After writing my post on basic electrical components I realized that batteries and transistors were going to require a good deal more research to understand adequately. Having completed my post on the former, the time has finally come to elucidate the foundation of modern electronics and computing: the humble transistor.
The development of the transistor began out of a need to find a superior means of amplifying telephone signals sent through long-distance wires. Around the turn of the twentieth century American Telephone and Telegraph (AT&T) had begun offering transcontinental telephone service as a way of staying competitive. The signal boost required to allow people to talk to each other over thousands of miles was achieved with triode vacuum tubes based on the design of Lee De Forest, an American inventor. But these vacuum tubes consumed a lot of power, produced a lot of heat, and were unreliable to boot. Mervin Kelly of Bell Labs recognized the need for an alternative and, after WWII, began assembling the team that would eventually succeed.
Credit for pioneering the transistor is typically given to William Shockley, John Bardeen, and Walter Brattain, also of of Bell Labs, but they were not the first people to file patents for the basic transistor principle: Julius Lilienfeld filed one for the field-effect transistor in 1925 and Oskar Hiel filed one in 1934. Neither man made much of an impact in the growing fields of electronics theory or electronics manufacturing, but there is evidence that William Shockley and Gerald Pearson, a co-worker at Bell Labs, did build a functioning transistor prototype from Lilienfeld’s patents.
Shockley, Brattain, and Bardeen understood that if they could solve certain basic problems they could build a device that would act like a signal amplifier in electronic circuits by exploiting the properties of semiconductors to influence electron flow.
Actually accomplishing this, of course, proved fairly challenging. After many failed attempts and cataloging much anomalous behavior a practical breakthrough was achieved. A strip of the best conductor, gold, was attached to a plastic wedge and then sliced with a razor, producing two gold foil leads separated by an extremely small space. This apparatus was then placed in contact with a germanium crystal which had an additional lead attached at its base. The space separating the two pieces of gold foil was just large enough to prevent electron flow. Unless, that is, current were applied to one of the gold-tipped leads, which caused ‘holes’ — i.e. spaces without electrons — to gather on the surface of the crystal. This allowed electron flow to begin between the base lead and the other gold-tipped lead. This device became known as the point-contact transistor, and gained the trio a Nobel Prize.
Though the point-contact transistor showed promise and was integrated with a number of electrical devices it was still fragile and impractical at a larger scale. This began to change when William Shockley, outraged at not receiving the credit he felt he deserved for the invention of this astonishing new device, developed an entirely new kind of transistor based on a ‘sandwich’ design. The result was essentially a precursor to the bipolar junction transistor, which is what almost everyone in the modern era means by the term ‘transistor’.
Under the Hood
In the simplest possible terms a transistor is essentially a valve for controlling the flow of electrons. Valves can be thought of as amplifiers: when you turn a faucet handle, force produced by your hand is amplified to control the flow of thousands of gallons of water, and when you press down on the accelerator in your car, the pressure of your foot is amplified to control the motion of thousands of pounds of fire and steel.
Valves, in other words, allow small forces to control much bigger forces. Transistors work in a similar way.
One common type of modern transistor is the bipolar junction NPN transistor, a cladistic descendant of Shockley’s original design. It is constructed from alternating layers of silicon which are doped with impurities to give them useful characteristics.
In its pure form silicon is a textbook semiconductor. It contains four electrons in its valence shell which causes it to form very tight crystal lattices that typically don’t facilitate the flow of electrons. The N layer is formed by injecting trace amounts of phosphorus, which contains five valence electrons, into this lattice. It requires much less energy to knock this fifth electron loose than it would to knock loose one of the four valence electrons in the silicon crystal, making the N layer semiconductive. Similarly, the P layer is formed by adding boron which, because of the three electrons in its valence shell, leaves holes throughout the silicon into which electrons can flow.
It’s important to bear in mind that neither the P nor the N layers are electrically charged. Both are neutral and both permit greater flow of electrons than pure silicon would. The interface between the N and P layers quickly becomes saturated as electrons from the phosphorus move into the holes in the valence shell of the Boron. As this happens it becomes increasingly difficult for electrons to flow between the N and P layers, and eventually a boundary is formed. This is called the ‘depletion layer’
Now, imagine that there is a ‘collector’ lead attached to the first N layer and another ’emitter’ lead attached to the other N layer. Current cannot flow between these two leads because the depletion layer at the P-N junction won’t permit it. Between these two layers, however, there is a third lead, called a ‘base’, placed very near the P layer. By making the base positively charged electrons can overcome the P-N junction and begin flowing from the emitter to the collector.
The key here is to realize that the amount of charge to the base required to get current moving is much smaller than the current flowing to the collector, and that current flow can be increased or decreased by a corresponding change in the current to the base. This is what gives the transistor its amplifier properties.
Transistors and Moore’s Law
Even more useful than this, however, is the ability of a transistor to act as a switch. Nothing about the underlying physics changes here. If current is not flowing in the transistor it is said to in cutoff, and if current is flowing in the transistor it is said to be in saturation. This binary property of transistors makes them ideally suited for the construction of logic gates, which are the basic components of every computer ever made.
A full discussion of logic gate construction would be well outside the purview of this essay, but it is worth briefly discussing one popular concept which requires a knowledge of transistors in order to be understood.
Named after Intel co-founder Gordon Moore, Moore’s Law is sometimes stated as the rule that computing power will double roughly every two years. The more accurate version is that the number of transistors which can fit in a given unit area will double every two years . These two definitions are fairly similar, but keeping the latter in mind will allow you to better understand the underlying technology and where it might head in the future.
Moore’s law has held for as long as it has because manufacturers have been able to make transistors smaller and smaller. Obviously this can’t continue forever, both because at a certain transistor density power consumption and heat dissipation become serious problems, and because at a certain size effects like quantum tunneling prevent the sequestering of electrons.
A number of alternatives to silicon-based chips are being seriously considered as a way of extending Moore’s Law. Because of how extremely thin it can be made, graphene is one such contender. The problem, however, is that the electrophysical properties of graphene are such that building a graphene transistor that can switch on and off is not straightforward. A graphene-based computer, therefore, might well have to develop an entirely different logical architecture to perform the same tasks as modern computers.
Other potentially fruitful avenues are quantum computing, optical computing, and DNA computing, all of which rely on very different architectures than conventional Von-Neumann computers. As I’m nearing the 1500 word mark I think I’ll end this essay here, but I do hope to return to these advanced computing topics at some point in the future 🙂
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Light-storing chip charted
Technology Research News
For years, researchers have been striving
to make high-speed, low-power chips that channel light rather than electricity,
but finding ways to briefly store light pulses has proved extremely challenging.
Recently, researchers have stored light pulses for fractions of
a second in hot gases, extremely cold gases or crystal doped with special
metal. But these techniques are challenging to carry out, and would be
difficult or impossible to configure into more practical chip form.
Researchers at Stanford University have come up with a scheme
to store light pulses under ordinary conditions using photonic crystal
-- semiconductor chips that contain regularly spaced holes or rods of
a different material. "Our discovery enables quantum coherent storage
of light pulses on a microchip about the size of the grain of salt," said
Mehmet Fatih Yanik, a research assistant at Stanford University.
The scheme could lead to inexpensive chips that power all-optical
communications switches, quantum computers and quantum communications
devices. "Operating wavelengths[and] bandwidths... can simply be designed
by standard lithographic techniques used in conventional microchip technologies,"
The method would allow light pulses to be stored in microchips
at room temperature without requiring any special light-matter interactions,
The researchers' findings run counter to the conventional wisdom
that devices using optical resonators -- tiny structures that vibrate
at light frequencies -- can do no more than slow light by a limited amount.
In one type of device, for example, light pulses at the telecommunications
wavelength of 1.55 microns and a rate of 10 gigabits per second can be
slowed to no less than one hundredth the speed of light in a vacuum, said
The key to the researchers' method is a technique that allows
them to change -- on-the-fly -- the way portions of the photonic crystal
respond to light. "We discovered a practical way to compress light's bandwidth
by an unlimited amount... using conventional optoelectronics technologies
at speeds sufficient to prevent light pulses [from] passing through our
system," said Yanik.
The researchers' simulation shows that light pulses can be slowed
to less than 10 centimeters per second, slow enough that the pulses would
be held essentially in place for tiny fractions of a second, according
to Yanik. This is long enough to make pulses interact to switch light
signals for high-speed communications or link photons for quantum computing.
The researchers' light-controlling chip design calls for photonic
crystal that contains a series of optical resonators, or cavities. Photonic
crystal refracts, or bends, light -- the same effect that produces the
familiar bent-drinking-straw illusion. The boundaries made by photonic
crystal's holes or rods refract light, and the spacing of these gaps determines
the degree to which a given wavelength of light is bent. Photonic crystal
can be designed to block or channel specific wavelengths.
In the researchers' design, one series of cavities forms a straight
waveguide that allows light pulses to pass through the device. Each cavity
in the waveguide is attached to a side cavity that connects to a second
The chip would briefly trap a pulse by changing the microcavities'
resonant frequencies. Tuning the waveguide to resonate at the same frequency
as the light pulse and at the same time keeping the side cavities out
of tune would allow the pulse to enter the device. Once the pulse is inside
the device, the waveguide would be gradually -- though at very high speed
-- detuned while the side cavities were tuned to the pulse frequency.
This would shunt the pulse into the side cavities. Reversing the tuning-detuning
process would release the pulse into the waveguide, allowing it to continue
on its way through the device.
