he expects mathematicians to teach RSA long after the world has migrated to post-quantum algorithms; because it is so easy to explain. Arguably, LWE is easier to explain than RSA but the Approximate Greatest Common Divisors problem (AGCD) is even easier than that and requires only scalars. Thus, it is a nice post-quantum alternative for an undergraduate mathematics module. Someone should perhaps write an undergraduate mathematics textbook introducing cryptography using Approximate Common Divisors.
To set a baseline, let’s start with recalling naive RSA.
- KeyGen. The public key is and the private key is , with
- where and prime,
- coprime to and
- such that .
This naive version of RSA only achieves a basic form of security — OW-CPA — even against classical adversaries: it is hard to recover random messages when eavesdropping. Kids, always implement RSA-OAEP. It is easy to see that an adversary that can factor large integers can break RSA: knowing and permits to compute which permits to compute . (It should be noted, though, that this does not mean an adversary has to factor to solve RSA.) The best known classical algorithm for factoring is the Number Field Sieve (NFS). It has a super-polynomial but sub-exponential complexity of
operations. On the other hand, and this is the reason why we care about post-quantum cryptography, an adversary with access to a quantum computer with
gates can factor using Shor’s algorithm.
Greatest Common Divisors
Now, to pivot to GCDs, what if two or more users generate moduli and , i.e. moduli with shared factors? We assume that factoring each of or is hard, but computing , i.e. the largest integer dividing both and , reveals (or a small multiple). We can compute greatest common divisors using the Euclidean algorithm:
def gcd(a, b): if b == 0: return a else: return gcd(b, a % b)
Approximate Greatest Common Divisors
Thus, computing GCDs can break RSA with poor randomness. On the other hand, adding a bit of noise to the problem – going from Greatest Common Divisors to Approximate Greatest Common Divisors – makes the problem (for all we know) hard, even on a quantum computer.
The Approximate GCD problem is the problem of distinguishing
from uniform with (, and are secret). For the problem to be hard, we require , and .
We can build public-key encryption from the AGCD problem as follows:
- KeyGen. The public key is a bunch of AGCD samples where the errors are multiples of 2, i.e. and the private key is . It can be shown that all errors being multiples of two does not weaken security.
- Enc. For output with , i.e. do a random zero-one combination of the samples in the public key and add . This effectively samples a new AGCD sample and adds .
- Dec. , i.e. take the ciphertext mod which produces and the take that mod 2 to recover .
If the AGCD problem is hard then this encryption scheme is IND-CPA secure. That’s better than merely OW-CPA but to achieve security against active attacks we would need to apply a generic transform.
How would we attempt to solve the AGCD problem? Following the mantra I first heard from Alexander May – first you try exhaustive search, then you try a time-memory trade-off, then you think – let’s start with exhaustive search.
Given and we know that
and we can simply guess and which costs GCD computations.
Thus, under this attack would get way with smaller but there is time-memory trade-off. The basic idea is the realisation that we can replace GCDs by multiplications, if or then we have and . That is, we can compute
for all guesses with . The cost of this is GCD computations (yay!), multiplications (boo!), so it does not give us much of a saving. Yet, this can be extended to a time-memory trade-off which recovers with overwhelming probability in time . This is why we require .
Finally, a lattice attack. Given and , consider
and note that . So there is a linear combination of and that produces something small. This is all nice and well, but we don’t know which to pick! Still, let’s generalise this observation and write it down in matrix form.
As before, multiplying on the left by the vector gives
which is a vector with small coefficients compared to the .
The set of all integer-linear combinations of the rows of matrix is called the lattice spanned by (the rows of) that matrix. Finding short vectors in lattices is assumed to be hard, even on a quantum computer.
While the above only sketches that we can break AGCD if we can find short vectors (similar to RSA and factoring), it is also possible to show that if you can solve the AGCD problem then we can also find short vectors in lattices (in contrast to RSA and factoring!). That is, if there is an algorithm efficiently solving the AGCD problem then there exists an algorithm which solves the Learning with Errors problem with essentially the same performance. Then, second step, if there is an algorithm efficiently solving the LWE problem then there exists a quantum algorithm which solves worst-case SIVP instances, i.e. finds short vectors in arbitrary lattices.
PS: Homomorphic encryption
Given with , we can compute
to get and . We can also compute
to get . That is, we can compute
XOR which suffice to build any gate. Thus, we can compute on encrypted data.


Dieser Artikel ist älter als ein Jahr!
Earlier this year, the University of Vienna’s Quantum Science and Technology department published their findings regarding a highly secure blind computation process that combines the power of quantum computing and quantum cryptography. In addition to being the world’s first demonstration of this theory, the team notes that this process can one day have huge implications on internet security, particularly in the growing field of quantum cloud computing.
And while this experiment is remarkable in it’s own right, I’m not really sure how many people outside the realm of quantum physicists, truly understand what and how this experiment has a real world application. To this end, I recently sat down with Stefanie Barz, team lead on the experiment to try to put things into a real world perspective.
They use laser beams
To begin, Stefanie provided me with an exclusive view of the quantum computer used to perform the experiment, including the laser array (Yes, they use laser beams!) used to produce the photons that will eventually be used to carry the data. To be clear, this laser array is a component used to produce the necessary photons that are then sent to the quantum computer. She explained that this array is responsible for entangling photons, meaning that they are in a certain state whereby they share a complex connection to each other. Researchers then measure one state of the photon, thus changing the state of said photon and affecting the state of the second photon. Physicists refer to this connection as super correlation, with Einstein referring to the process as “spooky action at a distance,” or “spukhafte Fernwirkung”. It’s precisely this interaction between particles that makes quantum computing far more efficient from the classical process we’re all familiar with. By capitalizing on the ability of quantum particles to be in more than one state at the same time, this allows the computer to perform any number of possible solutions to a given problem simultaneously.
From green to red
To create these entangled photons, Stefanie and her team use the laser beam to convert the wavelength from green to red, and then on to a blue beam. From this blue beam, the entangled photons are then routed through optical fibers and sent to the quantum computer to be further processed into a cluster state. The cluster state is then used to create the “blind qubit” that will then go on to be measured by the quantum computer.
Granted, that might seem like a whole lot of work (and power – did I mention lasers?) just to create few photons, and thus qubits, but keep in mind that we’re not talking about sending a typical email here. What Stefanie and her team have done is create an absolutely secure form of data processing that cannot be intercepted and understood. In addition to the ultra-secure method of transmission and encryption, the end computer is also unable to detect what it is actually processing.
Now before you start clamoring for your very own quantum computer to send completely secure emails, keep in mind that these devices are still in their infancy. A practical, real-world quantum computer is still far off, as the one I viewed consumed an entire room, and performed only a simple, yet highly effective computation.
Expensive & rare
If and when quantum computers do reach a practical level, it’s a fair statement to make that they’ll be quite expensive, and very rare. Enter cloud computing. With the usage of cloud computing growing on a daily rate, instead of needing their own quantum computer, researchers, (evil?) scientists, and others from around the world could theoretically rent or purchase computational time on said devices. Obviously, if you’re in need of the services of a quantum computer, there’s a good chance that you’d really rather not have others knowing exactly what you’re working on. Thus the need for the blind computation, and absolutely secure data processing.
Quantum computers do contain entangled qubits, therefor; simply generating and sending qubits isn’t going to solve this security issue fully. What Stefanie and her team have done is add an additional layer of security to this already confounding method of data transfer.
The random code
The trick here is a series of what appears to be random bits of code, but is in fact pre-encrypted by the sender. This “random” series of data is a form of photon polarization (vertical or horizontal), and remains encrypted throughout the calculation, and is still able to be processed by the quantum computer (although the computer has no idea what it is processing). However, if an eavesdropper where to intercept the data anywhere along the transmission path, they would have no way of knowing exactly how to put the encryption (polarization sequence) together to make any sense of the data. Having created the original encryption, the end receiver can then interpret the results, resulting in an absolutely secure form of data processing.
Two quantum algorithms in test
In the demonstration conducted at the University of Vienna, Barz and her team tested two quantum algorithms; Deutsch’s, which detects certain regularities in mathematical functions, and Grovers’, which can search an unsorted database (think phone book). They created the above-mentioned “spooky action at a distance” state of photons, and encrypted their data transmission. Having received the photons and created an entangled cluster state, the quantum computer then carried on and began solving the problem. However, because of this extra layer of polarized encryption, there was no way to determine exactly what the computer was doing and/or processing, thus proving the security of their test. The team had to wait until the results were returned to discover if the entire process had actually worked.
It’s also worth noting that this level of security is a two-way street. Meaning, those that are responsible for, or even own, a quantum computer are most likely quite protective of their asset. By providing this blind computational process, the sender of the data would have no way of peering into the inner workings of the computer processing their request, and of course, vice versa.
Who needs a quantum computer?
You and I are probably not going to have any need for a quantum computer in the near future, nor will we be sending data that could cause unrest in certain parts of the world, (sorry, pr0n doesn’t count). With that said, while Stefanie denies any contact, I can’t help but wondering if any government or military organizations have been in touch, as this is the absolute perfect application for such computational power and data transmission. Yes, the blind computational demonstration is a slightly-over-the-top form of secrecy, but in today’s world, there’s a perfect German expression, “Sicher ist Sicher.” (Better safe than sorry).