Key to the method is a way to tune the refractive index of the
photonic crystal in a way that preserves the shape of the pulse. Light
pulses contain multiple wavelengths, and the wavelengths bend to different
degrees as pulses travel through matter. This disperses the wavelengths,
causing light pulses to spread out, which limits the distance they can
travel through a material. Wavelength dispersion also limits the amount
light pulses can be slowed, because they can spread only so much before
The researchers' technique tunes a device's refractive index in
a way that lowers the frequency of all of the pulse's wavelengths consistently,
preserving the pulse.
A set of 120 microcavities whose tunings change at a maximum rate
of one gigahertz is sufficient to store and release a light pulse, according
to Yanik. Multiple light pulses could be stored simultaneously in the
device, and specific pulses could be released on demand, he said.
The researchers' scheme could also applied to other systems that
involve resonance, said Yanik. It could be used to slow and store microwave
signals and ultrasound waves, and possibly detect gravitational waves,
The technique is an advance over previous work on stopped light
because it uses microscopic optical cavities rather than atoms, said Raymond
Chiao, a professor of physics at the University of California at Berkeley.
"This allows much larger bandwidths of light to be stopped."
The work would have been more impressive had the authors demonstrated
the stopping of light experimentally, he added.
The researchers are aiming to demonstrate their technique by trapping
microwave signals. A demonstration should take place within a year, and
a practical prototype that works at optical frequencies could be made
in two to five years, said Yanik.
Yanik research colleague was Shanhui Fan. The work is slated for
publication in Physical Review Letters. The research was funded
by the National Science Foundation (NSF) and Stanford University.
Timeline: 2-5 years
Funding: Government, University
TRN Categories: Optical Computing, Optoelectronics and Photonics
Story Type: News
Related Elements: Technical paper, "Stopping Light All-Optically,"
posted at the arXiv physics archive at arxiv.org/abs/quant-ph/0312027
February 11/18, 2004
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Building a large-scale physical quantum computer is still challenging. When scaling up qubits, wiring diagrams get increasingly more complicated. Bogdan Govoreanu, quantum computing program manager at imec, presents a smart way of interconnecting neighboring silicon qubits in a 2D bilinear array. This architecture tackles the qubit connectivity problem and is a potential pathway for realizing a quantum computer. The array design, together with device geometrical requirements analysis based on advanced multiscale modeling, is presented in a paper at IEDM 2021.
How to build a large quantum computer
Quantum computers leverage the properties of quantum physics to process larger amounts of data significantly faster than classical computers. The basic units, quantum bits or qubits, simultaneously exist in two states making it possible to sift through a vast number of potential outcomes at once. Silicon-based qubits are very attractive for potential use in quantum computers because they are compatible with the well-established processes of high-volume manufacturing in the semiconductor industry. Nevertheless, scaling up the number of qubits remains a roadblock for building large-scale quantum computers. While small arrays have been demonstrated, a practical design that scales to the requirements where it outperforms classical computers is still lacking.
One bottleneck to developing larger quantum computers is the problem of how to arrange qubits. Efficient quantum algorithms require 2D-arrays where qubits can interact with their neighbors and be accessed by external circuits and devices. Each qubit needs dedicated lines for control and readout and a small pitch of typically tens of nanometers between two qubits. Increasing the number of qubits makes it therefore difficult to access the qubits at the center of the array. “We propose an elegant solution to this challenge: a bilinear 2D design for silicon qubits where each qubit connects to four other qubits,” tells Bogdan Govoreanu. “This architecture yields a compact array where different qubit coupling mechanisms are combined to achieve an overall connectivity of four, for each qubit in the bilinear array.”
Solving the qubit connectivity problem
“Our design is based on topologically mapping a 2D square lattice, to form a so-called bilinear design, where alternating rows of the lattice are shifted into two rows or 1D arrays (see Figure 1). By arranging the qubits in two rows they always remain addressable while maintaining the target connectivity of four in the equivalent 2D square lattice array. These arrays are also easily scalable as we only need to grow them in one dimension, along the rows,” explains Bogdan Govoreanu. “The connections between the two 1D arrays do not intersect because they are wired on two different planes, separated by a ground plane to isolate them from each other (Figure 2).”
In this architecture, each qubit corresponds to the spin orientation of an electron confined in a potential well, called a quantum dot. Coupling these qubits is necessary for ‘quantum entanglement’, a property that underlies the exponential computing power of quantum computers. Entangled qubits store all the possible combinations of the quantum states of each qubit (e.g. for two qubits, this results in four values). The quantum dots within a 1D array are coupled through the spin interaction between electrons in nearby quantum dots, where nearby electron spins naturally interact through a quantum mechanical process called exchange coupling. The quantum dots between the 1D arrays are coupled over a long distance (~mm) via a microwave resonator, fabricated using superconducting materials. Such a long range is possible since the qubit state can be coupled to the photonic mode of the resonator, when the qubit electron is delocalized between two quantum dots.
The quantum states are very fragile and prone to error. That is why building a large quantum computer is not just about scaling up the number of qubits, it is also about how resistant they are to errors. Since quantum computers cannot use the same error-correcting algorithms as classical computers, they fall back on quantum error correction techniques with ‘logical qubits’ - a complex arrangement of thousands of physical qubits that is used to encode a single qubit. “Our design is compatible with the widely accepted quantum error correction scheme, the surface code, that can run algorithms tolerating up to certain qubit error” explains Bogdan Govoreanu.
“The typical number of physical qubits to implement a logical qubit is believed to be somewhere between 103 to 104, depending on the quality of the physical qubits. Hundreds to thousands of logical qubits are necessary for running practical large-scale algorithms, which implies that overall physical qubit numbers can exceed a million. In this paper, we characterized the relevant quantum resources needed for viable quantum error correction, along with providing a detailed analysis of the required device dimensions, tolerable noise specifications and quantum gate operations times in the structure (Figure 3). The bilinear architecture needs an extremely compact quantum logic area of around 36mm2 even for a system with a million qubits. Moreover, the resonators and the electrostatic gates defining the quantum dots are easily accessible from both sides in the bilinear array, thereby considerably reducing the wiring fanout complexity.”
“This design is compatible with current CMOS fabrication technologies and can thus open the path for a future demonstration of large-scale silicon quantum computers,” concludes Bogdan Govoreanu.
Want to know more?
- Follow imec's presence at the 2021 IEEE International Electron Devices Meeting
- Read all details of the novel device architecture in the paper entitled “Large-Scale 2D Spin-Based Quantum Processor with a Bi-Linear Architecture” by F.A. Mohiyaddin, R. Li, S. Brebels, G. Simion, N. I. Dumoulin Stuyck, C. Godfrin, M. Shehata, A. Elsayed, B. Gys, S. Kubicek, J. Jussot, Y. Canvel, S. Massar, P. Weckx, P. Matagne, M. Mongillo, B. Govoreanu and I. P. Radu, presented at IEDM 2021, which can be requested here.
Bogdan Govoreanu is Quantum Computing Program Manager at imec, where he coordinates the technical research and development program activities. Prior to this, his research work included various topics in Memory Technology and Emerging Devices, with focus on Flash and resistive switching memory technologies, selectors and neuromorphic computing. He developed strong interests in novel concepts for computing and storage beyond current mainstream technologies. As of 2017, his research focuses on Quantum Computing Technologies and Systems. Bogdan holds a Ph.D. degree in Applied Sciences from KU Leuven for his research performed at imec on novel tunnel barrier concepts.
More about these topics:
11 December 2021


Quantum Inspire, hosted by QuTech, a collaboration of Delft University of Technology and TNO, the Netherlands Organization for Applied research, consists of two independent quantum processors, Spin-2 and Starmon-5, and a quantum simulator. Anyone can create an account, use the Web interface to write a quantum algorithm, and have it executed by one of the processors in milliseconds (if there is no queue), with the result returned within a minute. The process is fully automated.
Seen from the outside, Spin-2 and Starmon-5 are two large, cylindrical cryostats hanging from the ceiling in a university building. One floor up, a man-size stack of electronics for each takes care of the cooling, feeding the quantum processor input from users and reading out the results. Usually, there is no one in these rooms.
The facility officially went online on April 20, and over 1,000 accounts have been created since then. Though many curious visitors never returned, active users now upload about 6,000 jobs a month to be executed.
A quantum computer uses qubits for its computations. A qubit can be a single electron, or an electronic circuit, that has two quantum energy states, which correspond to the 0 and 1 of a classical bit of information. However, the magic of quantum physics enables a qubit to be in a superposition 0 and 1 at the same time, and N qubits that are prepared in a superposition can represent all 2N combinations of these 0s and 1s simultaneously. In a sense, its capacity doubles with every qubit added, so a quantum computer with just 50 error-free qubits could achieve an enormous speed-up compared to a standard computer.
With only two qubits, Spin-2 is mainly a proof of principle, for it is the first time that qubits based on electron spins are accessible online. These consist of a single electron each, trapped in a 'quantum dot', a nanoscale structure on a silicon chip cooled to 0.02 Kelvin (degrees above absolute zero). An electron can be in a superposition of two spin states, 'up' and 'down'. Calibration of the relatively unstable quantum dots still needs manual tuning: every four hours, the system has 20 minutes of downtime.
According to Richard Versluis, principal systems engineer at QuTech and Spin-2 lead, operating spin qubits is currently more difficult than other types of qubits, but they promise advantages in the long run: "They can be built with standard chip technology, and they are so tiny that millions would fit on one chip. The promise is very big, the challenge is also very big."
Starmon-5 has five transmon qubits. A transmon is a superconducting electronic circuit that can be switched between two tunable energy states. With five stable, entangled qubits, Starmon-5 can execute short quantum algorithms composed from a universal set of quantum gates, equivalent to the operations of classical computer logic, like AND and NOT.