PhD candidate Stefanie Barz of Vienna’s Quantum Science and Technology department


Richard Feynman was an American physicist who contributed significantly to developing quantum mechanics and quantum computing. Feynman was born in 1918 in New York City and received his PhD in physics from Princeton University in 1942. He is well known for his work in quantum electrodynamics (QED), which he developed in the 1940s and 1950s but crucially also for his work towards the ideas of Quantum Computing and even Nanotechnology. Richard Feynman was born on May 11, 1918, in New York City, United States.
Richard Feynman was a physicist who significantly contributed to the development of quantum mechanics and quantum computing. Feynman is best known for his work in quantum electrodynamics (QED), which he developed in the 1940s and 1950s.
The connection to Quantum Computing
In 1982, Feynman gave a lecture at the MIT Computer Science and Artificial Intelligence Laboratory. He proposed using quantum mechanical phenomena to perform calculations that would be impractical or impossible using classical computers. This idea was later developed into the field of quantum computing. Feynman’s ideas and work continue to influence the field of quantum computing, and he is often considered one of the founders of the entire field.
Feynman’s idea was to build a controllable quantum environment and use it for analogue quantum computations of things that are hard to simulate. Simulating complex systems often becomes an issue for systems which involve many-body interactions.
Simulating a single electron is relatively simple. It is in either state A or state B. We may even say spin-up or spin-down to denote the two possibilities. With two electrons, you have the possibility of having both in state A, both in B, one in A and the other B, or vice versa – a total of four probabilities (AA, AB, BA, BB). With ten electrons, this rises to 1,024 probabilities (2 to the power 10), and 20 electrons have 1,048,576 combinations. Consider how a potential drug molecule binds to a receptor, and you have some idea about the complexity and possible combinations that must be considered. Simulating, therefore, by conventional means becomes “computationally expensive” when you have potentially hundreds of atoms and, consequently, thousands of electrons.
But the systems that a scientist wants to investigate often have millions of electrons, and the number of probabilities becomes unworkable. In the late 1970s, Feynman began considering this problem. In a paper published in 1982, “Simulating Physics with Computers“, he postulated that to simulate quantum systems, you would need to build quantum computers. This may seem a bit of a misnomer, that quantum simulations require physical quantum systems, but this is now considered one of the very purposes of quantum computers where there are not enough accessible states available in conventional classical simulations.
Richard Feynman was also interested in using quantum mechanics to perform calculations. In 1982, he gave a lecture at the MIT Computer Science and Artificial Intelligence Laboratory in which he proposed the idea of using quantum mechanical phenomena to perform calculations that would be impractical or impossible using classical computers. This idea was later developed into the field of quantum computing.
Feynman received the Nobel Prize in Physics in 1965 for his contributions to developing QED (Quantum Electro Dynamics). He died in 1988, but his ideas and work continue to influence the field of physics and the development of quantum computing.
The connection to Nanotechnology
In 1959, Feynman gave a lecture at the American Physical Society meeting in which he discussed the possibility of manipulating and arranging individual atoms and molecules to create new materials and devices. This lecture, known as the “There’s Plenty of Room at the Bottom” speech, is often considered the starting point for nanotechnology.
Nanotechnology is the study and application of tiny things and manipulation of individual atoms and molecules. It involves creating and using materials, devices, and systems with unique properties because of their small size. According to the National Nanotechnology Initiative, Nanotechnology is science, engineering, and technology conducted at the nanoscale, which is about 1 to 100 nanometers.
Nanotechnology or Nanotech has the potential to revolutionize many fields, including electronics, medicine, energy production, and materials science. For example, nanotechnology could create more powerful and efficient computer processors, develop new and more effective drugs, and create more robust and lighter materials (nanomaterials). Some have even postulated nanomachines that can get inside the human cell and fix and repair it as needed.
To give the full title: “There’s Plenty of Room at the Bottom: An Invitation to Enter a New Field of Physics” was a lecture given by physicist Richard Feynman at the annual American Physical Society meeting at Caltech on December 29, 1959.
We can even link nanotechnology and quantum computing, for the techniques that researchers use to develop the latest qubits (the devices that do the computing) are often nanoscale and require fabrication at ever tinier length scales. Just as traditional transistors in microprocessors developed by the likes of Intel, AMD and NVIDIA are getting smaller, so too are the qubits (the quantum analogue of the transistor). For example, quantum dots can be used as semiconducting qubits, where the spin of electrons is used as the “switching unit” and can be operated upon.
In 1975, Richard Feynman and his wife, Gweneth Howarth, purchased a Dodge Tradesman Maxivan. They had it decorated with Feynman diagrams, which are symbols that Feynman himself had created to depict complex particle interactions through simple lines and loops. While it might appear arrogant to display one’s intellectual accomplishments in this way, Feynman’s daughter Michelle believes that the decorations on the van reflected Feynman’s passion for physics. The van still exists and has been lovingly restored.
The Manhattan Project
Richard Feynman was a member of the team of scientists who worked on the Manhattan Project, which was the Allied effort during World War II to develop the first nuclear weapons. Feynman was a relatively junior (at age 24) member of the team, but he made significant contributions to the project, particularly in developing the first successful atomic bomb. When he was at Princeton, Feynman was recruited for the theoretical division of the Manhattan Project. Feynman was present at the first detonation of the atomic bomb. It is thought that radiation exposure may have contributed to his death at 69 from abdominal cancer.
There is no question that Richard Feynman inspired generations of scientists, even now, almost four decades after his death. He leaves a legacy of radical thought and new innovative ways to think about problems and has impacted two significant fields: nanotechnology and quantum physics. If you want to read more about his work, I can suggest the following books Richard Feynman has authored:


In quantum teleportation, the properties of quantum entanglement are used to send a spin state (qubit) between observers without physically moving the involved particle. The particles themselves are not really teleported, but the state of one particle is destroyed on one side and extracted on the other side, so the information that the state encodes is communicated. The process is not instantaneous, because information must be communicated classically between observers as part of the process. The usefulness of quantum teleportation lies in its ability to send quantum information arbitrarily far distances without exposing quantum states to thermal decoherence from the environment or other adverse effects.
Although quantum teleportation can in principle be used to actually teleport macroscopic objects (in the sense that two objects in exactly the same quantum state are identical), the number of entangled states necessary to accomplish this is well outside anything physically achievable, since maintaining such a massive number of entangled states without decohering is a difficult problem. Quantum teleportation, is, however, vital to the operation of quantum computers, in which manipulation of quantum information is of paramount importance. Quantum teleportation may eventually assist in the development of a "quantum internet" that would function by transporting information between local quantum computers using quantum teleportation .
Below is a sketch of an algorithm for teleporting quantum information. Suppose Alice has state C, which she wants to send to Bob. To achieve this, Alice and Bob should follow the sequence of steps:
1) Generate an entangled pair of electrons with spin states A and B, in a particular Bell state:
Separate the entangled electrons, sending A to Alice and B to Bob.
2) Alice measures the "Bell state" (described below) of A and C, entangling A and C.
3) Alice sends the result of her measurement to Bob via some classical method of communication.
4) Bob measures the spin of state B along an axis determined by Alice's measurement
Since step 3 involves communicating via some classical method, the information in the entangled state must respect causality. Relativity is not violated because the information cannot be communicated faster than the classical communication in step 3 can be performed, which is sub-lightspeed.
The idea of quantum teleportation, which can be seen in the mathematics below, is that Alice's measurement disentangles A and B and entangles A and C. Depending on what particular entangled state Alice sees, Bob will know exactly how B was disentangled, and can manipulate B to take the state that C had originally. Thus the state C was "teleported" from Alice to Bob, who now has a state that looks identical to how C originally looked. It is important to note that state C is not preserved in the processes: the no-cloning and no-deletion theorems of quantum mechanics prevent quantum information from being perfectly replicated or destroyed. Bob receives a state that looks like C did originally, but Alice no longer has the original state C in the end, since it is now in an entangled state with A.
Which of the following is true of quantum teleportation?
1) Quantum information is transferred between states
2) The teleported particle is physically transferred between locations
3) A quantum state is cloned between observers
4) Quantum information is permanently removed from the system
As a review, recall the Pauli matrices:
The spin operators along each axis are defined as times each of for the axes respectively.
These Pauli matrices are used to construct Bell states, an orthonormal basis of entangled states for the tensor product space of spin- particles:
Measurements that project tensor products of spin states onto the Bell basis are called Bell measurements.
Now, follow the algorithm sketched in the previous section. Suppose Alice starts with state C, which she wants to send Bob. State C can be written in the most general form:
with and normalized complex constants.
1) Generate an entangled pair of electrons A and B in the Bell state:
The state of the full system of three particles is therefore . This is a product state between entangled pair AB and non-entangled C.
2) Alice measures the Bell state of AC, entangling A and C while disentangling B. The process of measuring the Bell state projects a non-entangled state into an entangled state, since all four Bell states are entangled.
Expanding Alice's full original state, she starts with:
Multiplying out the states and changing to the Bell basis of A and C, this state can be rewritten:
When Alice measures the Bell state of A and C, she will find one of , each with probability . Whichever she measures, the state of particle B will be after measurement.