Quantum Inspire does not yet offer sufficient qubits to achieve 'quantum supremacy', which means quickly performing a calculation that would take a classical computer thousands of years. Google claimed to have accomplished this last October with it 53-qubit Sycamore machine, although IBM disputed that claim. IBM itself offers Quantum Experience, an online quantum computer with 16 transmon qubits accessible for free.
Said Leonardo di Carlo, QuTech co-founder and Starmon-5 lead, "For us, the educational aspect is important. We plan to include this in our teaching to our students in the fall."
Starmon-5 is a useful testbed for quantum computations, for the correct execution of quantum software is less straightforward than running a few lines of code on a regular computer. Said Di Carlo, "I am an avid user of Quantum Inspire myself. I'm especially interested in learning how our quantum computers can improve from the end-user perspective."
Even a 5-qubit quantum computer that performs reliably is a huge step forward, says Di Carlo. "For example, Starmon-5 executes Grover's search algorithm with 88% success probability. The first time someone did this, in 2012, the success rate was 59%, and that was considered a breakthrough." Grover's algorithm, which does searches similar to looking up a name in a phone directory, is one of the few quantum algorithms that is proven to outperform all classical search algorithms, so it is often used as a test.
Also, Starmon-5 was designed with modularity in mind: a chip with many more transmon qubits would fit into the same cryostat. Di Carlo is cautious about making predictions when such a larger system will become available, but increasing the number of qubits is a key priority.
While QuTech is developing quantum computing hardware, another Dutch collaboration, QuSoft, works on the quantum software that will run on these computers. Koen Groenland, until recently doing a postdoc on 'Few qubit applications' at QuSoft, says Quantum Inspire is an interesting proof of concept, but still too small for serious research applications. According to Groenland, the minimum size for applications will be somewhere between 50 and 200 qubits.
Nevertheless, even with five qubits, valuable experience can be gained. Other than classical bits, which can only be switched from 0 to 1 or vice versa, operations on qubits resemble continuous rotations. Said Groenland, "Lots of variations are possible even with only a few qubits, and this allows you to fine-tune your quantum algorithm till it does what you want."
In quantum computing, size gets most of the attention, but quality matters as well, Groenland said. "On the one hand, you want more qubits, but you also want qubits that stay in superposition long enough to execute an algorithm with many consecutive operations."
Arnout Jaspers is a freelance science writer based in Leiden, the Netherlands.
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Nearly a century after the dark matter was first proposed to explain the motion of galaxy clusters, physicists still have no idea what it’s made of.
Researchers around the world have built dozens of detectors in hopes of discovering dark matter. As a graduate student, I helped design and operate one of these detectors, aptly named HAYSTAC. But despite decades of experimental effort, scientists have yet to identify the dark matter particle.
Now, the search for dark matter has received an unlikely assist from technology used in quantum computing research. In a new paper published in the journal Nature, my colleagues on the HAYSTAC team and I describe how we used a bit of quantum trickery to double the rate at which our detector can search for dark matter. Our result adds a much-needed speed boost to the hunt for this mysterious particle.
Scanning for a dark matter signal
There is compelling evidence from astrophysics and cosmology that an unknown substance called dark matter constitutes more than 80% of the matter in the universe. Theoretical physicists have proposed dozens of new fundamental particles that could explain dark matter. But to determine which – if any – of these theories is correct, researchers need to build different detectors to test each one.
One prominent theory proposes that dark matter is made of as-yet hypothetical particles called axions that collectively behave like an invisible wave oscillating at a very specific frequency through the cosmos. Axion detectors – including HAYSTAC – work something like radio receivers, but instead of converting radio waves to sound waves, they aim to convert axion waves into electromagnetic waves. Specifically, axion detectors measure two quantities called electromagnetic field quadratures. These quadratures are two distinct kinds of oscillation in the electromagnetic wave that would be produced if axions exist.
The main challenge in the search for axions is that nobody knows the frequency of the hypothetical axion wave. Imagine you’re in an unfamiliar city searching for a particular radio station by working your way through the FM band one frequency at a time. Axion hunters do much the same thing: They tune their detectors over a wide range of frequencies in discrete steps. Each step can cover only a very small range of possible axion frequencies. This small range is the bandwidth of the detector.
Tuning a radio typically involves pausing for a few seconds at each step to see if you’ve found the station you’re looking for. That’s harder if the signal is weak and there’s a lot of static. An axion signal – in even the most sensitive detectors – would be extraordinarily faint compared with static from random electromagnetic fluctuations, which physicists call noise. The more noise there is, the longer the detector must sit at each tuning step to listen for an axion signal.
Unfortunately, researchers can’t count on picking up the axion broadcast after a few dozen turns of the radio dial. An FM radio tunes from only 88 to 108 megahertz (one megahertz is one million hertz). The axion frequency, by contrast, may be anywhere between 300 hertz and 300 billion hertz. At the rate, today’s detectors are going, finding the axion or proving that it doesn’t exist could take more than 10,000 years.
Squeezing the quantum noise
On the HAYSTAC team, we don’t have that kind of patience. So in 2012, we set out to speed up the axion search by doing everything possible to reduce noise. But by 2017 we found ourselves running up against a fundamental minimum noise limit because of a law of quantum physics known as the uncertainty principle.
The uncertainty principle states that it is impossible to know the exact values of certain physical quantities simultaneously – for instance, you can’t know both the position and the momentum of a particle at the same time. Recall that axion detectors search for the axion by measuring two quadratures – those specific kinds of electromagnetic field oscillations. The uncertainty principle prohibits precise knowledge of both quadratures by adding a minimum amount of noise to the quadrature oscillations.
In conventional axion detectors, the quantum noise from the uncertainty principle obscures both quadratures equally. This noise can’t be eliminated, but with the right tools, it can be controlled. Our team worked out a way to shuffle around the quantum noise in the HAYSTAC detector, reducing its effect on one quadrature while increasing its effect on the other. This noise manipulation technique is called quantum squeezing.
In an effort led by graduate students Kelly Backes and Dan Palken, the HAYSTAC team took on the challenge of implementing squeezing in our detector, using superconducting circuit technology borrowed from quantum computing research. General-purpose quantum computers remain a long way off, but our new paper shows that this squeezing technology can immediately speed up the search for dark matter.
Bigger bandwidth, faster search
Our team succeeded in squeezing the noise in the HAYSTAC detector. But how did we use this to speed up the axion search?
Quantum squeezing doesn’t reduce the noise uniformly across the axion detector bandwidth. Instead, it has the largest effect at the edges. Imagine you tune your radio to 88.3 megahertz, but the station you want is actually at 88.1. With quantum squeezing, you would be able to hear your favorite song playing one station away.
In the world of radio broadcasting, this would be a recipe for disaster, because different stations would interfere with one another. But with only one dark matter signal to look for, a wider bandwidth allows physicists to search faster by covering more frequencies at once. In our latest result, we used squeezing to double the bandwidth of HAYSTAC, allowing us to search for axions twice as fast as we could before.
Quantum squeezing alone isn’t enough to scan through every possible axion frequency in a reasonable time. But doubling the scan rate is a big step in the right direction, and we believe further improvements to our quantum squeezing system may enable us to scan 10 times faster.
Nobody knows whether axions exist or whether they will resolve the mystery of dark matter; but thanks to this unexpected application of quantum technology, we’re one step closer to answering these questions.


During the past months we’ve been reporting several breakthroughs in the field of quantum computing, and now IBM seems ready to truly pave the way for quantum computers. Researchers announced they are now able to develop a superconducting qubit made from microfabricated silicon that maintains coherence long enough for practical computation. Whoa! That probably sounds like a lot to swallow, so let’s break it down.
Bits and Qubits
Information is measured in ‘bits’, and a bit may have two positions (described typically as 0 or 1). Quantum computers however don’t use these bits, and instead they use quantum bits, or ‘qubits’. But while a bit must be a 0 or a 1, a qubit can be both 0, 1, or a superposition of both. This difference might seem small and subtle, but in fact, it is absolutely humongous: a mere hundred qubits can store more classical ‘bit’ information than there are atoms in the Universe.
Needless to say a computer running on qubits would be game changing, in pretty much the same way microprocessors were in their days. But what makes quantum computing extremely difficult is a problem called ‘decoherence‘. In the quantum world, things don’t happen as they do in the ‘real world’; when a qubit will move from the 0 state to the 1 state or to a superposition, it will decohere to state 0 due to interference from other parts of the computer. Generally speaking, decoherence is the loss order of the phase angles between the components. So in order for quantum computers to be practical and scalable, the system would have to remain coherent for a long enough time to allow error-correction techniques to function properly.
“In 1999, coherence times were about 1 nanosecond,” said IBM scientist Matthias Steffen. “Last year, coherence times were achieved for as long as 1 to 4 microseconds. With these new techniques, we’ve achieved coherence times of 10 to 100 microseconds. We need to improve that by a factor of 10 to 100 before we’re at the threshold we want to be. But considering that in the past ten years we’ve increased coherence times by a factor of 10,000, I’m not scared.”
Two different approaches, one breakthrough
IBM announced they took two different approaches, both of which played a significant part in the breakthrough they revealed. The first one was to build a 3-D qubit made from superconducting, microfabricated silicon. The main advantage here is that the equipment and know-how necessary to create this technology already exists, nothing new has to be invented, thanks to developments made by Yale researchers (for which Steffen expressed a deep admiration). Using this approach, they managed to maintain coherence for 95 microseconds – “But you could round that to 100 for the piece if you want,” Steffen joked.
The second idea involved a traditional 2-D qubit, which IBM’s scientists used to build a “Controlled NOT gate” or CNOT gate, which is a building block of quantum computing. A CNOT gate connects two qubits in such a way that the second qubit will change state if the first qubit changes its state to 1. The CNOT gate was able to produce a coherence of 10 microseconds, which is long enough to show a 95% accuracy rate – a notable improvement from the 81% accuracy rate, the highest achieved until now. Of course, the technology is still years away from being actually on the shelves, but the developments are very impressive.
From quantum to reality
Given the rapid progress that is being made in the field of quantum computing, one can only feel that a quantum computer is looking more and more like a real possibility. As error correction protocols become more accurate and coherence times grow longer, we are moving more and more towards accurate quantum computing – but you shouldn’t expect a quantum smartphone just yet.
“There’s a growing sense that a quantum computer can’t be a laptop or desktop,” said Steffen. “Quantum computers may well just being housed in a large building somewhere. It’s not going to be something that’s very portable. In terms of application, I don’t think that’s a huge detriment because they’ll be able to solve problems so much faster than traditional computers.”
The next steps are simple, in principle, but extremely hard to do in practice. The accuracy rate has to be at at least 99.99%, up to the point where it achieves what is called a ‘logical qubit’ – one that, for practical purposes, doesn’t suffer decoherence. From that point, the only thing left to do is develop the quantum computer architecture, and this will prove troublesome too – but the reward is definitely worth it.
“We are very excited about how the quantum computing field has progressed over the past ten years,” he told me. “Our team has grown significantly over past 3 years, and I look forward to seeing that team continue to grow and take quantum computing to the next level.”


40 years ago, Nobel Prize-winner Richard Feynman argued that “nature isn't classical, dammit, and if you want to make a simulation of nature, you'd better make it quantum mechanical.” This was later perceived as a rallying cry for developing a quantum computer, leading to today’s rapid progress in the search for quantum supremacy. Here’s a very short history of the evolution of quantum computing.
1905 Albert Einstein explains the photoelectric effect—shining light on certain materials can function to release electrons from the material—and suggests that light itself consists of individual quantum particles or photons.
1924 The term quantum mechanics is first used in a paper by Max Born
1925 Werner Heisenberg, Max Born, and Pascual Jordan formulate matrix mechanics, the first conceptually autonomous and logically consistent formulation of quantum mechanics
1925 to 1927 Niels Bohr and Werner Heisenberg develop the Copenhagen interpretation, one of the earliest interpretations of quantum mechanics which remains one of the most commonly taught
1930 Paul Dirac publishes The Principles of Quantum Mechanics, a textbook that has become a standard reference book that is still used today
1935 Albert Einstein, Boris Podolsky, and Nathan Rosen publish a paper highlighting the counterintuitive nature of quantum superpositions and arguing that the description of physical reality provided by quantum mechanics is incomplete
1935 Erwin Schrödinger, discussing quantum superposition with Albert Einstein and critiquing the Copenhagen interpretation of quantum mechanics, develops a thought experiment in which a cat (forever known as Schrödinger’s cat) is simultaneously dead and alive; Schrödinger also coins the term “quantum entanglement”
1947 Albert Einstein refers for the first time to quantum entanglement as “spooky action at a distance” in a letter to Max Born
1976 Roman Stanisław Ingarden of the Nicolaus Copernicus University in Toruń, Poland, publishes one of the first attempts at creating a quantum information theory
1980 Paul Benioff of the Argonne National Laboratory publishes a paper describing a quantum mechanical model of a Turing machine or a classical computer, the first to demonstrate the possibility of quantum computing
1981 In a keynote speech titled Simulating Physics with Computers, Richard Feynman of the California Institute of Technology argues that a quantum computer had the potential to simulate physical phenomena that a classical computer could not simulate
1985 David Deutsch of the University of Oxford formulates a description for a quantum Turing machine
1992 The Deutsch–Jozsa algorithm is one of the first examples of a quantum algorithm that is exponentially faster than any possible deterministic classical algorithm
1993 The first paper describing the idea of quantum teleportation is published
1994 Peter Shor of Bell Laboratories develops a quantum algorithm for factoring integers that has the potential to decrypt RSA-encrypted communications, a widely-used method for securing data transmissions
1994 The National Institute of Standards and Technology organizes the first US government-sponsored conference on quantum computing
1996 Lov Grover of Bell Laboratories invents the quantum database search algorithm
1998 First demonstration of quantum error correction; first proof that a certain subclass of quantum computations can be efficiently emulated with classical computers
1999 Yasunobu Nakamura of the University of Tokyo and Jaw-Shen Tsai of Tokyo University of Science demonstrate that a superconducting circuit can be used as a qubit
2002 The first version of the Quantum Computation Roadmap, a living document involving key quantum computing researchers, is published
2004 First five-photon entanglement demonstrated by Jian-Wei Pan's group at the University of Science and Technology in China
2011 The first commercially available quantum computer is offered by D-Wave Systems
2012 1QB Information Technologies (1QBit), the first dedicated quantum computing software company, is founded
2014 Physicists at the Kavli Institute of Nanoscience at the Delft University of Technology, The Netherlands, teleport information between two quantum bits separated by about 10 feet with zero percent error rate
2017 Chinese researchers report the first quantum teleportation of independent single-photon qubits from a ground observatory to a low Earth orbit satellite with a distance of up to 1400 km
2018 The National Quantum Initiative Act is signed into law by President Donald Trump, establishing the goals and priorities for a 10-year plan to accelerate the development of quantum information science and technology applications in the United States
2019 Google claims to have reached quantum supremacy by performing a series of operations in 200 seconds that would take a supercomputer about 10,000 years to complete; IBM responds by suggesting it could take 2.5 days instead of 10,000 years, highlighting techniques a supercomputer may use to maximize computing speed
The race for quantum supremacy is on, to being able to demonstrate a practical quantum device that can solve a problem that no classical computer can solve in any feasible amount of time. Speed—and sustainability—has always been the measure of the jump to the next stage of computing.
In 1944, Richard Feynman, then a junior staff member at Los Alamos, organized a contest between human computers and the Los Alamos IBM facility, with both performing a calculation for the plutonium bomb. For two days, the human computers kept up with the machines. “But on the third day,” recalled an observer, “the punched-card machine operation began to move decisively ahead, as the people performing the hand computing could not sustain their initial fast pace, while the machines did not tire and continued at their steady pace” (see When Computers Were Human, by David Alan Greer).


Light and matter are typically viewed as distinct entities that follow their own, unique rules. Matter has mass and typically exhibits interactions with other matter, while light is massless and does not interact with itself. Yet, wave-particle duality tells us that matter and light both act sometimes like particles, and sometimes like waves.
Harnessing the shared wave nature of light and matter, researchers at the University of Chicago, led by Jonathan Simon, the Neubauer Family Assistant Professor of Physics, have used light to explore some of the most intriguing questions in the quantum mechanics of materials. The topic encompasses complex and non-intuitive phenomena that are often difficult to explain in non-technical language, but which carry important implications to specialists in the field.
In work published online this week in the journal Nature, Simon’s group presents new experimental observations of a quantum Hall material near a singularity of curvature in space.
Quantum effects give rise to some of the most useful and promising properties of materials: They define standard units of measurement, give rise to superconductivity and describe quantum computers. The quantum Hall materials are one prominent example in which electrons are trapped in non-conducting circular orbits except at the edges of the material. There, electrons exhibit quantized resistance-free electrical conduction that is immune to disorder such as material impurities or surface defects.
Furthermore, electrons in quantum Hall materials do not transmit sound waves but instead have particle-like excitations, some of which are unlike any other particles ever discovered. Some of these materials also exhibit simultaneous quantum entanglement between millions of electrons, meaning that the electrons are so interconnected, the state of one instantly influences the state of all others. This combination of properties makes quantum Hall materials a promising platform for future quantum computation.
Researchers worldwide have spent the past 35 years delving into the mysteries of quantum Hall materials, but always in the same fundamental way. They use superconducting magnets to make very powerful magnetic fields and refrigerators to cool electronic samples to thousandths of a degree above absolute zero.
In a new approach, Simon and his team demonstrated the creation of a quantum Hall material made up of light. “Using really good mirrors that are pointed at each other, we can trap light for a long time while it bounces back and forth many thousands of times between the mirrors,” explained graduate student Nathan Schine.
In the UChicago experiment, photons travel back and forth between mirrors, while their side-to-side motion mimics the behavior of massive particles like electrons. To emulate a strong magnetic field, the researchers created a non-planar arrangement of four mirrors that makes the light twist as it completes a round trip. The twisting motion causes the photons to move like charged particles in a magnetic field, even though there is no actual magnet present.
“We make the photons spin, which leads to a force that has the same effect as a magnetic field,” explained Schine. While the light is trapped, it behaves like the electrons in a quantum Hall material.
First, Simon’s group demonstrated that they had a quantum Hall material of light. To do so, they shined infrared laser light at the mirrors. By varying the laser’s frequency, Simon’s team could map out precisely at which frequencies the laser was transmitted through the mirrors. These transmission frequencies, along with camera images of the transmitted light, gave a telltale signature of a quantum Hall state.
Next, the researchers took advantage of the precise control that advanced optical systems provide to place the photons in curved space, which has not been possible so far with electrons. In particular, they made the photons behave as if they resided on the surface of a cone.