3) To send Bob the state of particle C, therefore, Alice does not need to send Bob the possibly infinite amount of information contained in the coefficients and which may be real numbers out to arbitrary precision. She needs only to send the integer of the Bell state of A and C, which is a maximum of two bits of information. Alice can send this information to Bob in whatever classical way she likes.
4) Bob receives the integer from Alice that labels the Bell state that she measured. After Alice's measurement, the overall state of the system is:
Bob therefore applies to the disentangled state on his end, by measuring the spin along axis . Since for all , Bob is left with the overall state:
Bob has therefore changed the spin state of particle B to:
which is identical to the original state of particle C that Alice wanted to send. The information in state C has been "teleported" to Bob's state: the final spin state of B looks like C's original state. Note, however, that the particles involved never change between observers: Alice always has A and C, and Bob always has B.
- Pirandola, S., & Braunstein, S. Physics: Unite to build a quantum Internet. Retrieved from http://www.nature.com/news/physics-unite-to-build-a-quantum-internet-1.19716
- Debenben, . quantum teleportation diagram. Retrieved from https://commons.wikimedia.org/w/index.php?curid=34503176


At the Consumer Electronics Show (CES) this year, IBM announced that its quantum computer Raleigh has achieved the goal of doubling its Quantum Volume to 32, up from 16 last year. As a matter of fact, IBM has been successful in doubling its systems’ Quantum Volume each year since 2017 when its computer Tenerife demonstrated a Quantum Volume of 4. This simply means the computer has doubled its potential for solving more real-world, complex problems.
Even for those of you who are well-acquainted with the nuances of traditional computing, the terms ‘quantum computer’ or for that matter ‘Quantum Volume’ may sound Greek. And that’s pretty understandable. Quantum computing involves the application of quantum physics, which may be too abstruse for many of us.
Here, in this article, we shall try to deal with the subject of quantum computing in the simplest manner possible. So, let’s begin.
Before we dwell on the basics of a quantum computer, let’s understand how a conventional computer works. This way, you will be able to appreciate the difference better.
Any traditional computer (like the one you use in your office or home) stores and processes information using switches called transistors. These switches are similar to the ones you use at home for turning on or off your electrical appliances. This transistor can either be on or off. If it’s on, it can be used for storing number 1. If it’s off, it can store 0.
Long strings of 0s and 1s can be used to store any number, text or symbol. Each of the 0s and 1s is referred to as a binary digit (bit) and using a string of eight bits, you can store 255 different characters (A-Z, a-z, 0-9 and commonly used symbols). A conventional computer calculates by using circuits called logic gates that are made of transistors connected together.
Quantum computers do not use bits to store information. They use quantum bits or qubits. Each qubit can not only be 0 or 1 but also be both 0 and 1 at the same time or even an infinite number of values in between. Now, why does this happen and what does it mean?
This happens because quantum bits are subatomic particles i.e. protons and electrons. Because they are subatomic particles, they do not follow the laws of classical physics. They follow quantum physics instead. One of the basic tenets of quantum physics is the principle of superposition where a particle can exist in multiple states simultaneously, at least until the state is measured and collapses into one.
So, quantum bits can exist in multiple states (including 0 and 1) at the same time. This simply means they can store multiple values at once and process them simultaneously. So, instead of working in a sequence (i.e. doing one thing only after the previous one is finished), a quantum computer can work in parallel (i.e. doing several things simultaneously). This property makes it a million times faster than a conventional computer.
When a quantum computer starts crunching through a problem, the qubits are in their hybrid state. When the solution is found, the qubits collapse into one of the possible states (0 or 1) for returning the solution.
For most of us, the existence of quantum computers will make no difference simply because the tasks we implement in our day-to-day lives can be easily accomplished with conventional computers. Quantum computers will only affect (in case they do) the elite research teams.
Certain problems are so complex that they cannot be solved using traditional computers. They have too many possible solutions and the solution is derived through trial and error, guessing until the answer is found. It would take traditional computers thousands of years to arrive at the correct solution.
The superposition property shown by quantum bits will slash the guessing time exponentially. While traditional computers make one guess at a time, quantum computers will make multiple guesses, thanks to their multiple states.
So, calculations requiring complex guesswork such as the simulation of atomic structures will be easy to carry out. As a result, scientists will be able to create new compounds for use in manufacturing.
Researchers will also be able to simulate other complex systems like genetic mutation patterns, financial models and economic market forces. This will offer impetus to path-breaking research work in genetics, finance and economics.
Yes, like every revolutionary technology, quantum computers have their dark side too. Quantum computing can have significant repercussions on cryptographic encryption that secures our computers and underpins modern internet communication.