...Near a singularity
“We created a cone for light, much like you might do by cutting a wedge of paper and taping the edges together,” said postdoctoral fellow Ariel Sommer, also a co-author of the paper. “In this case, we imposed a three-fold symmetry on our light, which essentially divides the plane into three wedges and forces the light to repeat itself on each wedge.”
The tip of a cone has infinite curvature—the singularity—so the researchers were able to study the effect of strong spatial curvature in a quantum Hall material. They observed that photons accumulated at the cone tip, confirming a previously untested theory of the quantum Hall effect in curved space.
Despite 20 years of interest, this is the first time an experiment has observed the behavior of quantum materials in curved space. “We are beginning to make our photons interact with each other,” said Schine. “This opens up many possibilities, such as making crystalline or exotic quantum liquid states of light. We can then see how they respond to spatial curvature.”
The researchers say this could be useful for characterizing a certain type of quantum computer that is built of quantum Hall materials.
“While quantum Hall materials were discovered in the ’80s, they continue to reveal their fascinating secrets to this day,” said Simon. “The final frontier is exploring the interplay of these beautiful materials with the curvature of space. That is what we’ve begun to explore with our photons.”
Citation: “Synthetic Landau levels for photons,” Nature Advance Online Publication, June 8, 2016, by Nathan Schine, Albert Ryou, Andrey Gromov, Ariel Sommer and Jonathan Simon. DOI: 10.1038/nature17943.
Funding: U.S. Department of Energy, Defense Advanced Research Projects Agency, Air Force Office of Scientific Research.


Quantum teleportation is a technique for transferring quantum information from a sender at one location to a receiver some distance away.
While teleportation is portrayed in science fiction as a means to transfer physical objects from one location to the next, quantum teleportation only transfers quantum information.
For the first time, a team of scientists and researchers have achieved sustained, high-fidelity ‘quantum teleportation’ — the instant transfer of ‘qubits’ (quantum bits) the basic unit of quantum information. the collaborative team, which includes NASA’s jet propulsion laboratory, successfully demonstrated sustained, long-distance teleportation of qubits of photons (quanta of light) with fidelity greater than 90%. the qubits were teleported 44 kilometers (27 miles) over a fiber-optic network using state-of-the-art single-photon detectors and off-the-shelf equipment.
Important point to keep in mind is quantum teleportation is the transfer of quantum states from one location to another using quantum entanglement, where the two particles in separate locations are connected by an invisible force, famously referred to as “spooky action at a distance” by Albert Einstein. Regardless of the distance, the encoded information shared by the “entangled” pair of particles can be passed between them. An interesting note is that the sender knows neither the location of the recipient nor the quantum state that will be transferred.
By sharing these quantum qubits, the basic units of quantum computing, researchers are hoping to create networks of quantum computers that can share information at blazing-fast speeds. But keeping this information flow stable over long distances has proven extremely difficult due to changes in the environment including noise. Researchers are now hoping to scale up such a system, using both entanglement to send information and quantum memory to store it as well.
On the same front, scientists have advanced their quantum technology research with the development of a chip that could be scaled up and used to build the quantum simulator of the future using nanochip that allows them to produce enough stable photons encoded with quantum information to scale up the technology. The chip, which is said to be less than one-tenth of the thickness of a human hair, may enable the scientists to achieve ‘quantum supremacy’ – where a quantum device can solve a given computational task faster than the world’s most powerful supercomputer.
In quantum entanglement particles that have interacted at some point retain a type of connection and can be entangled with each other in pairs, in a process known as correlation. Knowing the spin state of one entangled particle – up or down – allows one to know that the spin of its mate is in the opposite direction. Quantum entanglement allows qubits that are separated by incredible distances to interact with each other instantaneously (not limited to the speed of light). No matter how great the distance between the correlated particles, they will remain entangled as long as they are isolated.
In July, the US Department of Energy unveiled a blueprint for the first quantum internet, connecting several of its National Laboratories across the country. A quantum internet would be able to transmit large volumes of data across immense distances at a rate that exceeds the speed of light. You can imagine all the applications that can benefit from such speed.
Traditional computer data is coded in either zeros or ones. Quantum information is superimposed in both zeros and ones simultaneously. Academics, researchers and IT professionals will need to create devices for the infrastructure of quantum internet including: quantum routers, quantum repeaters, quantum gateways, quantum hubs, and other quantum tools. A whole new industry will be born based on the idea of quantum internet exists in parallel to the current ecosystem of companies we have in regular internet.
The “traditional internet “, as the regular internet is sometimes called, will still exist. It is expected that large organizations will rely on the quantum internet to safeguard data, but that individual consumers will continue to use the classical internet.
Experts predict that the financial sector will benefit from the quantum internet when it comes to securing online transactions. The healthcare sectors and the public sectors are also expected to see benefits. In addition to providing a faster, safer internet experience, quantum computing will better position organizations to solve complex problems, like supply chain management. Furthermore, it will expedite the exchange of vast amounts of data, and carrying out large-scale sensing experiments in astronomy, materials discovery and life sciences.
Ahmed Banafa is an expert in new tech with appearances on ABC, NBC , CBS, FOX TV and radio stations. He served as a professor, academic advisor and coordinator at well-known American universities and colleges. His researches are featured on Forbes, MIT Technology Review, ComputerWorld and Techonomy. He published over 100 articles about the internet of things, blockchain, artificial intelligence, cloud computing and big data. His research papers are used in many patents, numerous thesis and conferences. He is also a guest speaker at international technology conferences. He is the recipient of several awards, including Distinguished Tenured Staff Award, Instructor of the year and Certificate of Honor from the City and County of San Francisco. Ahmed studied cyber security at Harvard University. He is the author of the book: Secure and Smart Internet of Things Using Blockchain and AI.


Light and matter can interact in a number of different ways. Under certain conditions, light particles (photons) can affect the movement of atoms of matter. It happens because the emission and absorption of photons are followed by recoil. Such interactions are a study subject of one of the physics’ subdivisions, namely, quantum optomechanics.
A study of how and under which conditions particles of matter interact with light has a great number of practical applications. The scope of its use can be even more expanded in the future as humanity is heading towards the creation of computing devices based on the interaction of photons rather than electors – for example, optical computers.
“Imagine a chain of atoms placed in the vicinity of an optical waveguide through which photons can propagate. Each atom is a two-level system, meaning that it has two states – ground and excited. An atom can change its state due to the absorption of a photon or its emission. Such systems could be applied in the rapidly developing field of quantum computing,” explains Denis Sedov, a student at ITMO’s Faculty of Physics and Engineering.“
Strong coupling wanted
However, it is impossible to use such models for creating prototypes unless scientists will find ways to tackle multiple challenges. One of these fundamental issues is the relatively weak interaction of light and atoms that happens to interfere with the effective control of the atoms’ state using light particles.
Trying to solve this problem, a research team from ITMO University has created a theoretical model of a system with a strong-coupling regime.
“In our work, we presented an optomechanical system with the possibility of intensive interaction,” says Denis Sedov, one of the researchers in the project. “It is a ring waveguide in which photons are able to propagate only clockwise. The atoms are located above the waveguide in optical traps. There, they not only interact with each other with the help of photons but also oscillate near their equilibrium positions.”
Although similar systems have been studied in the past, such a ring unidirectional geometry of a waveguide and a full-fledged quantum accounting of atomic vibrations were considered for the first time. The study made it possible to obtain new and unexpected results.
It turned out that when two atoms are located above a completely chiral or, basically, a unidirectional waveguide, the considered model is an equivalent of the well-known and actively studied quantum Rabi model. The latter describes the interaction of a two-level system placed in an optical resonator (an arrangement of mirrors) and the electromagnetic field of the resonator.
This model has a Z2 symmetry, and the states describing this system can be divided into two categories: while some are characterized by an even number of excitations, others – by an odd number. Interestingly enough, this symmetry is mathematically similar to a 180-degree rotation of a figure, and two consecutive rotations are equivalent to no rotation. Even if the waveguide has no chirality, many of the obtained properties of the system are retained but non-chirality leads to new unexplored models that have yet to be investigated in the future.
The research has also proved the presence of Z3 symmetry in a three atom system.
“In simple cases, Z3 can be understood as symmetry over rotations of 120, 240, and 360 degrees, which make the system transform into itself. But although we have symmetry with a more complex nature and description, the principle remains unchanged,” says Denis Sedov, a student at ITMO University.
There is also a quantum phase transition in the system. It is a type of transformation that makes some properties of the system drastically change, for instance, an abrupt change in density during ice melting. The difference between a quantum phase transition and the classical one is that the first one occurs at absolute zero.
In the studied model, the optomechanical coupling – which describes the interaction between photons and the mechanical motion of atoms – reaching a critical value results in the occurrence of a quantum phase transition. It is accompanied by the phenomenon of self-organization of atoms over a waveguide. The process is that atoms communicate with one another through photons each going in their own specific directions.
“Under strong optomechanical coupling, the system is in the multicomponent Schrödinger-cat ground state, that is, in a superposition of multiple classical states of the atoms’ motion,” says Valerii Kozin, a PhD student at ITMO’s Faculty of Physics and Engineering. “Such systems can be used to create error-tolerant protocols for storing and processing quantum information.”
One of the major problems in creating devices for storing and processing quantum information (quantum computers) is that quantum states are extremely fragile and should be isolated from the environment. Therefore, scientists are now challenged to figure out how to store quantum information so that it remains resilient to errors. And here they may turn to the multicomponent Schrödinger-cat states that arise naturally in the proposed systems.
This paper was published in Physical Review Letters.