Robust encryption is what keeps our data and messages secure. But in the presence of exceptionally powerful quantum computers, the odds of bad actors breaking these encryption algorithms to access sensitive information increase exponentially.
Though they were proposed around 3 decades back, the concept of quantum computers remains largely theoretical even to this day. Starting with some breakthroughs in the year 2000, a Canadian company D-Wave Systems announced in 2011 that it has created a 128-qubit computer.
Later, in 2016, Isaac Chuang from MIT and scientists from the University of Innsbruck developed a 5-qubit, ion-trap computer that could potentially evolve into a powerful encryption buster.
Post-2016, more and more breakthroughs were unveiled in this field. In 2017, for instance, Microsoft announced that it has developed a quantum development kit including a special language Q#, especially for quantum computing applications.
The very next year, Google unveiled Bristlecone, a 72-qubit quantum processor that could be harnessed for research in areas of quantum simulation, optimization, and machine learning.
In October 2019, Google claimed to have attained what it called ‘quantum supremacy’. As per Google, their newly developed 54-qubit processor, Sycamore needed just 200 seconds to compute a super-complex algorithm which even the world’s fastest supercomputer would have taken 10,000 years.
This claim was disputed by IBM. As per IBM, the said calculation could be completed by an existing computer in less than 2.5 years and not 10,000 years, as claimed by Google.
Figure 2: The chart shows the quantum computing systems produced by organization(s) in qubits between 1998 and 2019. (Source: Statista)
So, while we have seen many breakthroughs happening in quantum computing of late, we will need to wait for several decades before we witness its practical applications. Google may have claimed to have achieved the pinnacle of quantum computing, but what Sycamore performed was just a benchmark test that has no real-world applications, so Google can’t deploy it for solving practical problems anytime soon.
Besides, quantum bits are stable only at cryogenic temperatures, so only governments and large corporations like IBM and Google can afford to keep a quantum computer at their premises. The rest of us would have to depend on cloud computing.
Need more such blogs on quantum computing? Let us know in the comment section below. Thanks for reading.


Researchers at the Indian Institute of Science (IISc) have created a novel hybrid of two remarkable materials called graphene and quantum dots, in a breakthrough that may inspire highly efficient and controllable next-generation displays and LEDs.
Quantum dots are semiconductor nanocrystals with the potential to revolutionize diverse technologies, from photovoltaics and medical imaging to quantum computing. They can absorb UV light and produce sharp, bright colours, making them especially attractive for next-generation TVs, smartphones and LEDs. However, they are poor electrical conductors, and therefore inefficient to use in devices on their own. To improve their efficiency, researchers have tried combining them with graphene, an excellent conductor. Adding graphene would also confer the ability to tinker with the output even after fabrication, or turn the device on and off at will.
Although the combination works well for photo-detectors and sensors, it is practically useless for displays and LEDs, because quantum dots lose their ability to emit light when fused with graphene. By modifying some experimental conditions, IISc scientists have found a way to eliminate this effect, and create a highly efficient and tunable hybrid material.
The results, published in ACS Photonics, open up possibilities for a new generation of state-of-the-art displays and LEDs.
Quantum dots are extremely tiny particles with properties vastly superior to conventional semiconductors. When activated by UV light, they can produce visible light in different colours depending on their size. Small dots produce blue light, for example, while large ones radiate red.
Quantum dots absorb light very well, but they are poor electrical conductors; quantum-dot based devices that convert light to electricity are therefore not very efficient. Graphene, on the other hand, is almost transparent to light, but it is an excellent electrical conductor. When the two are combined, graphene could, in principle, quickly pull the absorbed energy away from quantum dots — cutting down energy loss — and convert it to an electrical signal, for example. This makes it possible to create devices such as photo-detectors with extremely high efficiency.
“You get the best of both,” says senior author Jaydeep Kumar Basu, Professor, Department of Physics, IISc.
On the flip slide, the energy transfer to graphene leaves quantum dots with almost no energy left to emit light, making it impossible to use them in displays or LEDs.
“That is one area where the application of these hybrid materials has not taken off, because of this effect,” says Basu. “Graphene acts like a sponge as far as the quantum dots are concerned. It does not allow any emission.”
Basu’s team tried to overcome this “quenching” effect by bringing into play a phenomenon called superradiance. When individual atoms or emitters (such as quantum dots) in a layer are excited, each one emits light independently. Under certain conditions, all the atoms or emitters can be made to emit light cooperatively. This produces a very bright light, with an intensity significantly greater than the sum total of individual emissions.