Reference: D. D. Sedov, V. K. Kozin, and I. V. Iorsh. Chiral Waveguide Optomechanics: First Order Quantum Phase Transitions with Z3 Symmetry Breaking. Physical Review Letters, 2020/10.1103/PhysRevLett.125.263606


Credit: University of Rochester photo / J. Adam Fenster
Quantum computing has the potential to revolutionize technology, medicine, and science by providing faster and more efficient processors, sensors, and communication devices.
But transferring information and correcting errors within a quantum system remains a challenge to making effective quantum computers.
In a paper in the journal Nature, researchers from Purdue University and the University of Rochester, including John Nichol, an assistant professor of physics, and Rochester PhD students Yadav P. Kandel and Haifeng Qiao, demonstrate their method of relaying information by transferring the state of electrons. The research brings scientists one step closer to creating fully functional quantum computers and is the latest example of Rochester’s initiative to better understand quantum behavior and develop novel quantum systems. The University recently received a $4 million grant from the Department of Energy to explore quantum materials.
A quantum computer operates on the principles of quantum mechanics, a unique set of rules that govern at the extremely small scale of atoms and subatomic particles. When dealing with particles at these scales, many of the rules that govern classical physics no longer apply and quantum effects emerge; a quantum computer is able to perform complex calculations, factor extremely large numbers, and simulate the behaviors of atoms and particles at levels that classical computers cannot.
Quantum computers have the potential to provide more insight into principles of physics and chemistry by simulating the behavior of matter at unusual conditions at the molecular level. These simulations could be useful in developing new energy sources and studying the conditions of planets and galaxies or comparing compounds that could lead to new drug therapies.
“You and I are quantum systems. The particles in our body obey quantum physics. But, if you try to compute what happens with all of the atoms in our body, you cannot do it on a regular computer,” Nichol says. “A quantum computer could easily do this.”
Quantum computers could also open doors for faster database searches and cryptography.
“It turns out that almost all of modern cryptography is based on the extreme difficulty for regular computers to factor large numbers,” Nichol says. “Quantum computers can easily factor large numbers and break encryption schemes, so you can imagine why lots of governments are interested in this.”
BITS VS. QUBITS
A regular computer consists of billions of transistors, called bits. Quantum computers, on the other hand, are based on quantum bits, also known as qubits, which can be made from a single electron. Unlike ordinary transistors, which can be either “0” or “1,” qubits can be both “0” and “1” at the same time. The ability for individual qubits to occupy these “superposition states,” where they are simultaneously in multiple states, underlies the great potential of quantum computers. Just like ordinary computers, however, quantum computers need a way to transfer information between qubits, and this presents a major experimental challenge.
“A quantum computer needs to have many qubits, and they’re really difficult to make and operate,” Nichol says. “The state-of-the art right now is doing something with only a few qubits, so we’re still a long ways away from realizing the full potential of quantum computers.”
All computers, including both regular and quantum computers and devices like smart phones, also have to perform error correction. A regular computer contains copies of bits so if one of the bits goes bad, “the rest are just going to take a majority vote” and fix the error. However, quantum bits cannot be copied, Nichol says, “so you have to be very clever about how you correct for errors. What we’re doing here is one step in that direction.”
Quantum error correction requires that individual qubits interact with many other qubits. This can be difficult because an individual electron is like a bar magnet with a north pole and a south pole that can point either up or down. The direction of the pole–whether the north pole is pointing up or down, for instance–is known as the electron’s magnetic moment or quantum state.
If certain kinds of particles have the same magnetic moment, they cannot be in the same place at the same time. That is, two electrons in the same quantum state cannot sit on top of each other.
“This is one of the main reasons something like a penny, which is made out of metal, doesn’t collapse on itself,” Nichol says. “The electrons are pushing themselves apart because they cannot be in the same place at the same time.”
If two electrons are in opposite states, they can sit on top of each other. A surprising consequence of this is that if the electrons are close enough, their states will swap back and forth in time.
“If you have one electron that’s up and another electron that’s down and you push them together for just the right amount of time, they will swap,” Nichol says. “They did not switch places, but their states switched.”
To force this phenomenon, Nichol and his colleagues cooled down a semiconductor chip to extremely low temperatures. Using quantum dots–nanoscale semiconductors–they trapped four electrons in a row, then moved the electrons so they came in contact and their states switched.
“There’s an easy way to switch the state between two neighboring electrons, but doing it over long distances–in our case, it’s four electrons–requires a lot of control and technical skill,” Nichol says. “Our research shows this is now a viable approach to send information over long distances.”
A FIRST STEP
Transmitting the state of an electron back and forth across an array of qubits, without moving the position of electrons, provides a striking example of the possibilities allowed by quantum physics for information science.
“This experiment demonstrates that information in quantum states can be transferred without actually transferring the individual electron spins down the chain,” says Michael Manfra, a professor of physics and astronomy at Purdue University. “It is an important step for showing how information can be transmitted quantum-mechanically–in manners quite different than our classical intuition would lead us to believe.”
Nichol likens this to the steps that led from the first computing devices to today’s computers. That said, will we all someday have quantum computers to replace our desktop computers? “If you had asked that question of IBM in the 1960s, they probably would’ve said no, there’s no way that’s going to happen,” Nichol says. “That’s my reaction now. But, who knows?”


Physically, but not socially, isolated: Insights from a small Micronesian island (Eileen Tipoe, Economics)
The small Micronesian island of Yap is both geographically and economically isolated. Historically, the Yapese did engage in trade with other islanders, but it happened infrequently and Yap did not have close cultural links with its trading partners. Under this near-isolation, around two centuries ago the Yapese developed an innovative exchange system that comes surprisingly close to how money is used today.
While the Yapese used pearl shells as everyday currency, the island lacked the materials that people could use to make coins of greater value, such as durable rock or precious metals. So, the Yapese travelled hundreds of kilometres across the sea to Palau, where they carved large limestone discs (called rai) from quarries. These discs varied in size, from a few inches to twelve feet in diameter, and the largest required many men to lift. To make the discs easier to transport, the Yapese carved a hole into the middle of each disc so a rope or wooden pole could be used to carry it.
The quarrying and transport of rai was an important economic activity. According to one estimate, in the late 19th century, over 10% of adult men were involved in these expeditions (Bryan, 2004). Village chiefs acted like today’s central bankers, controlling the number of expeditions and quantity of rai in circulation. Village chiefs were also responsible for determining the value of each rai (in terms of pearl shells), which not only depended on its size but also the difficulty involved with obtaining it. For example, rai that involved greater risks when quarrying or were cut using shell tools and transported by canoes were valued more highly than similarly-sized rai that were cut using iron tools or transported by Western ships. Rai that were very costly to obtain were even given names, such as the village chief’s name or the name of the canoe that transported it.
The value of each rai was never written down, but was instead kept in collective memory, passed down from generation to generation by tribal elders. Each rai has its own unique story that is also part of the oral history, detailing the relationships and transactions involving it. These stories help rai retain their value even once they become old or broken, making them worth more than comparably-sized newer rai. This common knowledge also makes it impossible for villagers to steal or counterfeit rai, and so large rai that are difficult to move can be publicly displayed around the island, sometimes in a symbolic location, or in “stone money banks” in the village centre.
Rai were exclusively traded within Yap, and the biggest rai were mainly used for large transactions, such as to purchase a plantation, and for conceptual exchanges (celebrating an event or recognising a favour). Like electronic money in modern economies, transactions involving rai do not require physical movement of the currency, only the communication that the currency’s ownership has been transferred. Even though Yap adopted the US dollar as its official currency in 1986, rai are still used in this way today, and continue to be passed down within families from generation to generation.
While Yap’s system of exchange has vastly different features from modern financial systems, both share a common element: trust. Without credibility, social interactions could not take place. And even the most geographically-isolated communities recognise the importance of social interactions. Each individual does not function in isolation, as a self-sustaining “Robinson Crusoe economy”, but instead relies on and benefits from interactions with others. The social distancing measures due to the COVID-19 pandemic have shown just how much we value these interactions, and the worth of a social interaction cannot be entirely measured in terms of money.
Economics is not only about money, it is also about the people who use it and the way they interact. How the economy, the society it is embedded in, and institutions that govern it function in an interrelated manner is what economics seeks to understand.
Bryan, M. F. (2004). Island money. Federal Reserve Bank of Cleveland.
Gillilliand, C.L.C. (1975). “The stone money of Yap: A numismatic survey,” Smithsonian Studies in History and Technology, Number 23. Washington, D.C.: Smithsonian Institution Press.
Goldberg, D. (2005). Famous myths of “fiat money”. Journal of Money, Credit and Banking, 957-967.
Poole, R. M. (2018). “The tiny island with human-sized money”. BBC, 3 May. Accessible at http://www.bbc.com/travel/story/20180502-the-tiny-island-with-human-sized-money
Find out more about Eileen Tipoe here.
Mansfield Isolation Conversation
- 3rd and 4th Century Social Distancing in the Desert (Jenn Strawbridge, Theology)
- Avoiding an Empty Universe with Solitary Neutrinos (Steve Biller, Physics)
- Daniel Defoe's Journal of the Plague Year (Ros Ballaster, English)
- Doing Community in Isolation: Mosques, Mecca and One Direction
- Isolation and Revelation (Alison Salvesen, Oriental Studies)
- Magnets in isolation (Stephen Blundell, Physics)
- Oscar Wilde in prison (Michèle Mendelssohn, English)
- Physically, but not socially, isolated: Insights from a small Micronesian island
- Power and politics amidst COVID-19 seclusions—perspectives from geography (Amber Murrey, Geography)
- Samuel Taylor Coleridge’s ‘Fears in Solitude’ (Ruth Scobie, English)
- Social Distancing in Ancrene Wisse (Lucinda Rumsey, English)
- Social distancing and quantum computing – are we all qubits now? (Jason Smith, Materials Science)
- Thomas Nashe: ‘Plague’s Prisoner’ (Chris Salamone, English)
- Even buildings need isolation (Sinan Acikgoz, Engineering)


In the last post, we described what qubits are and how quantum computing involves the manipulation of these qubits to perform useful calculations.
In this post, we’ll abstract away from the details of the physics of qubits and just call the two observable states |0⟩ and |1⟩, rather than |ON⟩ and |OFF⟩. This will be useful for ultimately describing quantum algorithms. But before we get there, we need to take a few more steps into the details of quantum gates.
Recap: the general description of a qubit is |Ψ⟩ = α|0⟩ + β|1⟩, where α and β are called amplitudes, and |α|² and |β|² are the probabilities of observing the system in the state |0⟩ and |1⟩, respectively.
We can also express the states of qubits as vectors, like so:
Quantum gates are transformations from quantum states to other quantum states. We can express these transformations as matrices, which when applied to state vectors yield new state vectors. Here’s a simple example of a quantum gate called the X gate:
Applied to the states |0⟩ and |1⟩, this gate yields
Applied to any general state, this gate yields:
Another gate that is used all the time is the Hadamard gate, or H gate.
Let’s see what it does to the |0⟩ and |1⟩ states:
In words, H puts ordinary states into superposition. Superposition is the key to quantum computing. Without it, all we have is a fancier way of talking about classical computing. So it should make sense that H is a very useful gate.
One more note on H: When you apply it to a state twice, you get back the state you started with. A simple proof of this comes by just multiplying the H matrix by itself:
Okay, enough with single qubits. While they’re pretty cool as far as they go, any non-trivial quantum algorithm is going to involve multiple qubits.
It turns out that everything we’ve said so far generalizes quite nicely. If we have two qubits, we describe the combined system by smushing them together with what’s called a tensor product (denoted ⊗). What this ends up looking like is the following:
The first number refers to the state of the first qubit, and the second refers to the state of the second.
Let’s smush together two arbitrary qubits:
This is pretty much exactly what we should have expected combining qubit states would look like.
The amplitude for the combined state to be |00⟩ is just the product of the amplitude for the first qubit to be |0⟩ and the second to be |0⟩. The amplitude for the combined state to be |01⟩ is just the product of the amplitude for the first qubit to be |0⟩ and second to be |1⟩. And so on.
We can write a general two qubit state as a vector with four components.
And as you might expect by now, two-qubit gates are simply 4 by 4 matrices that act on such vectors to produce new vectors. For instance, we can calculate the 4×4 matrix corresponding to the action of a Hadamard gate on both qubits:
Why the two-qubit Hadamard gate has this exact form is a little beyond the scope of this post. Suffice it to say that this is the 4×4 matrix that successfully transforms two qubits as if they had each been put through a single-qubit Hadamard gate. (You can verify this for yourself by simply applying H to each qubit individually and then smushing them together in the way we described above.)
Here’s what the two-qubit Hadamard gate does to the four basic two-qubit states.
Here’s a visual representation of this transformation using bar graphs:
We can easily extend this further to three, four, or more qubits. The state vector describing a N-qubit system must consider the amplitude for all possible combinations of 0s and 1s for each qubit. There are 2ᴺ such combinations (starting at 00…0 and ending at 11…1). So the vector describing an N-qubit system is composed of 2ᴺ complex numbers.
If you’ve followed everything so far, then we are now ready to move on to some actual quantum algorithms! In the next post, we’ll see first how qubits can be used to solve problems that classical bits cannot, and then why quantum computers have this enhanced problem-solving ability.
Next: Deutsch-Josza Algorithm


If we provide a bog-standard computer with an infinite data store, it suddenly becomes a Turing machine: capable of answering any question answerable by any digital computer. Even quantum computers are no more powerful; they are merely faster. For example, a quantum computer recently factorised 15 into 5 * 3 by using Shor’s algorithm; an ordinary computer could do this much more slowly by exhaustive search.
Your computer isn’t even as powerful as a Turing machine. Having a mere finite data store, it falls into a weaker class of machines: linear-bounded automata (or LBAs). A linear bounded automaton is just a Turing machine with a large but finite amount of tape. Since we’re so familiar with digital computers, I’ll give examples of other, more unusual, LBAs.
Essentially, to make a LBA we just need to be able to make some simple components and connect them together. I’ll mention a few of the conventional and unconventional approaches here.
Approach 1: Logic gates
This is quite an abstract one. We use Boolean logic operations such as AND, OR and NOT, and have simple components performing each task. For example, the AND gate returns a ‘1’ if both inputs are ‘1’, and ‘0’ otherwise.
A better logic gate is negative-AND, or ‘NAND’. This inverts the output, so it returns a ‘0’ if both inputs are ‘1’, and ‘1’ otherwise. By connecting up arrangements of NAND gates, we can emulate any other logic circuit. We can even avoid the need for wire crossings by a strategic arrangement of three XOR gates, each composed of four NAND gates:
The inputs are on the left; the outputs are on the right.
Approach 2: Electronic circuits
A conventional way to build computers is to make these logic gates out of electronic components. We currently use CMOS (complementary metal-oxide semiconductor), although earlier versions used conventional transistors and even vacuum tubes. A few years ago I visited Manchester and saw the first stored-program computer. The Baby was built by a team of pioneers, including our favourite, Alan Turing. If you look closely, you’ll see diodes and some larger vacuum tubes called pentodes. No prizes for analysing the Greek stem and ascertaining how many electrodes a pentode has.
Of course, there have been earlier computers, including Charles Babbage’s analytical engine, which has yet to be physically constructed. The first computer program (which calculated the Bernoulli numbers) was written by Ada Byron, wife of Lord Byron, who happened to go to the same college as Charles Babbage, namely Trinity College, Cambridge. The web of fundamentally interconnected events never ceases to amaze…
You may believe that our silicon chips represent the most advanced possible electronic circuits, but this is not the case. Transistors have been built from carbon nanotubes, relying on the fact that they can either act as conductors or semiconductors. These nanotube circuits make silicon chips look like vacuum tubes by comparison!
Approach 3: Making logic gates out of sticky mess
Professor Andrew Adamatzky has created logic gates in amazing, unconventional ways. Reaction-diffusion systems (invented by Alan Turing) occur in certain chemical soups, such as the Belousov-Zhabotinsky reaction. This oscillates in colour violently, and causes spirals to emerge. You can investigate these sorts of patterns in the program Ready.
Adamatsky has made similar logic gates using the Plasmodium slime mould. This is a gooey cluster of cells in a much longer life cycle, involving sporangia (like fungi), spores, amoeboids, gametes (some of which have flagellae, like sperm) and zygotes.
Approach 4: Reversible logic
A key feature of the NAND gate is that we can’t reverse it: if a ‘1’ is outputted, the inputs could have either been both ‘0’, or one ‘1’ and one ‘0’. In other words, information is continually lost. An alternative is to use only reversible logic gates. My favourite is the Fredkin gate, which has three inputs (A, B, C) and three outputs (A’, B’, C’). The output C’ is an identical copy of C. The inputs A and B are mapped directly to A’ and B’ (if C is ‘0’) or swapped to B’ and A’ (if C is ‘1’). As such, it is occasionally known as a controlled-swap gate.
The Fredkin gate has three desirable properties:
- Reversibility: given A’, B’ and C’, we can deduce A, B and C. Indeed, the gate is an involution, which means it is equal to its own inverse.
- Conservation: A + B + C = A’ + B’ + C’, which means we can represent the pulses as solid particles.
- Universality: any logic gate can be emulated with Fredkin gates.
The idea led to the concept of a billiard-ball computer, where ‘1’ is represented by a billiard ball and ‘0’ by empty space. Computation is achieved by the balls undergoing elastic collisions with each other. Exactly what the ‘balls’ are is left to your imagination; they could be regular balls, ionised hydrogen atoms, or even planets. Andy Adamatzky has managed to do this with soldier crabs chased by images of predatory birds!
Reversible logic can also be implemented electronically. It is of interest as it can be engineered to produce very little heat, which is the main enemy to the continual miniaturisation of silicon chips.
Quantum computers also use reversible logic gates, but they can exist in a complex superposition of states rather than just one. To emulate a quantum computer on a classical computer, an exponentially large amount of memory is required. For example, a 30-qubit quantum computer requires more than 1000000000 complex numbers to describe its state.
Approach 5: Trains and cellular automata
Instead of logic gates, we can have a single pulse moving around a network of components, storing and reading information. A fun implementation of this is a train on a railway track. It transpires that only three types of points are needed, known as the lazy point, sprung point and flip-flop point. Ian Stewart has a brilliant write-up of this.
I recall that someone even implemented a linear-bounded automaton on the Chalcraft-Greene train set, which was emulated by the Wireworld cellular automaton. There’s an even simpler cellular automaton, Banks-I, which is able to emulate any linear bounded automaton.
The next state of each cell in Banks-I is determined only by that of itself and its four immediate neighbours. Moreover, there are only two states, and the rules are isotropic (reflecting and/or rotating a computer will not affect its operation).
Again, this could have practical applications. There’s an emerging field of ‘quantum-dot cellular automata’, where each cell is a mere 60nm wide. This is superior to existing semiconductor-based circuitry.
What isn’t a linear-bounded automaton?