In a previous study, Basu’s team was able to bring about superradiance in a thin layer of quantum dots by combining it with metal nanoparticles under certain experimental conditions. They recreated those conditions in the new quantum dot-graphene hybrid devices to successfully bring about superradiance, which was strong enough to compensate for the quenching. Using models, they found that this happens when individual quantum dots are 5 nm or less apart, and the quantum dot layer and graphene are separated by a distance of 3 nm or less.
“We have shown for the first time that we are able to get away from this ‘sponge’ effect, and keep the emitters alive,” says Basu.
When superradiance dominated, the intensity of light emitted in the presence of graphene was also found to be three times higher than what could have been achieved using quantum dots alone.
“The advantage with graphene is that you can also tune it electrically,” says Basu. “You can vary the intensity by simply changing the voltage or the current.”
The study also opens up new avenues for research on understanding how light and matter interact at the nanoscale, the authors say.
Reference: Electrically Tunable Enhanced Photoluminescence of Semiconductor Quantum Dots on Graphene, published in ACS Photonics, June 2017.
The study was funded by the Department of Science and Technology (Nanomission).
Jaydeep Kumar Basu
Department of Physics
Indian Institute of Science (IISc)


The quantum computer has been making waves in the tech world recently. It is a computer that uses quantum mechanics to function, and it is believed to be far more powerful than traditional computers.
What is a quantum computer:
A quantum computer is a computer that uses quantum mechanical phenomena to perform calculations. These computers are different in many ways from the computers that are in use today. For example, a quantum computer can be in multiple states simultaneously, whereas a classical computer can only be in one state at a time. This allows quantum computers to perform several calculations at once.
How does a quantum computer work:
This computer consists of qubits, which are units of information that can exist in more than one state simultaneously. A qubit is like an atom or particle that can exist in more than one state or energy level at the same time. In contrast, traditional bits can store only two values, 0 and 11
Why is the quantum computer important:
It has the potential to revolutionize computing. It is believed that these computers will be able to solve problems that are beyond the capabilities of classical computers. It could also be used to create new materials and drugs, and to develop new ways of communication.
How far along is quantum computer development:
Currently, there are a few working prototypes of quantum computers. However, these computers are not yet powerful enough to perform most tasks that a classical computer can do. Researchers are working on developing more qubits and increasing the power of these computers.
What are some real-world applications for quantum computers:
These computers have the potential to be used in a variety of fields, including finance, medicine, and logistics. For example, they could be used to develop new financial products, design more effective drugs, and create better algorithms for routing traffic.
Are quantum computers dangerous:
Some experts have raised concerns about the potential misuse of these computers. It is possible that these computers could be used to hack into secure systems or to create new viruses. However, these concerns are largely speculative at this point.
What are the limitations of quantum computers:
Currently, these computers are very limited in terms of their power and capacity. They also require extremely low temperatures and a high degree of stability. As a result, these computers are not yet able to perform most tasks that classical computers can do.
Despite these limitations, quantum computers have the potential to revolutionize computing and change the world as we know it.
Do you think quantum computers are the future:
Yes, I believe that these computers are the future of computing. These computers have the potential to solve problems that are currently unsolvable, and they could have a profound impact on many different fields. I think that these computers will become more powerful and more widely used in the coming years. Thanks for reading! I hope you found this article interesting. a quantum computer, qubits, quantum mechanics
Did you know that a qubit is a unit of information that can exist in more than one state simultaneously? This allows these computers to perform several calculations at once! Quantum computers
Here are 10 fascinating facts about the quantum computer:
1. The quantum computer was first developed in 1994 by Peter Shor.
Shor’s algorithm, which is a quantum algorithm for factoring large numbers, was developed in 1994. This algorithm demonstrated that computers could be used to perform calculations that are beyond the capabilities of classical computers.
Since then, these computers have been developed by a number of different companies and organizations, including IBM, Google, and Microsoft.
2. this computer can solve problems much faster than traditional computers.
Traditional computers use bits, which can store a maximum of two values (0 or 11). These computers use qubits, which can store a much larger number of values. This allows these computers to perform calculations much faster than traditional computers.
Fact, it is estimated that this computer could perform certain tasks in seconds that would take traditional computers billions of years to complete!
These computers are not yet powerful enough to perform most tasks that a classical computer can do. However, they have the potential to revolutionize computing and change the world as we know it.
3. This computer can hold much more information than traditional computers.
This is because qubits can exist in multiple states simultaneously. This allows these computers to perform several calculations at once! Traditional computers use bits, which can store only two values (0 or 11). In contrast, qubits can store a virtually unlimited amount of information. As a result, these computers have the potential to be much
4. This computer is immune to hacking and viruses.
This is because these computers use qubits, which are units of information that can exist in more than one state simultaneously. This makes it impossible for hackers to access the data stored on a quantum computer.