A Turing machine with infinite memory is more powerful than a linear-bounded automaton. Conversely, if the logic gates ‘burn out’ after a single use, like those in Matt Parker’s domino computer, it is less powerful than a LBA. If something can be ‘solved’ in an amount of time linear in the size of the machine, like the Antikythera mechanism, then it is also weaker than a linear-bounded automaton.


Research on quantum networking is well under way.
In April 2012, Gerhard Rempe and other researchers at the Max Planck Institute of Quantum Optics in Germany announced their first working quantum network to the world.
Then, just this year, Wolfgang Tittel and his researchers at the University of Calgary transported a light particle’s properties through six kilometres of cable.
Scientists know how to transmit quantum data through fibre optics or similar free space physical transmission. In a quantum transmission, photons alter across a long link of highly sensitive atoms. In a fibre optic cable, that transmission is sent through tiny glass fibres via light emissions. Free space connections also carry a quantum signal via light emissions, but without glass fibre. Therefore, a clear line of sight must exist between the starting point and destination of the signal. That means we can transmit data even quicker than through fibre optics, but it’s trickier to control.
Of course, there is already lots of fibre optic cable in developed parts of the world, but the usual usage, as of 2016, is for the binary digital signals that we all know and love.
Quantum data is kind of intriguing and mind-blowing because, while binary bits can only contain a 1 or a 0, quantum bits (qubits) can be both or neither. They’re elusive, and their physical properties add a bizarre new dimension to computer science.
Binary data has been around since before ENIAC was introduced in 1946. And look where we’ve taken it in over seventy years! We used to require a roomful of machinery for simple arithmetic. Now we can transfer minutes of audio and 1080p video from one end of the world within seconds from our 100 gram pocket sized devices. That’s all binary data, and we still haven’t completely explored its potential!
As of now, we can only transmit a very simple piece of data, such as a light particles information, in the quantum way. Broadcast transmission, which a lot of the internet must do, is impossible for us to do with quantum signals so far. There must be a single point A and point B unless we find some way around that.
Just looking at a qubit changes its data. A mere look is a photonic alteration in and of itself! Imagine designing firewalls and network monitoring tools for that… And the existing block and stream ciphers we use for the encryption of binary data absolutely won’t work with qubits either.
So, you’d think that the eventual implementation of quantum networks will pose challenges to information security like we’ve never seen before. It will, indeed. But because merely looking at a photon or changing its direction in any way will change its data, man-in-the-middle (MITM) attacks will be yesterday’s news. Well, at least as we know them. (Never say never!)
Here’s how MITM attacks usually work:
A client machine initiates a transmission to a server on the internet. The attacker gets between the legitimate client-to-server transmission. A cryptographic key request is made from the client machine with the server as its intended recipient. The man-in-the-middle attacker just sends that through and over to the server. The server sends a key to the client, but unbeknownst to the client and the server, the attacker makes a copy of that key before the key reaches the client. Because a cryptographic key has been received by the client, the client and the server think they have a secure, encrypted connection such as over HTTPS while the person using the client machine is doing their online banking.
Because the attacker can now decrypt with that key, they have access to all of that supposedly secure and highly sensitive financial data that’s being transmitted in that session.
Those usual sorts of MITM attacks won’t work with quantum networks and qubits, for merely looking at the photon alters it. And the client, server, or both will be aware of that alteration. It’s the photon itself that contains the qubit.
Large swaths of Canada and the United States could be covered in fibre optic cable already if it weren’t for the avarice and corporate collusion of certain tier one ISPs. Much of the developed world, including parts of Canada and the US do have fibre optic networks already, but we’d have so much more if it weren’t for corporate greed. Even binary data sent over fibre optics is more secure and much faster than binary data sent over coaxial.
I can only imagine the corporate resistance to free space cable later on! It’s these sorts of factors that will impede the implementation of quantum networking technology for the service of ordinary people.
Quantum cryptography offers tremendous potential to information security, as well. Artur Ekert of the National University of Singapore demonstrated some of this potential at the American Association for the Advancement of Science back in 2012. Just as MITM attacks seem impossible in the transmission of qubits, so does attempting to interfere with the transmission of a quantum cryptographic key. Just looking at the photon changes it, so the targeted parties will become aware of the attack.
This is the curious world of the transmission of information through the tiniest possible things; quantum things, photons. Securing that information, quantum information security, is a whole new world of advantages, complexity, and challenges.
About the Author: Kim Crawley spent years working in general tier two consumer tech support, most of which as a representative of Windstream, a secondary American ISP. Malware related tickets intrigued her, and her knowledge grew from fixing malware problems on thousands of client PCs. Her curiosity led her to research malware as a hobby, which grew into an interest in all things information security related.
By 2011, she was already ghostwriting study material for the InfoSec Institute’s CISSP and CEH certification exam preparation programs. Ever since, she’s contributed articles on a variety of information security topics to CIO, CSO, Computerworld, SC Magazine, and 2600 Magazine.
Her first solo developed PC game, Hackers Versus Banksters, had a successful Kickstarter and was featured at the Toronto Comic Arts Festival in May 2016. This October, she gave her first talk at an infosec convention, a penetration testing presentation at BSides Toronto.
She considers her sociological and psychological perspective on infosec to be her trademark. Given the rapid growth of social engineering vulnerabilities, always considering the human element is vital.
Editor’s Note: The opinions expressed in this guest author article are solely those of the contributor, and do not necessarily reflect those of Tripwire, Inc.


At any second of our lives, we can expect some 420 billion solar neutrino particles to pass through every square inch of Earth’s surface, including our bodies, our pets, our homes, our cars – all of our proud possessions. Don’t be alarmed. This has been happening since the birth of our Sun, for over four and a half billion years, even before we made our appearance on Earth, there were solar neutrinos.
Neutrinos were not seen, just theorized, first by Wolfgang Pauli in 1930 in the process of beta decay, understood later to be related to the process of fusion, the source of a star’s energy. Now physicists have used the underground Borexino Detector in Italy to find the first solar neutrinos formed in the fusion process responsible for most of our Sun’s energy.
Deep in the core of the Sun, roiling heat and pressure cause pairs of protons to fuse forming heavier atoms, releasing particles called neutrinos in the process. Neutrinos almost never interact with regular particles and fly straight through the empty space between atoms in our bodies and all other normal matter. Occasionally, given the right environment, a neutrino will collide with an atom and knock an electron loose.
The Borexino instrument, in a laboratory 1.4 kilometres deep beneath the Italian Apennine Mountains, is a metal sphere shielded by a large tank, containing 1,000 tonnes of water to prevent neutrons and gamma rays from entering. The 2,000 photomultiplier tubes lining the walls of the sphere are intended to pick up the measurements of proton-proton (p-p) neutrinos, which form in 99% of the Sun’s fusion process.
What makes fusion in the Sun, a process that showers us with phantom particles, like ghosts that pass through our bodies? Heat and light from the Sun are a result of the fusion process (more on fusion) happening in 0.2 solar radii of a million mile diameter sphere we call the Sun. Inside this zone, pressure is millions of times greater than the surface of the Earth with a temperature of more than 15 million Kelvin. On the Fahrenheit scale, that’s 26,999,540.3 degrees!
The temperature and pressure is enough to keep some 8.4 x 1056 (almost an octodecillion) hydrogen atoms bumping against one another and fusing for around another 4–5 billion years before they are spent. So your life insurance for your line of descendents will be spent for quite a while! That’s because our star is not Betelgeuse, which could explode at any time, though we wouldn’t know it for 640 years – which means it could have happened 639 years, 364 days ago and we won’t know until tomorrow.
But more about neutrinos…Why do we care? We know about things that physically affect us. The rest is in the realm of theory or superstition, depending on your society’s level of sophistication and your thirst for knowledge. If your level of curiosity is curbed by a personal agenda or torpor, then you and your similar-minded network of progress will be stymied, and if other networks are not similarly stymied – namely, the rest of the world—then you and your network are left hopelessly behind, mired in age-old beliefs.
Science can often reveal truths that dispel myths, superstitions, and fears that humankind has dealt with for hundreds of thousands of years, even more recent mysteries that put science in the realm of science fiction. But no one could see, hear or feel neutrinos, so no mythology needed to be built around them, only sensing the visible light of the Sun whose fusion process produced neutrinos.
We rebuke ideas like ghosts, for example, because we cannot see them. We can draw parallels with neutrinos, that don’t interact with particle orthodoxy. Now we can see their direct evidence because of science and hard-headed scientists with curiosity. I don’t know about ghosts and photomultiplier tubes – it’s just an example. When there is near-unanimity among scientists in believing anthropogenic climate change, then the outliers must have an agenda, that intellectual torpor, or know something we don’t know. Would you bet on the latter?
Progress depends on curiosity, unbiased thinking, an open mind – the willingness to seek knowledge. Neutrinos and non-visible wavelengths are examples of things we cannot see without the enhanced tools science has brought for that clear vision. What other mysteries can we solve by having an open mind?
Have you seen a wormhole in space that enables us to go from galaxy to galaxy (the movie Interstellar [should be Intergalactic] deploys them)? Our theories don’t allow us to go faster than the speed of light so science fiction uses wormholes and warp speed (warping space). But our theories are based on the science of a Type 0 Civilization (Karashev Scale). Does quantum entanglement (Wacky Physics) change this? That’s the point. Do we only believe or even consider what can be sensed and/or conditioned by the body of knowledge of a Type 0 civilization – or worse yet, based on an agenda?
Jim Hoover is a recently retired systems engineer. He has advanced degrees in Economics and English. Prior to his aerospace career, he taught in high schools, and he has also served as an adjunct college instructor. He recently published a science fiction novel called Extraordinary Visitors and writes political and science columns on several websites.