Additionally, quantum computers are not susceptible to viruses due to their qubit-based architecture.
5. This computer could eventually lead to the development of artificial intelligence.
These are just a few of the many fascinating facts about this computer! If you found this article interesting, be sure to check out our other blog posts about computing. a quantum computer, qubits, quantum mechanics
6. These computers are currently being used for data encryption and security purposes
In the future, quantum computers could be used for a variety of tasks, including weather forecasting, early detection of disease, and large-scale simulation of molecules.


The equivalent to a wormhole in space–time has been created on a quantum processor. Researchers in the US used an advanced quantum teleportation protocol to open the wormhole and send quantum signals through it. By studying the dynamics of the transmitted quantum information, the team gained insights into gravitational dynamics. The experiment could be further developed to explore quantum gravity or string theory.
A wormhole is a bridge in space–time that connects two different locations. While wormholes are consistent with Albert Einstein’s general theory of relativity, they have not been observed by physicists. Unlike wormholes in science fiction, they are not traversable – meaning things cannot pass through them.
Although general relativity forbids travelling through a wormhole, it is theorized that exotic matter – matter with negative energy density and negative pressure – could open a wormhole and make it traversable. But these theories are difficult to test, even if one could create a wormhole in a lab.
But physics has a trick up its sleeve – in the form of the quantum teleportation of information between two entangled particles. This process occurs instantaneously and therefore emulates the process of sending quantum information through a gravitational wormhole. In both cases, however, it is not possible to communicate faster than the speed of light because a subluminal signal is required to decode the information.
Quantum entanglement plays an important role in quantum computing, therefore a quantum processors is the ideal experimental device to explore the similarities between quantum teleportation and wormholes. In this scenario, quantum bits – or qubits – on the quantum processor are entangled with each other and teleportation is the equivalent of the qubit travelling through a wormhole.
Down the wormhole
Now Maria Spiropulu at Caltech, Daniel Jafferis at Harvard University and colleagues have done such an experiment. Their aim was to create a system that has the right ingredients for the type of teleportation that resembles a wormhole.
An important challenge that they first had to overcome is that it appeared that a large number of qubits would be needed to perform the experiment properly – many more qubits than are available in today’s quantum processors. To solve this problem, the researchers used machine learning to work out the minimum number of qubits required and how they should be coded to set up the quantum teleportation protocol. They discovered that they could create the wormhole dynamics on nine qubits with 164 two-qubit gates on a Google Sycamore quantum processor.
In their experiment the researchers showed that they could keep a wormhole open for a sufficient amount of time by applying negative energy shockwaves, which came in the form of special pulses of quantum fields. They then studied the dynamics of the quantum information that was sent through. Signals that travel through a wormhole experience a series of scrambling and unscrambling, with the quantum information exiting the wormhole intact.
On the Sycamore processor, they measured how much quantum information passed from one side to the other, when applying a negative versus a positive energy shockwave. And because only negative energy shockwaves would open up the wormhole, they found that only these shockwaves allowed signals to pass through. Overall, the information passing through the wormhole had key signatures of a traversable wormhole. This constitutes a step towards probing gravitational physics using quantum processors and could lead to the development of powerful testbeds to study ideas of string theory and quantum gravity.
Quantum complexity could solve a wormhole paradox
Juan Maldacena at the Institute for Advanced Study, in Princeton, US, who was not involved in the research, describes the work as an interesting first step in trying to create complex quantum systems that can have an emergent space–time description. He thinks the result is important because it is a demonstration that allows us to experimentally test some of the theoretical ideas about the connection between quantum mechanics and emergent space–time geometry. He says the research’s biggest achievement is that it has reproduced a kind of quantum teleportation that is inspired by gravitational problems.
Team member Daniel Jafferis believes that there are many additional protocols and new ideas to explore and he expects more “gravity experiments” to be performed by quantum computers in the future. He thinks that some of these will require much larger quantum computers or much deeper circuits, but that others are well-suited for near-term experimentation.
“One of the things we would like to do next is to realize somewhat larger systems and try to observe more detailed structure of the emergent wormholes and their gravitational dynamics”, he tells Physics World.
Edward Witten, also at the Institute for Advanced Study and not involved in this research, says that the authors have done a nice job of describing a simplified version of the protocol that could be realized experimentally. He calls this experiment – and the presumed improvements that may be possible – to be a “milestone” in developing control over microscopic quantum systems. He states that even though such an experiment can certainly not give the sort of information that comes from physics experiments such as LIGO or the LHC, success with such experiments can confirm the validity of quantum mechanics in a rather subtle situation and also confirm that the theory has been analysed correctly.
The research is described in Nature.